



"""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 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





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



					def 						snake_case			(			self				:    Dict	)->   str:
								lowerCamelCase__						:						Dict						      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

								lowerCamelCase__						:						Any						      =self.get_config()

								return config, pixel_values



					def 						snake_case			(			self				:    List[Any]	)->   str:
								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				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]	)->   Optional[Any]:
								lowerCamelCase__						:						Tuple						      =FlaxResNetModel(config=lowerCamelCase	)
								lowerCamelCase__						:						Dict						      =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 // 32, self.image_size // 32),    )



					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    List[str],    lowerCamelCase				:    Dict	)->   List[Any]:
								lowerCamelCase__						:						Dict						      =self.num_labels
								lowerCamelCase__						:						Dict						      =FlaxResNetForImageClassification(config=lowerCamelCase	)
								lowerCamelCase__						:						List[str]						      =model(lowerCamelCase	)
								self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)	)




					def 						snake_case			(			self				:    Union[str, Any]	)->   Any:
								lowerCamelCase__						:						int						      =self.prepare_config_and_inputs()
								lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =config_and_inputs
								lowerCamelCase__						:						str						      ={'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_flax
class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

					_a       =				False
					_a       =				False
					_a       =				False



					def 						snake_case			(			self				:    str	)->   None:
								lowerCamelCase__						:						Union[str, Any]						      =FlaxResNetModelTester(self	)
								lowerCamelCase__						:						str						      =ConfigTester(self,    config_class=lowerCamelCase,    has_text_modality=lowerCamelCase	)



					def 						snake_case			(			self				:    Optional[int]	)->   int:
								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				:    List[Any]	)->   Any:
								return



					def 						snake_case			(			self				:    str	)->   List[str]:
								lowerCamelCase__						:						Optional[Any]						      =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase	)



					def 						snake_case			(			self				:    Dict	)->   List[str]:
								lowerCamelCase__						:						Optional[Any]						      =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 						snake_case			(			self				:    Dict	)->   int:
								pass



					@unittest.skip(reason='''ResNet does not support input and output embeddings'''	)
					def 						snake_case			(			self				:    Optional[int]	)->   Optional[Any]:
								pass



					def 						snake_case			(			self				:    List[Any]	)->   Any:
								lowerCamelCase__      ,  lowerCamelCase__						:						str						      =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__						:						Tuple						      =model_class(lowerCamelCase	)
											lowerCamelCase__						:						Tuple						      =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 						snake_case			(			self				:    Optional[int]	)->   List[Any]:
								def check_hidden_states_output(lowerCamelCase				:    int,    lowerCamelCase				:    int,    lowerCamelCase				:    Tuple	):
											lowerCamelCase__						:						Tuple						      =model_class(lowerCamelCase	)

											lowerCamelCase__						:						int						      =model(**self._prepare_for_class(lowerCamelCase,    lowerCamelCase	)	)

											lowerCamelCase__						:						Dict						      =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											lowerCamelCase__						:						Union[str, Any]						      =self.model_tester.num_stages
											self.assertEqual(len(lowerCamelCase	),    expected_num_stages + 1	)



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




					def 						snake_case			(			self				:    Dict	)->   List[str]:
								lowerCamelCase__      ,  lowerCamelCase__						:						int						      =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__	):
														lowerCamelCase__						:						Optional[int]						      =self._prepare_for_class(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Optional[Any]						      =model_class(lowerCamelCase	)

														@jax.jit
														def model_jitted(lowerCamelCase				:    List[str],    **lowerCamelCase				:    Dict	):
																	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 	snake_case__  (						):
			"""simple docstring"""
			lowerCamelCase__						:						List[str]						      =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
			return image

@require_flax
class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





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




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

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

								lowerCamelCase__						:						List[str]						      =model(**lowerCamelCase	)

								# verify the logits
								lowerCamelCase__						:						Optional[Any]						      =(1, 1000)
								self.assertEqual(outputs.logits.shape,    lowerCamelCase	)

								lowerCamelCase__						:						List[str]						      =jnp.array([-11.1_069, -9.7_877, -8.3_777]	)

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









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase					:					Any 		=		{
    "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase					:					Any 		=		[
		    "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "UniSpeechSatForAudioFrameClassification",
		    "UniSpeechSatForCTC",
		    "UniSpeechSatForPreTraining",
		    "UniSpeechSatForSequenceClassification",
		    "UniSpeechSatForXVector",
		    "UniSpeechSatModel",
		    "UniSpeechSatPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_lowercase					:					int 		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




