

import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
       from comet_ml import OfflineExperiment

if is_tensorboard_available():
       import struct

       import tensorboard.compat.proto.event_pba as event_pba

UpperCAmelCase				   =  logging.getLogger(__name__)


@require_tensorboard
class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					='test_project_with_config'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowercase						,      __lowercase      )
                 accelerator.end_training()
                 for child in Path(f'''{dirpath}/{project_name}'''      ).glob('*/**'      ):
                      __lowercase					=list(filter(lambda __lowercase      : x.is_file()						,      child.iterdir()      )      )[0]
                 self.assertNotEqual(str(__lowercase      )						,      ''      )







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            __lowercase					='test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 accelerator.init_trackers(__lowercase      )
                 __lowercase					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowercase						,      step=0      )
                 accelerator.end_training()
                 # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
                 # Names are randomly generated each time
                 __lowercase					=list(filter(lambda __lowercase      : x.is_file()						,      Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
                 self.assertNotEqual(str(__lowercase      )						,      ''      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					='test_project_with_log'
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )
                 accelerator.init_trackers(__lowercase      )
                 __lowercase					={'tensor': torch.tensor(1      )}
                 accelerator.log(__lowercase						,      step=0      )
                 accelerator.end_training()
                 # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
                 # Names are randomly generated each time
                 __lowercase					=list(filter(lambda __lowercase      : x.is_file()						,      Path(f'''{dirpath}/{project_name}'''      ).iterdir()      )      )[0]
                 # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
                 with open(__lowercase						,      'rb'      ) as f:
                      __lowercase					=f.read()
                 __lowercase					=False
                 while data:
                      __lowercase					=struct.unpack('Q'						,      data[:8]      )

                      __lowercase					=data[12 : 12 + int(header[0]      )]  # 8+4
                      __lowercase					=data[12 + int(header[0]      ) + 4 :]
                      __lowercase					=event_pba.Event()

                      event.ParseFromString(__lowercase      )
                      if event.HasField('summary'      ):
                           for value in event.summary.value:
                                if value.simple_value == 1.0 and value.tag == "tensor":
                                     __lowercase					=True
                 self.assertTrue(__lowercase						,      'Converted tensor was not found in the log file!'      )







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            with self.assertRaisesRegex(__lowercase						,      'Logging with `tensorboard` requires a `logging_dir`'      ):
                 __lowercase					=Accelerator(log_with='tensorboard'      )
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=ProjectConfiguration(total_limit=30      )
            with tempfile.TemporaryDirectory() as dirpath:
                 __lowercase					=Accelerator(log_with='tensorboard'						,      project_dir=__lowercase						,      project_config=__lowercase      )






@require_wandb
@mock.patch.dict(os.environ   ,			{"WANDB_MODE": "offline"}   )
class   lowerCAmelCase     (      A   ,			A   ):







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            super().setUp()
            # wandb let's us override where logs are stored to via the WANDB_DIR env var
            self.add_mocks(mock.patch.dict(os.environ						,      {'WANDB_DIR': self.tmpdir}      )      )







       @staticmethod
       def 			snake_case   (							__lowercase			: str						,      __lowercase			: str						,      __lowercase			: bool = True      ):







            """simple docstring"""







            __lowercase					=rf'''{section} ([\S\s]*?)\n\n'''
            if record:
                 __lowercase					=rf'''Record: {pattern}'''
            __lowercase					=re.findall(__lowercase						,      __lowercase      )[0]
            # A config
            if section == "config" or section == "history":
                 __lowercase					=re.findall(r'"([a-zA-Z0-9_.,]+)'						,      __lowercase      )
                 return {key: val for key, val in zip(cleaned_record[0::2]						,      cleaned_record[1::2]      )}
            # Everything else
            else:
                 return dict(re.findall(r'(\w+): "([^\s]+)"'						,      __lowercase      )      )







