




import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):  # picklable for multiprocessing
				'''simple docstring'''
				return x.sum()




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):  # picklable for multiprocessing
				'''simple docstring'''
				return i + 1
@dataclass
class        __snake_case  :
				lowerCAmelCase__				=     42
				lowerCAmelCase__				=     42






class        __snake_case  (a__						):



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {}
								_lowerCAmelCase    :    List[Any]										=      []
								_lowerCAmelCase    :    int										=      1
								_lowerCAmelCase    :    List[str]										=      [1, 2]
								_lowerCAmelCase    :    List[Any]										=      {"""a""": 1, """b""": 2}
								_lowerCAmelCase    :    List[str]										=      {"""a""": [1, 2], """b""": [3, 4]}
								_lowerCAmelCase    :    Optional[int]										=      {"""a""": {"""1""": 1}, """b""": 2}
								_lowerCAmelCase    :    Tuple										=      {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
								_lowerCAmelCase    :    List[Any]										=      {}
								_lowerCAmelCase    :    str										=      []
								_lowerCAmelCase    :    List[Any]										=      2
								_lowerCAmelCase    :    Tuple										=      [2, 3]
								_lowerCAmelCase    :    Optional[int]										=      {"""a""": 2, """b""": 3}
								_lowerCAmelCase    :    Optional[int]										=      {"""a""": [2, 3], """b""": [4, 5]}
								_lowerCAmelCase    :    str										=      {"""a""": {"""1""": 2}, """b""": 3}
								_lowerCAmelCase    :    str										=      {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_      )		,	lowerCamelCase_      )

								_lowerCAmelCase    :    Dict										=      2
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )

								_lowerCAmelCase    :    Tuple										=      {"""a""": np.eye(2      ), """b""": np.zeros(3      ), """c""": np.ones(2      )}
								_lowerCAmelCase    :    Tuple										=      {"""a""": 2, """b""": 0, """c""": 2}
								_lowerCAmelCase    :    str										=      {
								    """a""": np.eye(2      ).astype(lowerCamelCase_      ),
								    """b""": np.zeros(3      ).astype(lowerCamelCase_      ),
								    """c""": np.ones(2      ).astype(lowerCamelCase_      ),
								}
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	map_numpy=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(
								    {k: v.tolist() for k, v in map_nested(lowerCamelCase_		,	lowerCamelCase_		,	map_numpy=lowerCamelCase_      ).items()}		,	{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,	)
								self.assertEqual(map_nested(lowerCamelCase_		,	lowerCamelCase_		,	map_numpy=lowerCamelCase_		,	num_proc=lowerCamelCase_      )		,	lowerCamelCase_      )
								self.assertEqual(
								    {k: v.tolist() for k, v in map_nested(lowerCamelCase_		,	lowerCamelCase_		,	map_numpy=lowerCamelCase_		,	num_proc=lowerCamelCase_      ).items()}		,	{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}		,	)
								with self.assertRaises(lowerCamelCase_      ):  # can't pickle a local lambda
												map_nested(lambda _UpperCAmelCase      : x + 1		,	lowerCamelCase_		,	num_proc=lowerCamelCase_      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      {"""a""": 1, """b""": 2}
								_lowerCAmelCase    :    List[Any]										=      {"""a""": 3, """b""": 4}
								_lowerCAmelCase    :    int										=      {"""a""": 5, """b""": 6}
								_lowerCAmelCase    :    Optional[int]										=      sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]      )
								self.assertEqual(sorted(zip_dict(lowerCamelCase_		,	lowerCamelCase_		,	lowerCamelCase_      )      )		,	lowerCamelCase_      )



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





								'''simple docstring'''




								class        __snake_case  :
												lowerCAmelCase__				=     "bar"






								_lowerCAmelCase    :    Union[str, Any]										=      Foo()
								self.assertEqual(foo.my_attr		,	"""bar"""      )
								with temporary_assignment(lowerCamelCase_		,	"""my_attr"""		,	"""BAR"""      ):
												self.assertEqual(foo.my_attr		,	"""BAR"""      )
								self.assertEqual(foo.my_attr		,	"""bar"""      )






@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""      ,    [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]      ,    )
def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				with patch("""datasets.utils.py_utils._single_map_nested"""       ) as mock_single_map_nested, patch(
				    """datasets.parallel.parallel.Pool"""       ) as mock_multiprocessing_pool:
								_lowerCAmelCase    :    Optional[Any]										=      {F"{i}": i for i in range(UpperCamelCase_       )}
								_lowerCAmelCase    :    Dict										=      map_nested(lambda UpperCamelCase_       : x + 10      ,    UpperCamelCase_      ,    num_proc=UpperCamelCase_      ,    parallel_min_length=16       )
								if expected_num_proc == 1:
												assert mock_single_map_nested.called
												assert not mock_multiprocessing_pool.called
								else:
												assert not mock_single_map_nested.called
												assert mock_multiprocessing_pool.called
												assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class        __snake_case  (a__						):



