
'''simple docstring'''





import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
     A           =   True
     A           =   "p3dn.24xlarge"
     import smdistributed.dataparallel.tensorflow as sdp
else:
     A           =   False
def   snake_case_       (							_a     :							Any ,					_a     :							int ,					_a     :							str ,					_a     :							List[Any] ,					_a     :							Any ,					_a     :							Optional[Any] ,					_a     :							List[Any]=None			):
      snake_case_						       =							tqdm(_a			)
      for i, batch in enumerate(_a			):
            with tf.GradientTape() as tape:
                  snake_case_       ,	snake_case_						       =							batch
                  snake_case_						       =							model(_a			)
                  snake_case_						       =							loss(_a ,					outputs.logits			)

            if SDP_ENABLED:
                  snake_case_						       =							sdp.DistributedGradientTape(_a ,					sparse_as_dense=_a			)

            snake_case_						       =							tape.gradient(_a ,					model.trainable_variables			)
            opt.apply_gradients(zip(_a ,					model.trainable_variables			)			)

            pbar.set_description(F'''Loss: {loss_value:.4f}'''			)

            if SDP_ENABLED and i == 0:
                  sdp.broadcast_variables(model.variables ,					root_rank=0			)
                  sdp.broadcast_variables(opt.variables() ,					root_rank=0			)

            if max_steps and i >= max_steps:
                  break

      snake_case_						       =							{"loss": loss_value.numpy()}
      return train_results
def   snake_case_       (							_a     :							Optional[Any] ,					_a     :							List[Any] ,					_a     :							int			):
      # Load dataset
      snake_case_       ,	snake_case_						       =							load_dataset("imdb" ,					split=["train", "test"]			)

      # Preprocess train dataset
      snake_case_						       =							train_dataset.map(
          lambda _a			: tokenizer(e["text"] ,					truncation=_a ,					padding="max_length"			) ,					batched=_a			)
      train_dataset.set_format(type="tensorflow" ,					columns=["input_ids", "attention_mask", "label"]			)

      snake_case_						       =							{
          x: train_dataset[x].to_tensor(default_value=0 ,					shape=[None, tokenizer.model_max_length]			)
          for x in ["input_ids", "attention_mask"]
      }
      snake_case_						       =							tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])			)

      # Preprocess test dataset
      snake_case_						       =							test_dataset.map(
          lambda _a			: tokenizer(e["text"] ,					truncation=_a ,					padding="max_length"			) ,					batched=_a			)
      test_dataset.set_format(type="tensorflow" ,					columns=["input_ids", "attention_mask", "label"]			)

      snake_case_						       =							{
          x: test_dataset[x].to_tensor(default_value=0 ,					shape=[None, tokenizer.model_max_length]			)
          for x in ["input_ids", "attention_mask"]
      }
      snake_case_						       =							tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])			)

      if SDP_ENABLED:
            snake_case_						       =							tf_train_dataset.shard(sdp.size() ,					sdp.rank()			)
            snake_case_						       =							tf_test_dataset.shard(sdp.size() ,					sdp.rank()			)
      snake_case_						       =							tf_train_dataset.batch(_a ,					drop_remainder=_a			)
      snake_case_						       =							tf_test_dataset.batch(_a ,					drop_remainder=_a			)

      return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
     A           =   argparse.ArgumentParser()

     # Hyperparameters sent by the client are passed as command-line arguments to the script.
     parser.add_argument("--epochs", type=int, default=3)
     parser.add_argument("--per_device_train_batch_size", type=int, default=1_6)
     parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
     parser.add_argument("--model_name_or_path", type=str)
     parser.add_argument("--learning_rate", type=str, default=5e-5)
     parser.add_argument("--do_train", type=bool, default=True)
     parser.add_argument("--do_eval", type=bool, default=True)
     parser.add_argument("--output_dir", type=str)
     parser.add_argument("--max_steps", type=int, default=None)

     # Data, model, and output directories
     parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
     parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
     parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