       @skip
       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					='test_project_with_config'
            __lowercase					=Accelerator(log_with='wandb'      )
            __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
            __lowercase					={'wandb': {'tags': ['my_tag']}}
            accelerator.init_trackers(__lowercase						,      __lowercase						,      __lowercase      )
            __lowercase					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
            accelerator.log(__lowercase						,      step=0      )
            accelerator.end_training()
            # The latest offline log is stored at wandb/latest-run/*.wandb
            for child in Path(f'''{self.tmpdir}/wandb/latest-run'''      ).glob('*'      ):
                 if child.is_file() and child.suffix == ".wandb":
                      __lowercase					=subprocess.check_output(
                          ['wandb', 'sync', '--view', '--verbose', str(__lowercase      )]						,      env=os.environ.copy()      ).decode('utf8'						,      'ignore'      )
                      break

        # Check HPS through careful parsing and cleaning
            __lowercase					=self.parse_log(__lowercase						,      'config'      )
            self.assertEqual(logged_items['num_iterations']						,      '12'      )
            self.assertEqual(logged_items['learning_rate']						,      '0.01'      )
            self.assertEqual(logged_items['some_boolean']						,      'false'      )
            self.assertEqual(logged_items['some_string']						,      'some_value'      )
            self.assertEqual(logged_items['some_string']						,      'some_value'      )

            # Run tags
            __lowercase					=self.parse_log(__lowercase						,      'run'						,      __lowercase      )
            self.assertEqual(logged_items['tags']						,      'my_tag'      )

            # Actual logging
            __lowercase					=self.parse_log(__lowercase						,      'history'      )
            self.assertEqual(logged_items['total_loss']						,      '0.1'      )
            self.assertEqual(logged_items['iteration']						,      '1'      )
            self.assertEqual(logged_items['my_text']						,      'some_value'      )
            self.assertEqual(logged_items['_step']						,      '0'      )






def 				__UpperCamelCase							(	self						:  Union[str, Any],					lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __lowercase					=run_name
     __lowercase					=OfflineExperiment(project_name=lowercase__,					offline_directory=lowercase__ )
     logger.info(F'''Initialized offline CometML project {self.run_name}''' )
     logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!' )


@require_comet_ml
@mock.patch.object(A   ,			"__init__"   ,			A   )
class   lowerCAmelCase     (      unittest.TestCase   ):







       @staticmethod
       def 			snake_case   (							__lowercase			: List[Any]						,      __lowercase			: str						,      __lowercase			: bool = False      ):







            """simple docstring"""







            for log in log_list:
                 __lowercase					=json.loads(__lowercase      )['payload']
                 if is_param and "param" in j.keys():
                      if j["param"]["paramName"] == key:
                           return j["param"]["paramValue"]
                 if "log_other" in j.keys():
                      if j["log_other"]["key"] == key:
                           return j["log_other"]["val"]
                 if "metric" in j.keys():
                      if j["metric"]["metricName"] == key:
                           return j["metric"]["metricValue"]







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=CometMLTracker('test_project_with_config'						,      __lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers(__lowercase						,      __lowercase      )
                 accelerator.end_training()
                 __lowercase					=os.listdir(__lowercase      )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 __lowercase					=os.path.join(__lowercase						,      __lowercase      )
                 __lowercase					=zipfile.ZipFile(__lowercase						,      'r'      )
                 __lowercase					=archive.open('messages.json'      ).read().decode('utf-8'      )
            __lowercase					=log.split('\n'      )[:-1]
            self.assertEqual(self.get_value_from_key(__lowercase						,      'num_iterations'						,      __lowercase      )						,      12      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'learning_rate'						,      __lowercase      )						,      0.0_1      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'some_boolean'						,      __lowercase      )						,      __lowercase      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'some_string'						,      __lowercase      )						,      'some_value'      )







