




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
	import jax

	from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class        __snake_case  (unittest.TestCase		):



				def __init__(      self      :		Tuple		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[int]=13		,	_UpperCAmelCase      :		Optional[int]=30		,	_UpperCAmelCase      :		Optional[Any]=2		,	_UpperCAmelCase      :		Dict=3		,	_UpperCAmelCase      :		Tuple=True		,	_UpperCAmelCase      :		List[Any]=True		,	_UpperCAmelCase      :		int=32		,	_UpperCAmelCase      :		int=5		,	_UpperCAmelCase      :		Tuple=4		,	_UpperCAmelCase      :		Dict=37		,	_UpperCAmelCase      :		List[str]="gelu"		,	_UpperCAmelCase      :		Dict=0.1		,	_UpperCAmelCase      :		List[str]=0.1		,	_UpperCAmelCase      :		Optional[Any]=10		,	_UpperCAmelCase      :		Union[str, Any]=0.02		,	)  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      parent
								_lowerCAmelCase    :    Any										=      batch_size
								_lowerCAmelCase    :    List[Any]										=      image_size
								_lowerCAmelCase    :    Any										=      patch_size
								_lowerCAmelCase    :    Tuple										=      num_channels
								_lowerCAmelCase    :    str										=      is_training
								_lowerCAmelCase    :    Any										=      use_labels
								_lowerCAmelCase    :    Optional[Any]										=      hidden_size
								_lowerCAmelCase    :    str										=      num_hidden_layers
								_lowerCAmelCase    :    Any										=      num_attention_heads
								_lowerCAmelCase    :    List[str]										=      intermediate_size
								_lowerCAmelCase    :    Any										=      hidden_act
								_lowerCAmelCase    :    Optional[int]										=      hidden_dropout_prob
								_lowerCAmelCase    :    str										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    Union[str, Any]										=      type_sequence_label_size
								_lowerCAmelCase    :    str										=      initializer_range

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								_lowerCAmelCase    :    Tuple										=      (image_size // patch_size) ** 2
								_lowerCAmelCase    :    List[str]										=      num_patches + 1



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





								'''simple docstring'''




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

								_lowerCAmelCase    :    str										=      ViTConfig(
								    image_size=self.image_size		,	patch_size=self.patch_size		,	num_channels=self.num_channels		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	is_decoder=_UpperCAmelCase		,	initializer_range=self.initializer_range		,	)

								return config, pixel_values



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





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      FlaxViTModel(config=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase      )
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								_lowerCAmelCase    :    Dict										=      (self.image_size, self.image_size)
								_lowerCAmelCase    :    List[Any]										=      (self.patch_size, self.patch_size)
								_lowerCAmelCase    :    Union[str, Any]										=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, num_patches + 1, self.hidden_size)      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.type_sequence_label_size
								_lowerCAmelCase    :    Optional[Any]										=      FlaxViTForImageClassification(config=_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      model(_UpperCAmelCase      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)      )

								# test greyscale images
								_lowerCAmelCase    :    Optional[int]										=      1
								_lowerCAmelCase    :    Any										=      FlaxViTForImageClassification(_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[int]										=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
								_lowerCAmelCase    :    Optional[int]										=      model(_UpperCAmelCase      )



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





								'''simple docstring'''




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






@require_flax
class        __snake_case  (_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    None:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      FlaxViTModelTester(self      )
								_lowerCAmelCase    :    Optional[int]										=      ConfigTester(self		,	config_class=_UpperCAmelCase		,	has_text_modality=_UpperCAmelCase		,	hidden_size=37      )



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





								'''simple docstring'''




								self.config_tester.run_common_tests()



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





								'''simple docstring'''




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



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[Any]										=      self.model_tester.prepare_config_and_inputs_for_common()

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

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Tuple:





								'''simple docstring'''




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

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__      ):
																_lowerCAmelCase    :    str										=      self._prepare_for_class(_UpperCAmelCase		,	_UpperCAmelCase      )
																_lowerCAmelCase    :    str										=      model_class(_UpperCAmelCase      )

																@jax.jit
																def model_jitted(_UpperCAmelCase      :		Any		,	**_UpperCAmelCase      :		List[Any]      ):
																				return model(pixel_values=_UpperCAmelCase		,	**_UpperCAmelCase      )

																with self.subTest("""JIT Enabled"""      ):
																				_lowerCAmelCase    :    Dict										=      model_jitted(**_UpperCAmelCase      ).to_tuple()

																with self.subTest("""JIT Disabled"""      ):
																				with jax.disable_jit():
																								_lowerCAmelCase    :    str										=      model_jitted(**_UpperCAmelCase      ).to_tuple()

																self.assertEqual(len(_UpperCAmelCase      )		,	len(_UpperCAmelCase      )      )
																for jitted_output, output in zip(_UpperCAmelCase		,	_UpperCAmelCase      ):
																				self.assertEqual(jitted_output.shape		,	output.shape      )



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





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase    :    int										=      model_class_name.from_pretrained("""google/vit-base-patch16-224"""      )
												_lowerCAmelCase    :    Optional[int]										=      model(np.ones((1, 3, 224, 224)      )      )
												self.assertIsNotNone(_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      )      )

