

'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import numpy as np
       import tensorflow as tf

       from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
       from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class   lowerCAmelCase     (      A   ):







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.config_class(**self.inputs_dict      )
            self.parent.assertTrue(hasattr(__lowercase						,      'hidden_sizes'      )      )
            self.parent.assertTrue(hasattr(__lowercase						,      'neck_hidden_sizes'      )      )
            self.parent.assertTrue(hasattr(__lowercase						,      'num_attention_heads'      )      )






class   lowerCAmelCase     :







       def __init__(							self			: Union[str, Any]						,      __lowercase			: int						,      __lowercase			: Dict=13						,      __lowercase			: Optional[Any]=32						,      __lowercase			: Tuple=2						,      __lowercase			: Tuple=3						,      __lowercase			: List[str]=640						,      __lowercase			: List[str]=4						,      __lowercase			: Optional[int]="silu"						,      __lowercase			: Tuple=3						,      __lowercase			: int=32						,      __lowercase			: Dict=0.1						,      __lowercase			: int=0.1						,      __lowercase			: Optional[Any]=0.1						,      __lowercase			: List[str]=0.0_2						,      __lowercase			: str=True						,      __lowercase			: Union[str, Any]=True						,      __lowercase			: str=10						,      __lowercase			: Optional[int]=None						,      ):







            """simple docstring"""







            __lowercase					=parent
            __lowercase					=batch_size
            __lowercase					=image_size
            __lowercase					=patch_size
            __lowercase					=num_channels
            __lowercase					=last_hidden_size
            __lowercase					=num_attention_heads
            __lowercase					=hidden_act
            __lowercase					=conv_kernel_size
            __lowercase					=output_stride
            __lowercase					=hidden_dropout_prob
            __lowercase					=attention_probs_dropout_prob
            __lowercase					=classifier_dropout_prob
            __lowercase					=use_labels
            __lowercase					=is_training
            __lowercase					=num_labels
            __lowercase					=initializer_range
            __lowercase					=scope







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







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

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

            __lowercase					=self.get_config()

            return config, pixel_values, labels, pixel_labels







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            return MobileViTConfig(
                image_size=self.image_size						,      patch_size=self.patch_size						,      num_channels=self.num_channels						,      num_attention_heads=self.num_attention_heads						,      hidden_act=self.hidden_act						,      conv_kernel_size=self.conv_kernel_size						,      output_stride=self.output_stride						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      classifier_dropout_prob=self.classifier_dropout_prob						,      initializer_range=self.initializer_range						,      )







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







            """simple docstring"""







            __lowercase					=TFMobileViTModel(config=__lowercase      )
            __lowercase					=model(__lowercase						,      training=__lowercase      )
            __lowercase					=__lowercase					=self.image_size // self.output_stride
            self.parent.assertEqual(
                result.last_hidden_state.shape						,      (self.batch_size, self.last_hidden_size, expected_height, expected_width)      )







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







            """simple docstring"""







            __lowercase					=self.num_labels
            __lowercase					=TFMobileViTForImageClassification(__lowercase      )
            __lowercase					=model(__lowercase						,      labels=__lowercase						,      training=__lowercase      )
            self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)      )







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







            """simple docstring"""







            __lowercase					=self.num_labels
            __lowercase					=TFMobileViTForSemanticSegmentation(__lowercase      )
            __lowercase					=__lowercase					=self.image_size // self.output_stride

            __lowercase					=model(__lowercase						,      training=__lowercase      )
            self.parent.assertEqual(
                result.logits.shape						,      (self.batch_size, self.num_labels, expected_height, expected_width)      )

            __lowercase					=model(__lowercase						,      labels=__lowercase						,      training=__lowercase      )
            self.parent.assertEqual(
                result.logits.shape						,      (self.batch_size, self.num_labels, expected_height, expected_width)      )







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







            """simple docstring"""







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






@require_tf
class   lowerCAmelCase     (      A   ,			A   ,			unittest.TestCase   ):
       lowerCAmelCase_		= (
           (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
           if is_tf_available()
           else ()
       )
       lowerCAmelCase_		= (
           {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
           if is_tf_available()
           else {}
       )

       lowerCAmelCase_		= False
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False
       lowerCAmelCase_		= False