     A    ,     A           =   parser.parse_known_args()

     # Set up logging
     A           =   logging.getLogger(__name__)

     logging.basicConfig(
         level=logging.getLevelName("INFO"),
         handlers=[logging.StreamHandler(sys.stdout)],
         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
     )

     if SDP_ENABLED:
          sdp.init()

          A           =   tf.config.experimental.list_physical_devices("GPU")
          for gpu in gpus:
               tf.config.experimental.set_memory_growth(gpu, True)
          if gpus:
               tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

    # Load model and tokenizer
     A           =   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     A           =   AutoTokenizer.from_pretrained(args.model_name_or_path)

     # get datasets
     A    ,     A           =   get_datasets(
         tokenizer=tokenizer,
         train_batch_size=args.per_device_train_batch_size,
         eval_batch_size=args.per_device_eval_batch_size,
     )

     # fine optimizer and loss
     A           =   tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     A           =   tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     A           =   [tf.keras.metrics.SparseCategoricalAccuracy()]
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

     # Training
     if args.do_train:
          # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
          A           =   time.time()
          A           =   fit(
              model,
              loss,
              optimizer,
              tf_train_dataset,
              args.epochs,
              args.per_device_train_batch_size,
              max_steps=args.max_steps,
          )
          A           =   time.time() - start_train_time
          logger.info("*** Train ***")
          logger.info(F"""train_runtime = {end_train_time}""")

          A           =   os.path.join(args.output_dir, "train_results.txt")

          if not SDP_ENABLED or sdp.rank() == 0:
               with open(output_eval_file, "w") as writer:
                    logger.info("***** Train results *****")
                    logger.info(train_results)
                    for key, value in train_results.items():
                         logger.info(F"""  {key} = {value}""")
                         writer.write(F"""{key} = {value}\n""")

    # Evaluation
     if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
          A           =   model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
          logger.info("*** Evaluate ***")

          A           =   os.path.join(args.output_dir, "eval_results.txt")

          with open(output_eval_file, "w") as writer:
               logger.info("***** Eval results *****")
               logger.info(result)
               for key, value in result.items():
                    logger.info(F"""  {key} = {value}""")
                    writer.write(F"""{key} = {value}\n""")

    # Save result
     if SDP_ENABLED:
          if sdp.rank() == 0:
               model.save_pretrained(args.output_dir)
               tokenizer.save_pretrained(args.output_dir)
     else:
          model.save_pretrained(args.output_dir)
          tokenizer.save_pretrained(args.output_dir)



'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

     from transformers.models.swin.modeling_tf_swin import (
         TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
         TFSwinModel,
     )


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class  lowerCAmelCase__   :



  '''simple docstring'''






  def __init__(self  : Optional[int]	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Any=13	, lowerCAmelCase_  : int=32	, lowerCAmelCase_  : int=2	, lowerCAmelCase_  : List[str]=3	, lowerCAmelCase_  : Any=16	, lowerCAmelCase_  : Tuple=[1, 2, 1]	, lowerCAmelCase_  : List[str]=[2, 2, 4]	, lowerCAmelCase_  : Dict=2	, lowerCAmelCase_  : str=2.0	, lowerCAmelCase_  : int=True	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.0	, lowerCAmelCase_  : List[Any]=0.1	, lowerCAmelCase_  : Optional[int]="gelu"	, lowerCAmelCase_  : Dict=False	, lowerCAmelCase_  : str=True	, lowerCAmelCase_  : Optional[int]=0.02	, lowerCAmelCase_  : List[str]=1E-5	, lowerCAmelCase_  : Dict=True	, lowerCAmelCase_  : Tuple=None	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : str=10	, lowerCAmelCase_  : Tuple=8	, )   ->       None:
        snake_case_						       =							parent
        snake_case_						       =							batch_size
        snake_case_						       =							image_size
        snake_case_						       =							patch_size
        snake_case_						       =							num_channels
        snake_case_						       =							embed_dim
        snake_case_						       =							depths
        snake_case_						       =							num_heads
        snake_case_						       =							window_size
        snake_case_						       =							mlp_ratio
        snake_case_						       =							qkv_bias
        snake_case_						       =							hidden_dropout_prob
        snake_case_						       =							attention_probs_dropout_prob
        snake_case_						       =							drop_path_rate
        snake_case_						       =							hidden_act
        snake_case_						       =							use_absolute_embeddings
        snake_case_						       =							patch_norm
        snake_case_						       =							layer_norm_eps
        snake_case_						       =							initializer_range
        snake_case_						       =							is_training
        snake_case_						       =							scope
        snake_case_						       =							use_labels
        snake_case_						       =							type_sequence_label_size
        snake_case_						       =							encoder_stride

