




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A						:		Union[str, Any]          =						{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A						:		Optional[int]          =						["LEDTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A						:		Optional[int]          =						[
	    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "LEDForConditionalGeneration",
	    "LEDForQuestionAnswering",
	    "LEDForSequenceClassification",
	    "LEDModel",
	    "LEDPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A						:		Tuple          =						["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
	from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
	from .tokenization_led import LEDTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_led_fast import LEDTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_led import (
		    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LEDForConditionalGeneration,
		    LEDForQuestionAnswering,
		    LEDForSequenceClassification,
		    LEDModel,
		    LEDPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
	import sys

	__A						:		List[Any]          =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''


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        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=3     ,      _snake_case=32     ,      _snake_case=3     ,      _snake_case=10     ,      _snake_case=[10, 20, 30, 40]     ,      _snake_case=[1, 1, 2, 1]     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case="relu"     ,      _snake_case=3     ,      _snake_case=None     ,      )				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       parent
								UpperCAmelCase_		:   Union[str, Any]													=       batch_size
								UpperCAmelCase_		:   List[Any]													=       image_size
								UpperCAmelCase_		:   List[Any]													=       num_channels
								UpperCAmelCase_		:   Dict													=       embeddings_size
								UpperCAmelCase_		:   str													=       hidden_sizes
								UpperCAmelCase_		:   List[Any]													=       depths
								UpperCAmelCase_		:   Optional[Any]													=       is_training
								UpperCAmelCase_		:   str													=       use_labels
								UpperCAmelCase_		:   List[str]													=       hidden_act
								UpperCAmelCase_		:   Dict													=       num_labels
								UpperCAmelCase_		:   List[Any]													=       scope
								UpperCAmelCase_		:   Tuple													=       len(_snake_case)


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   int													=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:   Optional[int]													=       self.get_config()

								return config, pixel_values


							def     _snake_case				(      self)				->					Union[str, 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     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					str:
								UpperCAmelCase_		:   Any													=       FlaxResNetModel(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Union[str, Any]													=       self.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       FlaxResNetForImageClassification(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels))





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.prepare_config_and_inputs()
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       config_and_inputs
								UpperCAmelCase_		:   str													=       {'pixel_values': pixel_values}
								return config, inputs_dict



@require_flax
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Dict= (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							_lowerCamelCase		:   Any= False
							_lowerCamelCase		:   List[Any]= False
							_lowerCamelCase		:   str= False


							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Any													=       FlaxResNetModelTester(self)
								UpperCAmelCase_		:   Optional[int]													=       ConfigTester(self     ,      config_class=_snake_case     ,      has_text_modality=_snake_case)


							def     _snake_case				(      self)				->					Tuple:
								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     _snake_case				(      self)				->					int:
								return


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Optional[Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   Union[str, Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case)


							@unittest.skip(reason='ResNet does not use inputs_embeds')
							def     _snake_case				(      self)				->					Optional[Any]:
								pass


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


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCAmelCase_		:   List[str]													=       ['pixel_values']
									self.assertListEqual(arg_names[:1]     ,      _snake_case)


							def     _snake_case				(      self)				->					List[Any]:
								def check_hidden_states_output(_snake_case     ,      _snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Union[str, Any]													=       model_class(_snake_case)

									UpperCAmelCase_		:   Any													=       model(**self._prepare_for_class(_snake_case     ,      _snake_case))

									UpperCAmelCase_		:   List[Any]													=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_		:   int													=       self.model_tester.num_stages
									self.assertEqual(len(_snake_case)     ,      expected_num_stages + 1)


							@unittest.skip(reason='ResNet does not use feedforward chunking')
							def     _snake_case				(      self)				->					List[Any]:
								pass





							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       self.model_tester.prepare_config_and_inputs_for_common()

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

										@jax.jit
										def model_jitted(_snake_case     ,      **_snake_case):
											return model(pixel_values=_snake_case     ,      **_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:   Optional[Any]													=       model_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:   Union[str, Any]													=       model_jitted(**_snake_case).to_tuple()

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







def     SCREAMING_SNAKE_CASE(	)     ->     Dict:
	UpperCAmelCase_		:   Tuple													=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_flax
class        lowercase    (	unittest.TestCase			):


							@cached_property
							def     _snake_case				(      self)				->					List[str]:
								return AutoImageProcessor.from_pretrained('microsoft/resnet-50') if is_vision_available() else None





							@slow
							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50')

								UpperCAmelCase_		:   List[str]													=       self.default_image_processor
								UpperCAmelCase_		:   Union[str, Any]													=       prepare_img()
								UpperCAmelCase_		:   int													=       image_processor(images=_snake_case     ,      return_tensors='np')

								UpperCAmelCase_		:   Any													=       model(**_snake_case)

								# verify the logits
								UpperCAmelCase_		:   Optional[int]													=       (1, 1000)
								self.assertEqual(outputs.logits.shape     ,      _snake_case)

								UpperCAmelCase_		:   Tuple													=       jnp.array([-11.1_069, -9.7_877, -8.3_777])

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






