
import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor


class 		__magic_name__   (						unittest.TestCase    ):





		def __init__(							self    :	List[Any]						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	Any=3						,  lowerCamelCase__    :	Optional[Any]=3_2						,  lowerCamelCase__    :	List[str]=3						,  lowerCamelCase__    :	Dict=1_0						,  lowerCamelCase__    :	int=[1_0, 2_0, 3_0, 4_0]						,  lowerCamelCase__    :	Dict=[1, 1, 2, 1]						,  lowerCamelCase__    :	Dict=True						,  lowerCamelCase__    :	Any=True						,  lowerCamelCase__    :	Optional[Any]="relu"						,  lowerCamelCase__    :	str=3						,  lowerCamelCase__    :	Optional[Any]=None						,  ):
							lowerCAmelCase		:					List[str]  	=     parent
							lowerCAmelCase		:					List[str]  	=     batch_size
							lowerCAmelCase		:					Any  	=     image_size
							lowerCAmelCase		:					Optional[Any]  	=     num_channels
							lowerCAmelCase		:					int  	=     embeddings_size
							lowerCAmelCase		:					int  	=     hidden_sizes
							lowerCAmelCase		:					Optional[int]  	=     depths
							lowerCAmelCase		:					Tuple  	=     is_training
							lowerCAmelCase		:					Optional[int]  	=     use_labels
							lowerCAmelCase		:					str  	=     hidden_act
							lowerCAmelCase		:					Tuple  	=     num_labels
							lowerCAmelCase		:					Optional[int]  	=     scope
							lowerCAmelCase		:					int  	=     len(lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					str  	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCAmelCase		:					Dict  	=     self.get_config()

							return config, pixel_values





		def       _A							(							self    :	List[Any]				):
							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       _A							(							self    :	List[Any]						,  lowerCamelCase__    :	int						,  lowerCamelCase__    :	Optional[Any]				):
							lowerCAmelCase		:					str  	=     FlaxResNetModel(config=lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     model(lowerCamelCase__				)

							# Output shape (b, c, h, w)
							self.parent.assertEqual(
							    result.last_hidden_state.shape						,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)						,  )





		def       _A							(							self    :	List[Any]						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Union[str, Any]				):
							lowerCAmelCase		:					str  	=     self.num_labels
							lowerCAmelCase		:					int  	=     FlaxResNetForImageClassification(config=lowerCamelCase__				)
							lowerCAmelCase		:					int  	=     model(lowerCamelCase__				)
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)				)





		def       _A							(							self    :	Any				):
							lowerCAmelCase		:					Dict  	=     self.prepare_config_and_inputs()
							lowerCAmelCase	,							lowerCAmelCase		:					str  	=     config_and_inputs
							lowerCAmelCase		:					List[str]  	=     {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_flax
class 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Tuple  	=     FlaxResNetModelTester(self				)
							lowerCAmelCase		:					Optional[int]  	=     ConfigTester(self						,  config_class=lowerCamelCase__						,  has_text_modality=lowerCamelCase__				)





		def       _A							(							self    :	Union[str, Any]				):
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()





		def       _A							(							self    :	List[Any]				):
							return





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Any  	=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					str  	=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__				)





		@unittest.skip(reason='''ResNet does not use inputs_embeds'''				)
		def       _A							(							self    :	Optional[int]				):
							pass





		@unittest.skip(reason='''ResNet does not support input and output embeddings'''				)
		def       _A							(							self    :	int				):
							pass





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase	,							lowerCAmelCase		:					List[str]  	=     self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCAmelCase		:					Tuple  	=     ['''pixel_values''']
												self.assertListEqual(arg_names[:1]						,  lowerCamelCase__				)





		def       _A							(							self    :	str				):
							def check_hidden_states_output(lowerCamelCase__    :	List[Any]						,  lowerCamelCase__    :	int						,  lowerCamelCase__    :	Any				):
												lowerCAmelCase		:					str  	=     model_class(lowerCamelCase__				)

												lowerCAmelCase		:					Union[str, Any]  	=     model(**self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)				)

												lowerCAmelCase		:					Union[str, Any]  	=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowerCAmelCase		:					int  	=     self.model_tester.num_stages
												self.assertEqual(len(lowerCamelCase__				)						,  expected_num_stages + 1				)





		@unittest.skip(reason='''ResNet does not use feedforward chunking'''				)
		def       _A							(							self    :	Optional[int]				):
							pass





		def       _A							(							self    :	Dict				):
							lowerCAmelCase	,							lowerCAmelCase		:					List[Any]  	=     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__				):
																	lowerCAmelCase		:					Dict  	=     self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)
																	lowerCAmelCase		:					int  	=     model_class(lowerCamelCase__				)

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

																	with self.subTest('''JIT Enabled'''				):
																						lowerCAmelCase		:					Any  	=     model_jitted(**lowerCamelCase__				).to_tuple()

																	with self.subTest('''JIT Disabled'''				):
																						with jax.disable_jit():
																											lowerCAmelCase		:					Dict  	=     model_jitted(**lowerCamelCase__				).to_tuple()

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



def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					str  	=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
					return image


@require_flax
class 		__magic_name__   (						unittest.TestCase    ):





		@cached_property
		def       _A							(							self    :	Dict				):
							return AutoImageProcessor.from_pretrained('''microsoft/resnet-50'''				) if is_vision_available() else None





		@slow
		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					List[Any]  	=     FlaxResNetForImageClassification.from_pretrained('''microsoft/resnet-50'''				)

							lowerCAmelCase		:					List[str]  	=     self.default_image_processor
							lowerCAmelCase		:					Optional[Any]  	=     prepare_img()
							lowerCAmelCase		:					Dict  	=     image_processor(images=lowerCamelCase__						,  return_tensors='''np'''				)

							lowerCAmelCase		:					Dict  	=     model(**lowerCamelCase__				)

							# verify the logits
							lowerCAmelCase		:					List[str]  	=     (1, 1_0_0_0)
							self.assertEqual(outputs.logits.shape						,  lowerCamelCase__				)

							lowerCAmelCase		:					Tuple  	=     jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]				)

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


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