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=CometMLTracker('test_project_with_config'						,      __lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 accelerator.init_trackers(__lowercase      )
                 __lowercase					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowercase						,      step=0      )
                 accelerator.end_training()
                 __lowercase					=os.listdir(__lowercase      )[0]  # Comet is nice, it's just a zip file here
                 # We parse the raw logs
                 __lowercase					=os.path.join(__lowercase						,      __lowercase      )
                 __lowercase					=zipfile.ZipFile(__lowercase						,      'r'      )
                 __lowercase					=archive.open('messages.json'      ).read().decode('utf-8'      )
            __lowercase					=log.split('\n'      )[:-1]
            self.assertEqual(self.get_value_from_key(__lowercase						,      'curr_step'						,      __lowercase      )						,      0      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'total_loss'      )						,      0.1      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'iteration'      )						,      1      )
            self.assertEqual(self.get_value_from_key(__lowercase						,      'my_text'      )						,      'some_value'      )






class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= [
           "total_loss",
           "iteration",
           "my_text",
           "learning_rate",
           "num_iterations",
           "some_boolean",
           "some_string",
       ]

       lowerCAmelCase_		= "my_custom_tracker"
       lowerCAmelCase_		= False







       def __init__(							self			: List[str]						,      __lowercase			: str      ):







            """simple docstring"""







            __lowercase					=open(f'''{dir}/log.csv'''						,      'w+'      )
            __lowercase					=csv.DictWriter(self.f						,      fieldnames=self._col_names      )
            self.writer.writeheader()







       @property
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return self.writer







       def 			snake_case   (							self			: Tuple						,      __lowercase			: dict      ):







            """simple docstring"""







            logger.info('Call init'      )
            self.writer.writerow(__lowercase      )







       def 			snake_case   (							self			: List[Any]						,      __lowercase			: dict						,      __lowercase			: Optional[int]      ):







            """simple docstring"""







            logger.info('Call log'      )
            self.writer.writerow(__lowercase      )







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            self.f.close()






class   lowerCAmelCase     (      unittest.TestCase   ):







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=MyCustomTracker(__lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 __lowercase					={'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
                 accelerator.init_trackers('Some name'						,      __lowercase      )
                 accelerator.end_training()
                 with open(f'''{d}/log.csv'''						,      'r'      ) as f:
                      __lowercase					=csv.DictReader(__lowercase      )
                      __lowercase					=next(__lowercase      )
                      __lowercase					={
                          'total_loss': '',
                          'iteration': '',
                          'my_text': '',
                          'learning_rate': '0.01',
                          'num_iterations': '12',
                          'some_boolean': 'False',
                          'some_string': 'some_value',
                      }
                      self.assertDictEqual(__lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            with tempfile.TemporaryDirectory() as d:
                 __lowercase					=MyCustomTracker(__lowercase      )
                 __lowercase					=Accelerator(log_with=__lowercase      )
                 accelerator.init_trackers('Some name'      )
                 __lowercase					={'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
                 accelerator.log(__lowercase						,      step=0      )
                 accelerator.end_training()
                 with open(f'''{d}/log.csv'''						,      'r'      ) as f:
                      __lowercase					=csv.DictReader(__lowercase      )
                      __lowercase					=next(__lowercase      )
                      __lowercase					={
                          'total_loss': '0.1',
                          'iteration': '1',
                          'my_text': 'some_value',
                          'learning_rate': '',
                          'num_iterations': '',
                          'some_boolean': '',
                          'some_string': '',
                      }
                      self.assertDictEqual(__lowercase						,      __lowercase      )