  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Dict:
        snake_case_						       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

        snake_case_						       =							self.get_config()

        return config, pixel_values, labels

  def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
        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	, )

  def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : List[Any]	, lowerCAmelCase_  : Optional[int]    )   ->       Any:
        snake_case_						       =							TFSwinModel(config=lowerCAmelCase_    )
        snake_case_						       =							model(lowerCAmelCase_    )

        snake_case_						       =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
        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 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : Optional[int]    )   ->       Optional[int]:
        snake_case_						       =							TFSwinForMaskedImageModeling(config=lowerCAmelCase_    )
        snake_case_						       =							model(lowerCAmelCase_    )
        self.parent.assertEqual(
            result.logits.shape	, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

        # test greyscale images
        snake_case_						       =							1
        snake_case_						       =							TFSwinForMaskedImageModeling(lowerCAmelCase_    )

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

  def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Optional[Any]    )   ->       Tuple:
        snake_case_						       =							self.type_sequence_label_size
        snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
        snake_case_						       =							model(lowerCAmelCase_	, labels=lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )

        # test greyscale images
        snake_case_						       =							1
        snake_case_						       =							TFSwinForImageClassification(lowerCAmelCase_    )
        snake_case_						       =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
        snake_case_						       =							model(lowerCAmelCase_    )
        self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.type_sequence_label_size)    )


  def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       str:
        snake_case_						       =							self.prepare_config_and_inputs()
        snake_case_       ,	snake_case_       ,	snake_case_						       =							config_and_inputs
        snake_case_						       =							{"pixel_values": pixel_values}
        return config, inputs_dict



@require_tf
class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):



  '''simple docstring'''






  _UpperCamelCase						= (
      (
          TFSwinModel,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  _UpperCamelCase						= (
      {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
      if is_tf_available()
      else {}
  )

  _UpperCamelCase						= False
  _UpperCamelCase						= False
  _UpperCamelCase						= False
  _UpperCamelCase						= False

  def 		_lowerCamelCase	(self  : Any    )   ->       List[Any]:
        snake_case_						       =							TFSwinModelTester(self    )
        snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, embed_dim=37    )

  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Optional[Any]:
        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 		_lowerCamelCase	(self  : List[Any]    )   ->       List[str]:
        return

  def 		_lowerCamelCase	(self  : Any    )   ->       Dict:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : int    )   ->       Any:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Tuple    )   ->       Optional[Any]:
        snake_case_						       =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_    )

  @unittest.skip(reason="Swin does not use inputs_embeds"    )
  def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Union[str, Any]:
        pass

  def 		_lowerCamelCase	(self  : List[str]    )   ->       Any:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              snake_case_						       =							model_class(lowerCAmelCase_    )
              self.assertIsInstance(model.get_input_embeddings()	, tf.keras.layers.Layer    )
              snake_case_						       =							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCAmelCase_	, tf.keras.layers.Dense    )    )

  def 		_lowerCamelCase	(self  : Dict    )   ->       Dict:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case_						       =							["pixel_values"]
              self.assertListEqual(arg_names[:1]	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Dict    )   ->       Any:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_						       =							True

        for model_class in self.all_model_classes:
              snake_case_						       =							True
              snake_case_						       =							False
              snake_case_						       =							True
              snake_case_						       =							model_class(lowerCAmelCase_    )
              snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
              snake_case_						       =							outputs.attentions
              snake_case_						       =							len(self.model_tester.depths    )
              self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              snake_case_						       =							True
              snake_case_						       =							config.window_size**2
              snake_case_						       =							model_class(lowerCAmelCase_    )
              snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
              snake_case_						       =							outputs.attentions
              self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

