code: 
  
  
  '''simple docstring'''
  
  
  
  import os
  from argparse import ArgumentParser
  from pathlib import Path
  from shutil import copyfile
  from typing import List
  
  from datasets import config
  from datasets.builder import DatasetBuilder
  from datasets.commands import BaseDatasetsCLICommand
  from datasets.download.download_config import DownloadConfig
  from datasets.download.download_manager import DownloadMode
  from datasets.load import dataset_module_factory, import_main_class
  from datasets.utils.info_utils import VerificationMode
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   **_SCREAMING_SNAKE_CASE   :     List[Any]     ):
  					return RunBeamCommand(
  					    args.dataset		,   args.name		,   args.cache_dir		,   args.beam_pipeline_options		,   args.data_dir		,   args.all_configs		,   args.save_info or args.save_infos		,   args.ignore_verifications		,   args.force_redownload		,   **_SCREAMING_SNAKE_CASE		,   )
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	@staticmethod
  	def    __UpperCAmelCase							(     __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						parser.add_parser('run_beam'		,       help='Run a Beam dataset processing pipeline'							)
  						run_beam_parser.add_argument('dataset'		,       type=__a		,       help='Name of the dataset to download'							)
  						run_beam_parser.add_argument('--name'		,       type=__a		,       default=__a		,       help='Dataset config name'							)
  						run_beam_parser.add_argument(
  						    '--cache_dir'		,       type=__a		,       default=__a		,       help='Cache directory where the datasets are stored'		,       )
  						run_beam_parser.add_argument(
  						    '--beam_pipeline_options'		,       type=__a		,       default=''		,       help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'		,       )
  						run_beam_parser.add_argument(
  						    '--data_dir'		,       type=__a		,       default=__a		,       help='Can be used to specify a manual directory to get the files from'		,       )
  						run_beam_parser.add_argument('--all_configs'		,       action='store_true'		,       help='Test all dataset configurations'							)
  						run_beam_parser.add_argument('--save_info'		,       action='store_true'		,       help='Save the dataset infos file'							)
  						run_beam_parser.add_argument(
  						    '--ignore_verifications'		,       action='store_true'		,       help='Run the test without checksums and splits checks'							)
  						run_beam_parser.add_argument('--force_redownload'		,       action='store_true'		,       help='Force dataset redownload'							)
  						# aliases
  						run_beam_parser.add_argument('--save_infos'		,       action='store_true'		,       help='alias for save_info'							)
  						run_beam_parser.set_defaults(func=__a							)
  
  
  
  
  	def __init__(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       **__a		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						dataset
  						__a    : str      					=						name
  						__a    : Any      					=						cache_dir
  						__a    : Tuple      					=						beam_pipeline_options
  						__a    : str      					=						data_dir
  						__a    : Optional[Any]      					=						all_configs
  						__a    : List[Any]      					=						save_infos
  						__a    : List[str]      					=						ignore_verifications
  						__a    : Dict      					=						force_redownload
  						__a    : List[Any]      					=						config_kwargs
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						import apache_beam as beam
  
  						if self._name is not None and self._all_configs:
  											print('Both parameters `name` and `all_configs` can\'t be used at once.'							)
  											exit(1							)
  						__a    : Any      					=						self._dataset, self._name
  						__a    : Union[str, Any]      					=						dataset_module_factory(__a							)
  						__a    : str      					=						import_main_class(dataset_module.module_path							)
  						__a    : List[DatasetBuilder]      					=						[]
  						if self._beam_pipeline_options:
  											__a    : Any      					=						beam.options.pipeline_options.PipelineOptions(
  											    flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','							) if opt]							)
  						else:
  											__a    : Dict      					=						None
  						if self._all_configs and len(builder_cls.BUILDER_CONFIGS							) > 0:
  											for builder_config in builder_cls.BUILDER_CONFIGS:
  																builders.append(
  																    builder_cls(
  																        config_name=builder_config.name		,       data_dir=self._data_dir		,       hash=dataset_module.hash		,       beam_options=__a		,       cache_dir=self._cache_dir		,       base_path=dataset_module.builder_kwargs.get('base_path'							)		,       )							)
  						else:
  											builders.append(
  											    builder_cls(
  											        config_name=__a		,       data_dir=self._data_dir		,       beam_options=__a		,       cache_dir=self._cache_dir		,       base_path=dataset_module.builder_kwargs.get('base_path'							)		,       **self._config_kwargs		,       )							)
  
