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





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

				lowerCAmelCase		:					Optional[Any]  	=     self.get_config()

				return config, pixel_values





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





		def       _A							(							self    :	int				):
				lowerCAmelCase		:					Tuple  	=     self.prepare_config_and_inputs()
				lowerCAmelCase		:					Optional[Any]  	=     config_and_inputs
				lowerCAmelCase		:					int  	=     {'pixel_values': pixel_values}
				return config, inputs_dict



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

		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False





		def       _A							(							self    :	Optional[int]				):
				lowerCAmelCase		:					Dict  	=     FlaxResNetModelTester(self				)
				lowerCAmelCase		:					List[Any]  	=     ConfigTester(self						,  config_class=lowerCamelCase__						,  has_text_modality=lowerCamelCase__				)





		def       _A							(							self    :	Optional[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    :	Dict				):
				return





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





		def       _A							(							self    :	Optional[Any]				):
				lowerCAmelCase		:					Optional[int]  	=     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    :	Dict				):
				pass





		@unittest.skip(reason='''ResNet does not support input and output embeddings'''				)
		def       _A							(							self    :	Union[str, Any]				):
				pass





		def       _A							(							self    :	str				):
				lowerCAmelCase		:					Dict  	=     self.model_tester.prepare_config_and_inputs_for_common()

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

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





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

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

						lowerCAmelCase		:					Tuple  	=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowerCAmelCase		:					str  	=     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    :	int				):
				pass





		def       _A							(							self    :	Union[str, Any]				):
				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		:					Any  	=     self._prepare_for_class(lowerCamelCase__						,  lowerCamelCase__				)
								lowerCAmelCase		:					List[str]  	=     model_class(lowerCamelCase__				)

								@jax.jit
								def model_jitted(lowerCamelCase__    :	int						,  **lowerCamelCase__    :	str				):
										return model(pixel_values=lowerCamelCase__						,  **lowerCamelCase__				)

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

								with self.subTest('''JIT Disabled'''				):
										with jax.disable_jit():
												lowerCAmelCase		:					Optional[int]  	=     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		:					Any  	=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
		return image


@require_flax
class 		__magic_name__   (						unittest.TestCase    ):





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





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

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

				lowerCAmelCase		:					str  	=     model(**lowerCamelCase__				)

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

				lowerCAmelCase		:					Dict  	=     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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__SCREAMING_SNAKE_CASE			:   str         =     {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['DetaImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   str         =     [
		    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DetaForObjectDetection',
		    'DetaModel',
		    'DetaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Dict         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