'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
       from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor


class   lowerCAmelCase     :







       def __init__(							self			: List[str]						,      __lowercase			: Optional[int]						,      __lowercase			: Optional[int]=13						,      __lowercase			: Dict=32						,      __lowercase			: int=2						,      __lowercase			: Tuple=3						,      __lowercase			: Optional[int]=16						,      __lowercase			: Dict=[1, 2, 1]						,      __lowercase			: Dict=[2, 2, 4]						,      __lowercase			: int=2						,      __lowercase			: Optional[Any]=2.0						,      __lowercase			: int=True						,      __lowercase			: List[Any]=0.0						,      __lowercase			: int=0.0						,      __lowercase			: Optional[Any]=0.1						,      __lowercase			: List[str]="gelu"						,      __lowercase			: Any=False						,      __lowercase			: Dict=True						,      __lowercase			: Optional[Any]=0.0_2						,      __lowercase			: int=1E-5						,      __lowercase			: Dict=True						,      __lowercase			: Optional[int]=None						,      __lowercase			: List[Any]=True						,      __lowercase			: List[Any]=10						,      __lowercase			: Any=8						,      __lowercase			: Optional[int]=["stage1", "stage2"]						,      __lowercase			: str=[1, 2]						,      ):







            """simple docstring"""







            __lowercase					=parent
            __lowercase					=batch_size
            __lowercase					=image_size
            __lowercase					=patch_size
            __lowercase					=num_channels
            __lowercase					=embed_dim
            __lowercase					=depths
            __lowercase					=num_heads
            __lowercase					=window_size
            __lowercase					=mlp_ratio
            __lowercase					=qkv_bias
            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=drop_path_rate
            __lowercase					=hidden_act
            __lowercase					=use_absolute_embeddings
            __lowercase					=patch_norm
            __lowercase					=layer_norm_eps
            __lowercase					=initializer_range
            __lowercase					=is_training
            __lowercase					=scope
            __lowercase					=use_labels
            __lowercase					=type_sequence_label_size
            __lowercase					=encoder_stride
            __lowercase					=out_features
            __lowercase					=out_indices







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

            __lowercase					=None
            if self.use_labels:
                 __lowercase					=ids_tensor([self.batch_size]						,      self.type_sequence_label_size      )

            __lowercase					=self.get_config()

            return config, pixel_values, labels







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            return SwinConfig(
                image_size=self.image_size						,      patch_size=self.patch_size						,      num_channels=self.num_channels						,      embed_dim=self.embed_dim						,      depths=self.depths						,      num_heads=self.num_heads						,      window_size=self.window_size						,      mlp_ratio=self.mlp_ratio						,      qkv_bias=self.qkv_bias						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      drop_path_rate=self.drop_path_rate						,      hidden_act=self.hidden_act						,      use_absolute_embeddings=self.use_absolute_embeddings						,      path_norm=self.patch_norm						,      layer_norm_eps=self.layer_norm_eps						,      initializer_range=self.initializer_range						,      encoder_stride=self.encoder_stride						,      out_features=self.out_features						,      out_indices=self.out_indices						,      )







       def 			snake_case   (							self			: Optional[Any]						,      __lowercase			: str						,      __lowercase			: Tuple						,      __lowercase			: int      ):







            """simple docstring"""







            __lowercase					=SwinModel(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            __lowercase					=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
            __lowercase					=int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

            self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, expected_seq_len, expected_dim)      )







       def 			snake_case   (							self			: str						,      __lowercase			: str						,      __lowercase			: Dict						,      __lowercase			: List[Any]      ):







            """simple docstring"""







            __lowercase					=SwinBackbone(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            # verify hidden states
            self.parent.assertEqual(len(result.feature_maps      )						,      len(config.out_features      )      )
            self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,      [self.batch_size, model.channels[0], 16, 16]      )

            # verify channels
            self.parent.assertEqual(len(model.channels      )						,      len(config.out_features      )      )

            # verify backbone works with out_features=None
            __lowercase					=None
            __lowercase					=SwinBackbone(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )

            # verify feature maps
            self.parent.assertEqual(len(result.feature_maps      )						,      1      )
            self.parent.assertListEqual(list(result.feature_maps[0].shape      )						,      [self.batch_size, model.channels[-1], 4, 4]      )

            # verify channels
            self.parent.assertEqual(len(model.channels      )						,      1      )







       def 			snake_case   (							self			: int						,      __lowercase			: int						,      __lowercase			: List[str]						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=SwinForMaskedImageModeling(config=__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(
                result.logits.shape						,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

