




# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_lowerCamelCase      :  str      						=						get_logger(__name__)
class        __snake_case  :
				lowerCAmelCase__				=     "dummy_data"
				lowerCAmelCase__				=     "datasets"
				lowerCAmelCase__				=     False



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[Version, str]		,	_UpperCAmelCase      :		Optional[str] = None		,	_UpperCAmelCase      :		bool = False		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Optional[List[Callable]] = None		,	)  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      0
								_lowerCAmelCase    :    List[Any]										=      dataset_name
								_lowerCAmelCase    :    Optional[Any]										=      cache_dir
								_lowerCAmelCase    :    str										=      use_local_dummy_data
								_lowerCAmelCase    :    List[Any]										=      config
								# download_callbacks take a single url as input
								_lowerCAmelCase    :    List[Callable]										=      download_callbacks or []
								# if False, it doesn't load existing files and it returns the paths of the dummy files relative
								# to the dummy_data zip file root
								_lowerCAmelCase    :    Optional[Any]										=      load_existing_dummy_data

								# TODO(PVP, QL) might need to make this more general
								_lowerCAmelCase    :    Dict										=      str(_UpperCAmelCase      )
								# to be downloaded
								_lowerCAmelCase    :    Union[str, Any]										=      None
								_lowerCAmelCase    :    Dict										=      None



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Tuple:





								'''simple docstring'''




								if self._dummy_file is None:
												_lowerCAmelCase    :    List[Any]										=      self.download_dummy_data()
								return self._dummy_file



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[Any]:





								'''simple docstring'''




								if self.config is not None:
												# structure is dummy / config_name / version_name
												return os.path.join("""dummy"""		,	self.config.name		,	self.version_name      )
								# structure is dummy / version_name
								return os.path.join("""dummy"""		,	self.version_name      )



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Tuple:





								'''simple docstring'''




								return os.path.join(self.dummy_data_folder		,	"""dummy_data.zip"""      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      (
								    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
								)

								_lowerCAmelCase    :    Dict										=      cached_path(
								    _UpperCAmelCase		,	cache_dir=self.cache_dir		,	extract_compressed_file=_UpperCAmelCase		,	force_extract=_UpperCAmelCase      )

								return os.path.join(_UpperCAmelCase		,	self.dummy_file_name      )



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Union[str, Any]:





								'''simple docstring'''




								return os.path.join(self.datasets_scripts_dir		,	self.dataset_name		,	self.dummy_zip_file      )



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Any:





								'''simple docstring'''




								if self._bucket_url is None:
												_lowerCAmelCase    :    Any										=      hf_github_url(self.dataset_name		,	self.dummy_zip_file.replace(os.sep		,	"""/"""      )      )
								return self._bucket_url



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								if os.path.isdir(self.dummy_file      ):
												return self.dummy_file
								# else cut off path to file -> example `xsum`.
								return "/".join(self.dummy_file.replace(os.sep		,	"""/"""      ).split("""/"""      )[:-1]      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Dict		,	*_UpperCAmelCase      :		Union[str, Any]      )  ->    Tuple:





								'''simple docstring'''




								if self.load_existing_dummy_data:
												# dummy data is downloaded and tested
												_lowerCAmelCase    :    List[str]										=      self.dummy_file
								else:
												# dummy data cannot be downloaded and only the path to dummy file is returned
												_lowerCAmelCase    :    Dict										=      self.dummy_file_name

								# special case when data_url is a dict
								if isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
												return self.create_dummy_data_dict(_UpperCAmelCase		,	_UpperCAmelCase      )
								elif isinstance(_UpperCAmelCase		,	(list, tuple)      ):
												return self.create_dummy_data_list(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												return self.create_dummy_data_single(_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		Tuple		,	*_UpperCAmelCase      :		Optional[Any]      )  ->    str:





								'''simple docstring'''




								return self.download_and_extract(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								return self.download_and_extract(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Dict		,	*_UpperCAmelCase      :		List[str]		,	**_UpperCAmelCase      :		Tuple      )  ->    Any:





								'''simple docstring'''




								return path



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''simple docstring'''




								return {}



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      {}
								for key, single_urls in data_url.items():
												for download_callback in self.download_callbacks:
																if isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
																				for single_url in single_urls:
																								download_callback(_UpperCAmelCase      )
																else:
																				_lowerCAmelCase    :    int										=      single_urls
																				download_callback(_UpperCAmelCase      )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
												if isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
																_lowerCAmelCase    :    Any										=      [os.path.join(_UpperCAmelCase		,	urllib.parse.quote_plus(Path(_UpperCAmelCase      ).name      )      ) for x in single_urls]
												else:
																_lowerCAmelCase    :    Dict										=      single_urls
																_lowerCAmelCase    :    str										=      os.path.join(_UpperCAmelCase		,	urllib.parse.quote_plus(Path(_UpperCAmelCase      ).name      )      )
												_lowerCAmelCase    :    Tuple										=      value