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            __lowercase					=TFMobileViTModelTester(self      )
            __lowercase					=TFMobileViTConfigTester(self						,      config_class=__lowercase						,      has_text_modality=__lowercase      )







       def 			snake_case   (							self			: Dict      ):







            """simple docstring"""







            self.config_tester.run_common_tests()







       @unittest.skip(reason='MobileViT does not use inputs_embeds'      )
       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            pass







       @unittest.skip(reason='MobileViT does not support input and output embeddings'      )
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            pass







       @unittest.skip(reason='MobileViT does not output attentions'      )
       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







            pass







       def 			snake_case   (							self			: Optional[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      )
                 __lowercase					=inspect.signature(model.call      )
                 # 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			: Dict      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            def check_hidden_states_output(__lowercase			: Tuple						,      __lowercase			: Optional[int]						,      __lowercase			: List[Any]      ):
                 __lowercase					=model_class(__lowercase      )

                 __lowercase					=model(**self._prepare_for_class(__lowercase						,      __lowercase      )      )

                 __lowercase					=outputs.hidden_states

                 __lowercase					=5
                 self.assertEqual(len(__lowercase      )						,      __lowercase      )

                 # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                 # with the width and height being successively divided by 2.
                 __lowercase					=2
                 for i in range(len(__lowercase      )      ):
                      self.assertListEqual(
                          list(hidden_states[i].shape[-2:]      )						,      [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]						,      )
                      divisor *= 2

                 self.assertEqual(self.model_tester.output_stride						,      divisor // 2      )

            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()

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

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

                 check_hidden_states_output(__lowercase						,      __lowercase						,      __lowercase      )







       def 			snake_case   (							self			: List[str]      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Tuple      ):







            """simple docstring"""







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







       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('GPU'      )      ) == 0						,      reason='TF does not support backprop for grouped convolutions on CPU.'						,      )
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            super().test_dataset_conversion()







       def 			snake_case   (							self			: Any						,      __lowercase			: Any						,      __lowercase			: List[str]						,      __lowercase			: Optional[Any]=2E-1						,      __lowercase			: Tuple=2E-1      ):







            """simple docstring"""







            self.assertTrue(np.allclose(__lowercase						,      __lowercase						,      atol=__lowercase						,      rtol=__lowercase      )      )







       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('GPU'      )      ) == 0						,      reason='TF does not support backprop for grouped convolutions on CPU.'						,      )
       @slow
       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:
                 # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
                 if model_class.__name__ != "TFMobileViTModel":
                      __lowercase					=model_class(__lowercase      )
                      if getattr(__lowercase						,      'hf_compute_loss'						,      __lowercase      ):
                           super().test_keras_fit()







       def 			snake_case   (							self			: str      ):







            """simple docstring"""







            __lowercase		,     __lowercase					=self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                 # set an ignore index to correctly test the masked loss used in
                 # `TFMobileViTForSemanticSegmentation`.
                 if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                      __lowercase					=5

                 __lowercase					=model_class(__lowercase      )
                 if getattr(__lowercase						,      'hf_compute_loss'						,      __lowercase      ):
                      # The number of elements in the loss should be the same as the number of elements in the label
                      __lowercase					=self._prepare_for_class(inputs_dict.copy()						,      __lowercase						,      return_labels=__lowercase      )
                      __lowercase					=prepared_for_class[
                          sorted(prepared_for_class.keys() - inputs_dict.keys()						,      reverse=__lowercase      )[0]
                      ]
                      __lowercase					=added_label.shape.as_list()[:1]

                      # Test that model correctly compute the loss with kwargs
                      __lowercase					=self._prepare_for_class(inputs_dict.copy()						,      __lowercase						,      return_labels=__lowercase      )
                      __lowercase					={'input_ids', 'pixel_values', 'input_features'}
                      __lowercase					=possible_input_names.intersection(set(__lowercase      )      ).pop()
                      __lowercase					=prepared_for_class.pop(__lowercase      )

