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 __snake_case							:




   def __init__(       self     :							int			,      _lowercase     :							List[Any]			,      _lowercase     :							Dict=13			,      _lowercase     :							List[str]=32			,      _lowercase     :							Optional[Any]=2			,      _lowercase     :							str=3			,      _lowercase     :							List[str]=16			,      _lowercase     :							str=[1, 2, 1]			,      _lowercase     :							Optional[int]=[2, 2, 4]			,      _lowercase     :							List[str]=2			,      _lowercase     :							Dict=2.0			,      _lowercase     :							Any=True			,      _lowercase     :							str=0.0			,      _lowercase     :							Optional[Any]=0.0			,      _lowercase     :							Optional[int]=0.1			,      _lowercase     :							int="gelu"			,      _lowercase     :							Dict=False			,      _lowercase     :							Tuple=True			,      _lowercase     :							Optional[int]=0.02			,      _lowercase     :							Dict=1E-5			,      _lowercase     :							Dict=True			,      _lowercase     :							int=None			,      _lowercase     :							Tuple=True			,      _lowercase     :							Optional[Any]=10			,      _lowercase     :							Dict=8			,      _lowercase     :							Optional[Any]=["stage1", "stage2"]			,      _lowercase     :							Dict=[1, 2]			,      ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__     				= self.get_config()

     return config, pixel_values, labels




   def       __a		(       self     :							List[str] ):



     """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       __a		(       self     :							List[Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= SwinModel(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase )

     SCREAMING_SNAKE_CASE__     				= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							List[str]			,      _lowercase     :							Any			,      _lowercase     :							Tuple			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= SwinBackbone(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= None
     SCREAMING_SNAKE_CASE__     				= SwinBackbone(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Tuple			,      _lowercase     :							List[Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[Any] ):



     """simple docstring"""






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

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

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




   def       __a		(       self     :							List[str]			,      _lowercase     :							List[Any]			,      _lowercase     :							Dict			,      _lowercase     :							Any ):



     """simple docstring"""






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

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

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




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	
     )     				= config_and_inputs
     SCREAMING_SNAKE_CASE__     				= {"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						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       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= SwinModelTester(self )
     SCREAMING_SNAKE_CASE__     				= ConfigTester(self			,      config_class=_lowercase			,      embed_dim=37 )




   def       __a		(       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       __a		(       self     :							Dict ):



     """simple docstring"""






     return




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							int ):



     """simple docstring"""






     pass




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     super().test_training_gradient_checkpointing()




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*_lowercase )




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase )




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							str ):



     """simple docstring"""






     pass




   @unittest.skip(reason="""Swin Transformer does not use feedforward chunking""" )
   def       __a		(       self     :							str ):



     """simple docstring"""






     pass




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

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




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__     				= ["""pixel_values"""]
       self.assertListEqual(arg_names[:1]			,      _lowercase )




   def       __a		(       self     :							Any ):



     """simple docstring"""






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

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

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

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

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

       SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							List[Any]			,      _lowercase     :							int			,      _lowercase     :							Tuple			,      _lowercase     :							Dict			,      _lowercase     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= model_class(_lowercase )
     model.to(_lowercase )
     model.eval()

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

     SCREAMING_SNAKE_CASE__     				= outputs.hidden_states

     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= (
         config.patch_size
         if isinstance(config.patch_size			,      collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

     SCREAMING_SNAKE_CASE__     				= (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":
       SCREAMING_SNAKE_CASE__     				= outputs.reshaped_hidden_states
       self.assertEqual(len(_lowercase )			,      _lowercase )

       SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= reshaped_hidden_states[0].shape
       SCREAMING_SNAKE_CASE__     				= (
           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       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__     				= (
         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:
       SCREAMING_SNAKE_CASE__     				= 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"]
       SCREAMING_SNAKE_CASE__     				= True

       self.check_hidden_states_output(_lowercase			,      _lowercase			,      _lowercase			,      _lowercase )




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__     				= (
         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)
     )
     SCREAMING_SNAKE_CASE__     				= (
         config.patch_size
         if isinstance(config.patch_size			,      collections.abc.Iterable )
         else (config.patch_size, config.patch_size)
     )

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

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= 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"]
       SCREAMING_SNAKE_CASE__     				= True
       self.check_hidden_states_output(_lowercase			,      _lowercase			,      _lowercase			,      (padded_height, padded_width) )




   @slow
   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__     				= _config_zero_init(_lowercase )
     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= 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 __snake_case							(		unittest.TestCase						):




   @cached_property
   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224""" ).to(_lowercase )
     SCREAMING_SNAKE_CASE__     				= self.default_image_processor

     SCREAMING_SNAKE_CASE__     				= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
     SCREAMING_SNAKE_CASE__     				= image_processor(images=_lowercase			,      return_tensors="""pt""" ).to(_lowercase )

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

     # verify the logits
     SCREAMING_SNAKE_CASE__     				= torch.Size((1, 10_00) )
     self.assertEqual(outputs.logits.shape			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= torch.tensor([-0.09_48, -0.64_54, -0.09_21] ).to(_lowercase )
     self.assertTrue(torch.allclose(outputs.logits[0, :3]			,      _lowercase			,      atol=1E-4 ) )







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




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= SwinModelTester(self )

# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