				@require_tf
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								import tensorflow as tf
								from tensorflow.keras import layers

								_lowerCAmelCase    :    int										=      layers.Dense(2      )

								def gen_random_output():
												_lowerCAmelCase    :    Any										=      tf.random.uniform((1, 3)      )
												return model(lowerCamelCase_      ).numpy()

								with temp_seed(42		,	set_tensorflow=lowerCamelCase_      ):
												_lowerCAmelCase    :    Union[str, Any]										=      gen_random_output()
								with temp_seed(42		,	set_tensorflow=lowerCamelCase_      ):
												_lowerCAmelCase    :    Any										=      gen_random_output()
								_lowerCAmelCase    :    Dict										=      gen_random_output()

								np.testing.assert_equal(lowerCamelCase_		,	lowerCamelCase_      )
								self.assertGreater(np.abs(outa - outa      ).sum()		,	0      )



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





								'''simple docstring'''




								import torch

								def gen_random_output():
												_lowerCAmelCase    :    Optional[int]										=      torch.nn.Linear(3		,	2      )
												_lowerCAmelCase    :    Union[str, Any]										=      torch.rand(1		,	3      )
												return model(lowerCamelCase_      ).detach().numpy()

								with temp_seed(42		,	set_pytorch=lowerCamelCase_      ):
												_lowerCAmelCase    :    Any										=      gen_random_output()
								with temp_seed(42		,	set_pytorch=lowerCamelCase_      ):
												_lowerCAmelCase    :    Tuple										=      gen_random_output()
								_lowerCAmelCase    :    Dict										=      gen_random_output()

								np.testing.assert_equal(lowerCamelCase_		,	lowerCamelCase_      )
								self.assertGreater(np.abs(outa - outa      ).sum()		,	0      )



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





								'''simple docstring'''




								def gen_random_output():
												return np.random.rand(1		,	3      )

								with temp_seed(42      ):
												_lowerCAmelCase    :    Union[str, Any]										=      gen_random_output()
								with temp_seed(42      ):
												_lowerCAmelCase    :    int										=      gen_random_output()
								_lowerCAmelCase    :    Optional[int]										=      gen_random_output()

								np.testing.assert_equal(lowerCamelCase_		,	lowerCamelCase_      )
								self.assertGreater(np.abs(outa - outa      ).sum()		,	0      )






@pytest.mark.parametrize("""input_data"""      ,    [{}]       )
def 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      NestedDataStructure(UpperCamelCase_       ).data
				assert output_data == input_data




@pytest.mark.parametrize(
    """data, expected_output"""      ,    [
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]      ,    )
def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Tuple										=      NestedDataStructure(UpperCamelCase_       ).flatten()
				assert output == expected_output




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      A(x=1      ,    y="""foobar"""       )
				_lowerCAmelCase    :    List[Any]										=      {"""x""": 1, """y""": """foobar"""}
				assert asdict(UpperCamelCase_       ) == expected_output

				_lowerCAmelCase    :    Tuple										=      {"""a""": {"""b""": A(x=10      ,    y="""foo"""       )}, """c""": [A(x=20      ,    y="""bar"""       )]}
				_lowerCAmelCase    :    Dict										=      {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
				assert asdict(UpperCamelCase_       ) == expected_output

				with pytest.raises(UpperCamelCase_       ):
								asdict([1, A(x=10      ,    y="""foo"""       )]       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				return text.split()




def 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]       ):
				'''simple docstring'''
				yield (time.time(), content)
				time.sleep(2       )
				yield (time.time(), content)





def 							_UpperCAmelCase  ():
				'''simple docstring'''
				with Pool(2       ) as pool:
								_lowerCAmelCase    :    List[str]										=      list(iflatmap_unordered(UpperCamelCase_      ,    _split_text      ,    kwargs_iterable=[{"""text""": """hello there"""}] * 10       )       )
								assert out.count("""hello"""       ) == 10
								assert out.count("""there"""       ) == 10
								assert len(UpperCamelCase_       ) == 20

				# check multiprocess from pathos (uses dill for pickling)
				with multiprocess.Pool(2       ) as pool:
								_lowerCAmelCase    :    str										=      list(iflatmap_unordered(UpperCamelCase_      ,    _split_text      ,    kwargs_iterable=[{"""text""": """hello there"""}] * 10       )       )
								assert out.count("""hello"""       ) == 10
								assert out.count("""there"""       ) == 10
								assert len(UpperCamelCase_       ) == 20

				# check that we get items as fast as possible
				with Pool(2       ) as pool:
								_lowerCAmelCase    :    Optional[Any]										=      []
								for yield_time, content in iflatmap_unordered(
								    UpperCamelCase_      ,    _aseconds_generator_of_aitems_with_timing      ,    kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]       ):
												assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
												out.append(UpperCamelCase_       )
								assert out.count("""a"""       ) == 2
								assert out.count("""b"""       ) == 2
								assert len(UpperCamelCase_       ) == 4







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      )      )