  						for builder in builders:
  											builder.download_and_prepare(
  											    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
  											    if not self._force_redownload
  											    else DownloadMode.FORCE_REDOWNLOAD		,       download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH							)		,       verification_mode=VerificationMode.NO_CHECKS
  											    if self._ignore_verifications
  											    else VerificationMode.ALL_CHECKS		,       try_from_hf_gcs=__a		,       )
  											if self._save_infos:
  																builder._save_infos()
  
  						print('Apache beam run successful.'							)
  
  						# If save_infos=True, the dataset infos file is created next to the loaded module file.
  						# Let's move it to the original directory of the dataset script, to allow the user to
  						# upload them on S3 at the same time afterwards.
  						if self._save_infos:
  											__a    : Tuple      					=						os.path.join(builder_cls.get_imported_module_dir()		,       config.DATASETDICT_INFOS_FILENAME							)
  
  											__a    : Optional[int]      					=						Path(__a							).name + '.py'
  
  											__a    : Dict      					=						os.path.join(__a		,       __a							)
  											if os.path.isfile(__a							):
  																__a    : int      					=						os.path.dirname(__a							)
  											elif os.path.isfile(__a							):
  																__a    : List[Any]      					=						path
  											else:  # in case of a remote dataset
  																print(f"""Dataset Infos file saved at {dataset_infos_path}"""							)
  																exit(1							)
  
  											# Move datasetinfo back to the user
  											__a    : str      					=						os.path.join(__a		,       config.DATASETDICT_INFOS_FILENAME							)
  											copyfile(__a		,       __a							)
  											print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""							)
  
  
  
  
style_context: 
  
  
  '''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 							__UpperCamelCase       :
  
  
  
  
  	def __init__(     self		,       __a		,       __a=13		,       __a=32		,       __a=2		,       __a=3		,       __a=16		,       __a=[1, 2, 1]		,       __a=[2, 2, 4]		,       __a=2		,       __a=2.0		,       __a=True		,       __a=0.0		,       __a=0.0		,       __a=0.1		,       __a="gelu"		,       __a=False		,       __a=True		,       __a=0.02		,       __a=1E-5		,       __a=True		,       __a=None		,       __a=True		,       __a=10		,       __a=8		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						parent
  						__a    : int      					=						batch_size
  						__a    : str      					=						image_size
  						__a    : List[Any]      					=						patch_size
  						__a    : Any      					=						num_channels
  						__a    : int      					=						embed_dim
  						__a    : Optional[Any]      					=						depths
  						__a    : Optional[Any]      					=						num_heads
  						__a    : List[Any]      					=						window_size
  						__a    : Dict      					=						mlp_ratio
  						__a    : Any      					=						qkv_bias
  						__a    : Any      					=						hidden_dropout_prob
  						__a    : Union[str, Any]      					=						attention_probs_dropout_prob
  						__a    : str      					=						drop_path_rate
  						__a    : Dict      					=						hidden_act
  						__a    : List[str]      					=						use_absolute_embeddings
  						__a    : str      					=						patch_norm
  						__a    : List[str]      					=						layer_norm_eps
  						__a    : Optional[Any]      					=						initializer_range
  						__a    : str      					=						is_training
  						__a    : Any      					=						scope
  						__a    : Union[str, Any]      					=						use_labels
  						__a    : Optional[int]      					=						type_sequence_label_size
  						__a    : Optional[Any]      					=						encoder_stride
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
  
  						__a    : List[Any]      					=						None
  						if self.use_labels:
  											__a    : str      					=						ids_tensor([self.batch_size]		,       self.type_sequence_label_size							)
  
  						__a    : int      					=						self.get_config()
  
  						return config, pixel_values, labels
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''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		,       )
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						TFSwinModel(config=__a							)
  						__a    : List[str]      					=						model(__a							)
  