								# make sure that values are unique
								if all(isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ) for i in dummy_data_dict.values()      ) and len(set(dummy_data_dict.values()      )      ) < len(
								    dummy_data_dict.values()      ):
												# append key to value to make its name unique
												_lowerCAmelCase    :    Optional[int]										=      {key: value + key for key, value in dummy_data_dict.items()}

								return dummy_data_dict



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      []
								# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
								_lowerCAmelCase    :    Tuple										=      all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}"""		,	_UpperCAmelCase      )      ) for url in data_url      )
								_lowerCAmelCase    :    Tuple										=      all(
								    url.startswith("""https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"""      ) for url in data_url      )
								if data_url and (is_tf_records or is_pubmed_records):
												_lowerCAmelCase    :    str										=      [data_url[0]] * len(_UpperCAmelCase      )
								for single_url in data_url:
												for download_callback in self.download_callbacks:
																download_callback(_UpperCAmelCase      )
												# we force the name of each key to be the last file / folder name of the url path
												# if the url has arguments, we need to encode them with urllib.parse.quote_plus
												_lowerCAmelCase    :    Tuple										=      os.path.join(_UpperCAmelCase		,	urllib.parse.quote_plus(single_url.split("""/"""      )[-1]      )      )
												dummy_data_list.append(_UpperCAmelCase      )
								return dummy_data_list



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								for download_callback in self.download_callbacks:
												download_callback(_UpperCAmelCase      )
								# we force the name of each key to be the last file / folder name of the url path
								# if the url has arguments, we need to encode them with urllib.parse.quote_plus
								_lowerCAmelCase    :    Union[str, Any]										=      os.path.join(_UpperCAmelCase		,	urllib.parse.quote_plus(data_url.split("""/"""      )[-1]      )      )
								if os.path.exists(_UpperCAmelCase      ) or not self.load_existing_dummy_data:
												return value
								else:
												# Backward compatibility, maybe deprecate at one point.
												# For many datasets with single url calls to dl_manager.download_and_extract,
												# the dummy_data.zip file is actually the zipped downloaded file
												# while now we expected the dummy_data.zip file to be a directory containing
												# the downloaded file.
												return path_to_dummy_data



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Optional[int]      )  ->    List[str]:





								'''simple docstring'''




								def _iter_archive_members(_UpperCAmelCase      :		Optional[int]      ):
												# this preserves the order of the members inside the ZIP archive
												_lowerCAmelCase    :    List[str]										=      Path(self.dummy_file      ).parent
												_lowerCAmelCase    :    Tuple										=      path.relative_to(_UpperCAmelCase      )
												with ZipFile(self.local_path_to_dummy_data      ) as zip_file:
																_lowerCAmelCase    :    str										=      zip_file.namelist()
												for member in members:
																if member.startswith(relative_path.as_posix()      ):
																				yield dummy_parent_path.joinpath(_UpperCAmelCase      )

								_lowerCAmelCase    :    int										=      Path(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      _iter_archive_members(_UpperCAmelCase      ) if self.use_local_dummy_data else path.rglob("""*"""      )
								for file_path in file_paths:
												if file_path.is_file() and not file_path.name.startswith((""".""", """__""")      ):
																yield file_path.relative_to(_UpperCAmelCase      ).as_posix(), file_path.open("""rb"""      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		Dict      )  ->    Tuple:





								'''simple docstring'''




								if not isinstance(_UpperCAmelCase		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    str										=      [paths]
								for path in paths:
												if os.path.isfile(_UpperCAmelCase      ):
																if os.path.basename(_UpperCAmelCase      ).startswith((""".""", """__""")      ):
																				return
																yield path
												else:
																for dirpath, dirnames, filenames in os.walk(_UpperCAmelCase      ):
																				if os.path.basename(_UpperCAmelCase      ).startswith((""".""", """__""")      ):
																								continue
																				dirnames.sort()
																				for filename in sorted(_UpperCAmelCase      ):
																								if filename.startswith((""".""", """__""")      ):
																												continue
																								yield os.path.join(_UpperCAmelCase		,	_UpperCAmelCase      )