            # test greyscale images
            __lowercase					=1
            __lowercase					=SwinForMaskedImageModeling(__lowercase      )
            model.to(__lowercase      )
            model.eval()

            __lowercase					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, 1, self.image_size, self.image_size)      )







       def 			snake_case   (							self			: Any						,      __lowercase			: Optional[Any]						,      __lowercase			: List[str]						,      __lowercase			: Dict      ):







            """simple docstring"""







            __lowercase					=self.type_sequence_label_size
            __lowercase					=SwinForImageClassification(__lowercase      )
            model.to(__lowercase      )
            model.eval()
            __lowercase					=model(__lowercase						,      labels=__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)      )

            # test greyscale images
            __lowercase					=1
            __lowercase					=SwinForImageClassification(__lowercase      )
            model.to(__lowercase      )
            model.eval()

            __lowercase					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
            __lowercase					=model(__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.type_sequence_label_size)      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase					=self.prepare_config_and_inputs()
            (
                (
                __lowercase
            )		,     (
                __lowercase
            )		,     (
                __lowercase
            )		,     
            )					=config_and_inputs
            __lowercase					={'pixel_values': pixel_values}
            return config, inputs_dict






@require_torch
class   lowerCAmelCase     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (
           (
               SwinModel,
               SwinBackbone,
               SwinForImageClassification,
               SwinForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_		= (
           {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase_		= True

       lowerCAmelCase_		= False
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=SwinModelTester(self      )
            __lowercase					=ConfigTester(self						,      config_class=__lowercase						,      embed_dim=37      )







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            return







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowercase      )







       @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'      )
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            super().test_training_gradient_checkpointing()







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_backbone(*__lowercase      )







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase      )







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*__lowercase      )







       @unittest.skip(reason='Swin does not use inputs_embeds'      )
       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            pass







       @unittest.skip(reason='Swin Transformer does not use feedforward chunking'      )
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __lowercase					=model_class(__lowercase      )
                 self.assertIsInstance(model.get_input_embeddings()						,      (nn.Module)      )
                 __lowercase					=model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__lowercase						,      nn.Linear      )      )







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __lowercase					=model_class(__lowercase      )
                 __lowercase					=inspect.signature(model.forward      )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 __lowercase					=[*signature.parameters.keys()]

                 __lowercase					=['pixel_values']
                 self.assertListEqual(arg_names[:1]						,      __lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            __lowercase					=True

            for model_class in self.all_model_classes:
                 __lowercase					=True
                 __lowercase					=False
                 __lowercase					=True
                 __lowercase					=model_class(__lowercase      )
                 model.to(__lowercase      )
                 model.eval()
                 with torch.no_grad():
                      __lowercase					=model(**self._prepare_for_class(__lowercase						,      __lowercase      )      )
                 __lowercase					=outputs.attentions
                 __lowercase					=len(self.model_tester.depths      )
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 __lowercase					=True
                 __lowercase					=config.window_size**2
                 __lowercase					=model_class(__lowercase      )
                 model.to(__lowercase      )
                 model.eval()
                 with torch.no_grad():
                      __lowercase					=model(**self._prepare_for_class(__lowercase						,      __lowercase      )      )
                 __lowercase					=outputs.attentions
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]      )						,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,      )
                 __lowercase					=len(__lowercase      )

                 # Check attention is always last and order is fine
                 __lowercase					=True
                 __lowercase					=True
                 __lowercase					=model_class(__lowercase      )
                 model.to(__lowercase      )
                 model.eval()
                 with torch.no_grad():
                      __lowercase					=model(**self._prepare_for_class(__lowercase						,      __lowercase      )      )

                 # also another +1 for reshaped_hidden_states
                 __lowercase					=1 if model_class.__name__ == 'SwinBackbone' else 2
                 self.assertEqual(out_len + added_hidden_states						,      len(__lowercase      )      )