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

              # Check attention is always last and order is fine
              snake_case_						       =							True
              snake_case_						       =							True
              snake_case_						       =							model_class(lowerCAmelCase_    )
              snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )

              if hasattr(self.model_tester	, "num_hidden_states_types"    ):
                    snake_case_						       =							self.model_tester.num_hidden_states_types
              else:
                    # also another +1 for reshaped_hidden_states
                    snake_case_						       =							2
              self.assertEqual(out_len + added_hidden_states	, len(lowerCAmelCase_    )    )

              snake_case_						       =							outputs.attentions

              self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

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

  def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : List[Any]    )   ->       Optional[int]:
        snake_case_						       =							model_class(lowerCAmelCase_    )
        snake_case_						       =							model(**self._prepare_for_class(lowerCAmelCase_	, lowerCAmelCase_    )    )
        snake_case_						       =							outputs.hidden_states

        snake_case_						       =							getattr(
            self.model_tester	, "expected_num_hidden_layers"	, len(self.model_tester.depths    ) + 1    )
        self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

        # Swin has a different seq_length
        snake_case_						       =							to_atuple(config.patch_size    )

        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]	, )

        snake_case_						       =							outputs.reshaped_hidden_states
        self.assertEqual(len(lowerCAmelCase_    )	, lowerCAmelCase_    )

        snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_						       =							reshaped_hidden_states[0].shape

        snake_case_						       =							tf.reshape(reshaped_hidden_states[0]	, (batch_size, num_channels, height * width)    )
        snake_case_						       =							tf.transpose(lowerCAmelCase_	, (0, 2, 1)    )

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

  def 		_lowerCamelCase	(self  : Dict    )   ->       str:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_						       =							to_atuple(self.model_tester.image_size    )

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

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

              self.check_hidden_states_output(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )

  def 		_lowerCamelCase	(self  : Tuple    )   ->       List[Any]:
        snake_case_       ,	snake_case_						       =							self.model_tester.prepare_config_and_inputs_for_common()
        snake_case_						       =							3

        snake_case_						       =							to_atuple(self.model_tester.image_size    )
        snake_case_						       =							to_atuple(config.patch_size    )

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

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

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


  @slow
  def 		_lowerCamelCase	(self  : Any    )   ->       List[str]:
        for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case_						       =							TFSwinModel.from_pretrained(lowerCAmelCase_    )
              self.assertIsNotNone(lowerCAmelCase_    )



@require_vision
@require_tf
class  lowerCAmelCase__   (       unittest.TestCase       ):



  '''simple docstring'''






  @cached_property
  def 		_lowerCamelCase	(self  : Dict    )   ->       str:
        return (
            AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
            if is_vision_available()
            else None
        )


  @slow
  def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
        snake_case_						       =							TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"    )
        snake_case_						       =							self.default_image_processor

        snake_case_						       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
        snake_case_						       =							image_processor(images=lowerCAmelCase_	, return_tensors="tf"    )

        # forward pass
        snake_case_						       =							model(lowerCAmelCase_    )

        # verify the logits
        snake_case_						       =							tf.TensorShape((1, 10_00)    )
        self.assertEqual(outputs.logits.shape	, lowerCAmelCase_    )
        snake_case_						       =							tf.constant([-0.0_948, -0.6_454, -0.0_921]    )
        self.assertTrue(np.allclose(outputs.logits[0, :3]	, lowerCAmelCase_	, atol=1E-4    )    )