  						__a    : Dict      					=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
  						__a    : Any      					=						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    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						TFSwinForMaskedImageModeling(config=__a							)
  						__a    : Tuple      					=						model(__a							)
  						self.parent.assertEqual(
  						    result.logits.shape		,       (self.batch_size, self.num_channels, self.image_size, self.image_size)							)
  
  						# test greyscale images
  						__a    : str      					=						1
  						__a    : Union[str, Any]      					=						TFSwinForMaskedImageModeling(__a							)
  
  						__a    : Optional[int]      					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  						__a    : Any      					=						model(__a							)
  						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, 1, self.image_size, self.image_size)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						self.type_sequence_label_size
  						__a    : Tuple      					=						TFSwinForImageClassification(__a							)
  						__a    : Any      					=						model(__a		,       labels=__a							)
  						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)							)
  
  						# test greyscale images
  						__a    : Tuple      					=						1
  						__a    : str      					=						TFSwinForImageClassification(__a							)
  						__a    : List[Any]      					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  						__a    : List[str]      					=						model(__a							)
  						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						self.prepare_config_and_inputs()
  						__a					,       __a					,       __a    : Tuple      					=						config_and_inputs
  						__a    : Dict      					=						{'pixel_values': pixel_values}
  						return config, inputs_dict
  
  
  
  
  
  @require_tf
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					(
  	    (
  	        TFSwinModel,
  	        TFSwinForImageClassification,
  	        TFSwinForMaskedImageModeling,
  	    )
  	    if is_tf_available()
  	    else ()
  	)
  	A_			    =					(
  	    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
  	    if is_tf_available()
  	    else {}
  	)
  
  	A_			    =					False
  	A_			    =					False
  	A_			    =					False
  	A_			    =					False
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						TFSwinModelTester(self							)
  						__a    : Union[str, Any]      					=						ConfigTester(self		,       config_class=__a		,       embed_dim=37							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_masked_image_modeling(*__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_image_classification(*__a							)
  
  
  
  
  	@unittest.skip(reason='Swin does not use inputs_embeds'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : int      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  											__a    : Dict      					=						model_class(__a							)
  											self.assertIsInstance(model.get_input_embeddings()		,       tf.keras.layers.Layer							)
  											__a    : str      					=						model.get_output_embeddings()
  											self.assertTrue(x is None or isinstance(__a		,       tf.keras.layers.Dense							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Dict      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  											__a    : Optional[int]      					=						model_class(__a							)
  											__a    : Union[str, Any]      					=						inspect.signature(model.call							)
  											# signature.parameters is an OrderedDict => so arg_names order is deterministic
  											__a    : Optional[int]      					=						[*signature.parameters.keys()]
  
  											__a    : str      					=						['pixel_values']
  											self.assertListEqual(arg_names[:1]		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Any      					=						self.model_tester.prepare_config_and_inputs_for_common()
  						__a    : Optional[int]      					=						True
  
  						for model_class in self.all_model_classes:
  											__a    : Optional[int]      					=						True
  											__a    : Dict      					=						False
  											__a    : Any      					=						True
  											__a    : Tuple      					=						model_class(__a							)
  											__a    : List[str]      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  											__a    : int      					=						outputs.attentions
  											__a    : List[Any]      					=						len(self.model_tester.depths							)
  											self.assertEqual(len(__a							)		,       __a							)
  
  											# check that output_attentions also work using config
  											del inputs_dict["output_attentions"]
  											__a    : Any      					=						True
  											__a    : List[Any]      					=						config.window_size**2
  											__a    : Optional[Any]      					=						model_class(__a							)
  											__a    : Tuple      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  											__a    : str      					=						outputs.attentions
  											self.assertEqual(len(__a							)		,       __a							)
  
  											self.assertListEqual(
  											    list(attentions[0].shape[-3:]							)		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )
  											__a    : Any      					=						len(__a							)
  