                 __lowercase					=outputs.attentions

                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]      )						,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,      )







       def 			snake_case   (							self			: List[str]						,      __lowercase			: Dict						,      __lowercase			: List[str]						,      __lowercase			: List[str]						,      __lowercase			: Any      ):







            """simple docstring"""







            __lowercase					=model_class(__lowercase      )
            model.to(__lowercase      )
            model.eval()

            with torch.no_grad():
                 __lowercase					=model(**self._prepare_for_class(__lowercase						,      __lowercase      )      )

            __lowercase					=outputs.hidden_states

            __lowercase					=getattr(
                self.model_tester						,      'expected_num_hidden_layers'						,      len(self.model_tester.depths      ) + 1      )
            self.assertEqual(len(__lowercase      )						,      __lowercase      )

            # Swin has a different seq_length
            __lowercase					=(
                config.patch_size
                if isinstance(config.patch_size						,      collections.abc.Iterable      )
                else (config.patch_size, config.patch_size)
            )

            __lowercase					=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )						,      [num_patches, self.model_tester.embed_dim]						,      )

            if not model_class.__name__ == "SwinBackbone":
                 __lowercase					=outputs.reshaped_hidden_states
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

                 __lowercase		,     __lowercase		,     __lowercase		,     __lowercase					=reshaped_hidden_states[0].shape
                 __lowercase					=(
                     reshaped_hidden_states[0].view(__lowercase						,      __lowercase						,      height * width      ).permute(0						,      2						,      1      )
                 )
                 self.assertListEqual(
                     list(reshaped_hidden_states.shape[-2:]      )						,      [num_patches, self.model_tester.embed_dim]						,      )







       def 			snake_case   (							self			: Union[str, Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            __lowercase					=(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size						,      collections.abc.Iterable      )
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      __lowercase      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 __lowercase					=True

                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            __lowercase					=3

            __lowercase					=(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size						,      collections.abc.Iterable      )
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            __lowercase					=(
                config.patch_size
                if isinstance(config.patch_size						,      collections.abc.Iterable      )
                else (config.patch_size, config.patch_size)
            )

            __lowercase					=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            __lowercase					=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      (padded_height, padded_width)      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 __lowercase					=True
                 self.check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase						,      (padded_height, padded_width)      )







       @slow
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=SwinModel.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

            __lowercase					=_config_zero_init(__lowercase      )
            for model_class in self.all_model_classes:
                 __lowercase					=model_class(config=__lowercase      )
                 for name, param in model.named_parameters():
                      if "embeddings" not in name and param.requires_grad:
                           self.assertIn(
                               ((param.data.mean() * 1E9).round() / 1E9).item()						,      [0.0, 1.0]						,      msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''						,      )






@require_vision
@require_torch
class   lowerCAmelCase     (      unittest.TestCase   ):







       @cached_property
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return (
                AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      )
                if is_vision_available()
                else None
            )







       @slow
       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'      ).to(__lowercase      )
            __lowercase					=self.default_image_processor

            __lowercase					=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
            __lowercase					=image_processor(images=__lowercase						,      return_tensors='pt'      ).to(__lowercase      )

            # forward pass
            with torch.no_grad():
                 __lowercase					=model(**__lowercase      )

            # verify the logits
            __lowercase					=torch.Size((1, 1000)      )
            self.assertEqual(outputs.logits.shape						,      __lowercase      )
            __lowercase					=torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]      ).to(__lowercase      )
            self.assertTrue(torch.allclose(outputs.logits[0, :3]						,      __lowercase						,      atol=1E-4      )      )






@require_torch
class   lowerCAmelCase     (      unittest.TestCase   ,			A   ):
       lowerCAmelCase_		= (SwinBackbone,) if is_torch_available() else ()
       lowerCAmelCase_		= SwinConfig







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=SwinModelTester(self      )


