



"""simple docstring"""






import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

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



					def 		snake_case	(	self							:  str					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =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							:  Any					)-> int:
								return RegNetConfig(
								    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							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  int					)-> int:
								lowerCamelCase__		:		List[str]		     =FlaxRegNetModel(config=lowerCAmelCase__					)
								lowerCamelCase__		:		List[str]		     =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[str],  lowerCamelCase							:  Any,  lowerCamelCase							:  Tuple					)-> Union[str, Any]:
								lowerCamelCase__		:		Dict		     =self.num_labels
								lowerCamelCase__		:		int		     =FlaxRegNetForImageClassification(config=lowerCAmelCase__					)
								lowerCamelCase__		:		List[str]		     =model(lowerCAmelCase__					)
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)					)




					def 		snake_case	(	self							:  int					)-> int:
								lowerCamelCase__		:		int		     =self.prepare_config_and_inputs()
								lowerCamelCase__		:		Dict		     =config_and_inputs
								lowerCamelCase__		:		str		     ={"pixel_values": pixel_values}
								return config, inputs_dict







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


					'''simple docstring'''





					_a             =							(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

					_a             =							False
					_a             =							False
					_a             =							False



					def 		snake_case	(	self							:  Tuple					)-> None:
								lowerCamelCase__		:		int		     =FlaxRegNetModelTester(self					)
								lowerCamelCase__		:		str		     =ConfigTester(self,  config_class=lowerCAmelCase__,  has_text_modality=lowerCAmelCase__					)



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



					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__					)



					def 		snake_case	(	self							:  Dict					)-> List[Any]:
								lowerCamelCase__		:		str		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__					)



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



					@unittest.skip(reason='''RegNet does not support input and output embeddings'''					)
					def 		snake_case	(	self							:  Tuple					)-> Any:
								pass



					def 		snake_case	(	self							:  Any					)-> int:
								lowerCamelCase__		:		Optional[Any]		     =self.model_tester.prepare_config_and_inputs_for_common()

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

											lowerCamelCase__		:		Optional[int]		     =["pixel_values"]
											self.assertListEqual(arg_names[:1],  lowerCAmelCase__					)



					def 		snake_case	(	self							:  int					)-> Dict:
								def check_hidden_states_output(lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Any					):
											lowerCamelCase__		:		Optional[Any]		     =model_class(lowerCAmelCase__					)

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

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

											lowerCamelCase__		:		List[Any]		     =self.model_tester.num_stages
											self.assertEqual(len(lowerCAmelCase__					),  expected_num_stages + 1					)

								lowerCamelCase__		:		int		     =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__		:		int		     =True
											check_hidden_states_output(lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__					)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											lowerCamelCase__		:		Any		     =True

											check_hidden_states_output(lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__					)




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

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

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

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

														with self.subTest('''JIT Disabled'''					):
																	with jax.disable_jit():
																				lowerCamelCase__		:		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 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =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							:  Optional[Any]					)-> List[str]:
								return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''					) if is_vision_available() else None




					@slow
					def 		snake_case	(	self							:  Optional[int]					)-> Tuple:
								lowerCamelCase__		:		str		     =FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''					)

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

								lowerCamelCase__		:		Optional[int]		     =model(**lowerCAmelCase__					)

								# verify the logits
								lowerCamelCase__		:		List[str]		     =(1, 1000)
								self.assertEqual(outputs.logits.shape,  lowerCAmelCase__					)

								lowerCamelCase__		:		str		     =jnp.array([-0.4_180, -1.5_051, -3.4_836]					)

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









"""simple docstring"""






from ...processing_utils import ProcessorMixin

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'SpeechT5FeatureExtractor'
					_a             =							'SpeechT5Tokenizer'



					def __init__(	self							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str					)-> Any:
								super().__init__(lowerCamelCase,  lowerCamelCase					)



					def __call__(	self							:  Tuple,  *lowerCamelCase							:  List[str],  **lowerCamelCase							:  Optional[int]					)-> List[str]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''audio''',  lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =kwargs.pop('''text''',  lowerCamelCase					)
								lowerCamelCase__		:		int		     =kwargs.pop('''text_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''audio_target''',  lowerCamelCase					)
								lowerCamelCase__		:		Any		     =kwargs.pop('''sampling_rate''',  lowerCamelCase					)

								if audio is not None and text is not None:
											raise ValueError(
											    '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''					)
								if audio_target is not None and text_target is not None:
											raise ValueError(
											    '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''					)
								if audio is None and audio_target is None and text is None and text_target is None:
											raise ValueError(
											    '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''					)

								if audio is not None:
											lowerCamelCase__		:		Union[str, Any]		     =self.feature_extractor(lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
								elif text is not None:
											lowerCamelCase__		:		List[Any]		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if audio_target is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor(audio_target=lowerCamelCase,  *lowerCamelCase,  sampling_rate=lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								elif text_target is not None:
											lowerCamelCase__		:		Dict		     =self.tokenizer(lowerCamelCase,  **lowerCamelCase					)
											lowerCamelCase__		:		int		     =targets['''input_ids''']
								else:
											lowerCamelCase__		:		List[str]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Dict		     =labels

											lowerCamelCase__		:		Any		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Dict		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  int,  *lowerCamelCase							:  Optional[Any],  **lowerCamelCase							:  Optional[int]					)-> Optional[Any]:
								lowerCamelCase__		:		List[Any]		     =kwargs.pop('''input_values''',  lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =kwargs.pop('''input_ids''',  lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =kwargs.pop('''labels''',  lowerCamelCase					)

								if input_values is not None and input_ids is not None:
											raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''					)
								if input_values is None and input_ids is None and labels is None:
											raise ValueError(
											    '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''					)

								if input_values is not None:
											lowerCamelCase__		:		List[str]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
								elif input_ids is not None:
											lowerCamelCase__		:		Tuple		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
								else:
											lowerCamelCase__		:		Any		     =None

								if labels is not None:
											if "input_ids" in labels or (isinstance(lowerCamelCase,  lowerCamelCase					) and "input_ids" in labels[0]):
														lowerCamelCase__		:		str		     =self.tokenizer.pad(lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =targets['''input_ids''']
											else:
														lowerCamelCase__		:		Any		     =self.feature_extractor.feature_size
														lowerCamelCase__		:		Optional[Any]		     =self.feature_extractor.num_mel_bins
														lowerCamelCase__		:		Optional[int]		     =self.feature_extractor.pad(lowerCamelCase,  *lowerCamelCase,  **lowerCamelCase					)
														lowerCamelCase__		:		List[Any]		     =feature_size_hack
														lowerCamelCase__		:		Tuple		     =targets['''input_values''']
								else:
											lowerCamelCase__		:		Optional[Any]		     =None

								if inputs is None:
											return targets

								if targets is not None:
											lowerCamelCase__		:		Tuple		     =labels

											lowerCamelCase__		:		Optional[int]		     =targets.get('''attention_mask'''					)
											if decoder_attention_mask is not None:
														lowerCamelCase__		:		Optional[Any]		     =decoder_attention_mask

								return inputs



					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  Union[str, Any],  **lowerCamelCase							:  List[Any]					)-> List[Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase					)




					def 		snake_case	(	self							:  List[str],  *lowerCamelCase							:  List[Any],  **lowerCamelCase							:  Tuple					)-> int:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase					)