  											# Check attention is always last and order is fine
  											__a    : int      					=						True
  											__a    : Any      					=						True
  											__a    : str      					=						model_class(__a							)
  											__a    : str      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  
  											if hasattr(self.model_tester		,       'num_hidden_states_types'							):
  																__a    : Optional[int]      					=						self.model_tester.num_hidden_states_types
  											else:
  																# also another +1 for reshaped_hidden_states
  																__a    : Union[str, Any]      					=						2
  											self.assertEqual(out_len + added_hidden_states		,       len(__a							)							)
  
  											__a    : Dict      					=						outputs.attentions
  
  											self.assertEqual(len(__a							)		,       __a							)
  
  											self.assertListEqual(
  											    list(self_attentions[0].shape[-3:]							)		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						model_class(__a							)
  						__a    : int      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  						__a    : Tuple      					=						outputs.hidden_states
  
  						__a    : Any      					=						getattr(
  						    self.model_tester		,       'expected_num_hidden_layers'		,       len(self.model_tester.depths							) + 1							)
  						self.assertEqual(len(__a							)		,       __a							)
  
  						# Swin has a different seq_length
  						__a    : Optional[int]      					=						to_atuple(config.patch_size							)
  
  						__a    : List[str]      					=						(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]		,       )
  
  						__a    : str      					=						outputs.reshaped_hidden_states
  						self.assertEqual(len(__a							)		,       __a							)
  
  						__a					,       __a					,       __a					,       __a    : str      					=						reshaped_hidden_states[0].shape
  
  						__a    : int      					=						tf.reshape(reshaped_hidden_states[0]		,       (batch_size, num_channels, height * width)							)
  						__a    : List[str]      					=						tf.transpose(__a		,       (0, 2, 1)							)
  
  						self.assertListEqual(
  						    list(reshaped_hidden_states.shape[-2:]							)		,       [num_patches, self.model_tester.embed_dim]		,       )
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Optional[Any]      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						__a    : Union[str, Any]      					=						to_atuple(self.model_tester.image_size							)
  
  						for model_class in self.all_model_classes:
  											__a    : Optional[int]      					=						True
  											self.check_hidden_states_output(__a		,       __a		,       __a		,       __a							)
  
  											# check that output_hidden_states also work using config
  											del inputs_dict["output_hidden_states"]
  											__a    : Tuple      					=						True
  
  											self.check_hidden_states_output(__a		,       __a		,       __a		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						self.model_tester.prepare_config_and_inputs_for_common()
  						__a    : List[str]      					=						3
  
  						__a    : Any      					=						to_atuple(self.model_tester.image_size							)
  						__a    : Any      					=						to_atuple(config.patch_size							)
  
  						__a    : Any      					=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  						__a    : Optional[int]      					=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
  						for model_class in self.all_model_classes:
  											__a    : Union[str, Any]      					=						True
  											self.check_hidden_states_output(__a		,       __a		,       __a		,       (padded_height, padded_width)							)
  
  											# check that output_hidden_states also work using config
  											del inputs_dict["output_hidden_states"]
  											__a    : int      					=						True
  											self.check_hidden_states_output(__a		,       __a		,       __a		,       (padded_height, padded_width)							)
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  											__a    : List[Any]      					=						TFSwinModel.from_pretrained(__a							)
  											self.assertIsNotNone(__a							)
  
  
  
  
  
  @require_vision
  @require_tf
  class 							__UpperCamelCase       (	unittest.TestCase      ):
  
  
  
  
  	@cached_property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (
  						    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
  						    if is_vision_available()
  						    else None
  						)
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
  						__a    : str      					=						self.default_image_processor
  
  						__a    : Optional[Any]      					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
  						__a    : Optional[Any]      					=						image_processor(images=__a		,       return_tensors='tf'							)
  
  						# forward pass
  						__a    : List[Any]      					=						model(__a							)
  
  						# verify the logits
  						__a    : List[str]      					=						tf.TensorShape((1, 1000)							)
  						self.assertEqual(outputs.logits.shape		,       __a							)
  						__a    : Optional[Any]      					=						tf.constant([-0.0948, -0.6454, -0.0921]							)
  						self.assertTrue(np.allclose(outputs.logits[0, :3]		,       __a		,       atol=1E-4							)							)
  
  
  
  