                      __lowercase					=model(__lowercase						,      **__lowercase      )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                      # Test that model correctly compute the loss when we mask some positions
                      __lowercase					=self._prepare_for_class(inputs_dict.copy()						,      __lowercase						,      return_labels=__lowercase      )
                      __lowercase					={'input_ids', 'pixel_values', 'input_features'}
                      __lowercase					=possible_input_names.intersection(set(__lowercase      )      ).pop()
                      __lowercase					=prepared_for_class.pop(__lowercase      )
                      if "labels" in prepared_for_class:
                           __lowercase					=prepared_for_class['labels'].numpy()
                           if len(labels.shape      ) > 1 and labels.shape[1] != 1:
                                # labels[0] = -100
                                __lowercase					=tf.convert_to_tensor(__lowercase      )
                                __lowercase					=model(__lowercase						,      **__lowercase      )[0]
                                self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
                                self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
                      __lowercase					=self._prepare_for_class(inputs_dict.copy()						,      __lowercase						,      return_labels=__lowercase      )
                      __lowercase					=model(__lowercase      )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

                      # Test that model correctly compute the loss with a tuple
                      __lowercase					=self._prepare_for_class(inputs_dict.copy()						,      __lowercase						,      return_labels=__lowercase      )

                      # Get keys that were added with the _prepare_for_class function
                      __lowercase					=prepared_for_class.keys() - inputs_dict.keys()
                      __lowercase					=inspect.signature(model.call      ).parameters
                      __lowercase					=list(signature.keys()      )

                      # Create a dictionary holding the location of the tensors in the tuple
                      __lowercase					={0: input_name}
                      for label_key in label_keys:
                           __lowercase					=signature_names.index(__lowercase      )
                           __lowercase					=label_key
                      __lowercase					=sorted(tuple_index_mapping.items()      )
                      # Initialize a list with their default values, update the values and convert to a tuple
                      __lowercase					=[]

                      for name in signature_names:
                           if name != "kwargs":
                                list_input.append(signature[name].default      )

                      for index, value in sorted_tuple_index_mapping:
                           __lowercase					=prepared_for_class[value]

                      __lowercase					=tuple(__lowercase      )

                      # Send to model
                      __lowercase					=model(tuple_input[:-1]      )[0]

                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )







       @slow
       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase					=TFMobileViTModel.from_pretrained(__lowercase      )
                 self.assertIsNotNone(__lowercase      )






def 				__UpperCamelCase							(	):






     '''simple docstring'''





     __lowercase					=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
     return image







@require_tf
class   lowerCAmelCase     (      unittest.TestCase   ):







       @slow
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            __lowercase					=TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'      )

            __lowercase					=MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'      )
            __lowercase					=prepare_img()
            __lowercase					=image_processor(images=__lowercase						,      return_tensors='tf'      )

            # forward pass
            __lowercase					=model(**__lowercase						,      training=__lowercase      )

            # verify the logits
            __lowercase					=tf.TensorShape((1, 1000)      )
            self.assertEqual(outputs.logits.shape						,      __lowercase      )

            __lowercase					=tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]      )

            tf.debugging.assert_near(outputs.logits[0, :3]						,      __lowercase						,      atol=1E-4						,      rtol=1E-04      )







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







            """simple docstring"""







            __lowercase					=TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'      )

            __lowercase					=MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'      )

            __lowercase					=prepare_img()
            __lowercase					=image_processor(images=__lowercase						,      return_tensors='tf'      )

            # forward pass
            __lowercase					=model(inputs.pixel_values						,      training=__lowercase      )
            __lowercase					=outputs.logits

            # verify the logits
            __lowercase					=tf.TensorShape((1, 21, 32, 32)      )
            self.assertEqual(logits.shape						,      __lowercase      )

            __lowercase					=tf.constant(
                [
                    [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
                    [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
                    [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
                ]      )

            tf.debugging.assert_near(logits[0, :3, :3, :3]						,      __lowercase						,      rtol=1E-4						,      atol=1E-4      )





'''simple docstring'''

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      )