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFResNetForImageClassification, TFResNetModel
	from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor
class        __snake_case  :



				def __init__(      self      :		str		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[str]=3		,	_UpperCAmelCase      :		List[str]=32		,	_UpperCAmelCase      :		str=3		,	_UpperCAmelCase      :		Tuple=10		,	_UpperCAmelCase      :		Optional[Any]=[10, 20, 30, 40]		,	_UpperCAmelCase      :		int=[1, 1, 2, 1]		,	_UpperCAmelCase      :		str=True		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		str="relu"		,	_UpperCAmelCase      :		int=3		,	_UpperCAmelCase      :		int=None		,	)  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      parent
								_lowerCAmelCase    :    int										=      batch_size
								_lowerCAmelCase    :    List[str]										=      image_size
								_lowerCAmelCase    :    Optional[int]										=      num_channels
								_lowerCAmelCase    :    List[str]										=      embeddings_size
								_lowerCAmelCase    :    int										=      hidden_sizes
								_lowerCAmelCase    :    str										=      depths
								_lowerCAmelCase    :    int										=      is_training
								_lowerCAmelCase    :    Dict										=      use_labels
								_lowerCAmelCase    :    Dict										=      hidden_act
								_lowerCAmelCase    :    Dict										=      num_labels
								_lowerCAmelCase    :    List[Any]										=      scope
								_lowerCAmelCase    :    Optional[int]										=      len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								_lowerCAmelCase    :    Tuple										=      None
								if self.use_labels:
												_lowerCAmelCase    :    Optional[int]										=      ids_tensor([self.batch_size]		,	self.num_labels      )

								_lowerCAmelCase    :    Dict										=      self.get_config()

								return config, pixel_values, labels



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    int:





								'''simple docstring'''




								return ResNetConfig(
								    num_channels=self.num_channels		,	embeddings_size=self.embeddings_size		,	hidden_sizes=self.hidden_sizes		,	depths=self.depths		,	hidden_act=self.hidden_act		,	num_labels=self.num_labels		,	image_size=self.image_size		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      TFResNetModel(config=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      model(_UpperCAmelCase      )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Optional[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.num_labels
								_lowerCAmelCase    :    Any										=      TFResNetForImageClassification(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      model(_UpperCAmelCase		,	labels=_UpperCAmelCase      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.prepare_config_and_inputs()
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      config_and_inputs
								_lowerCAmelCase    :    Tuple										=      {"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_tf
class        __snake_case  (_a						,	_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
				lowerCAmelCase__				=     (
				    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False
				lowerCAmelCase__				=     False



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      TFResNetModelTester(self      )
								_lowerCAmelCase    :    Union[str, Any]										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	has_text_modality=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''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       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Union[str, Any]:





								'''simple docstring'''




								return



				@unittest.skip(reason="""ResNet does not use inputs_embeds"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""ResNet does not support input and output embeddings"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Union[str, Any]:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.model_tester.prepare_config_and_inputs_for_common()

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

												_lowerCAmelCase    :    Optional[Any]										=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								def check_hidden_states_output(_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Tuple      ):
												_lowerCAmelCase    :    Dict										=      model_class(_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[int]										=      model(**self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )      )

												_lowerCAmelCase    :    Optional[Any]										=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												_lowerCAmelCase    :    Dict										=      self.model_tester.num_stages
												self.assertEqual(len(_UpperCAmelCase      )		,	expected_num_stages + 1      )

												# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]      )		,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,	)

								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCAmelCase    :    str										=      ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
												for layer_type in layers_type:
																_lowerCAmelCase    :    Tuple										=      layer_type
																_lowerCAmelCase    :    Any										=      True
																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_lowerCAmelCase    :    Any										=      True

																check_hidden_states_output(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase    :    Dict										=      TFResNetModel.from_pretrained(_UpperCAmelCase      )
												self.assertIsNotNone(_UpperCAmelCase      )






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
				return image





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



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
								    if is_vision_available()
								    else None
								)



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

								_lowerCAmelCase    :    Optional[int]										=      self.default_image_processor
								_lowerCAmelCase    :    int										=      prepare_img()
								_lowerCAmelCase    :    int										=      image_processor(images=_UpperCAmelCase		,	return_tensors="""tf"""      )

								# forward pass
								_lowerCAmelCase    :    int										=      model(**_UpperCAmelCase      )

								# verify the logits
								_lowerCAmelCase    :    Union[str, Any]										=      tf.TensorShape((1, 1000)      )
								self.assertEqual(outputs.logits.shape		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    Any										=      tf.constant([-11.1069, -9.7877, -8.3777]      )

								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,	_UpperCAmelCase		,	atol=1E-4      )      )

