

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"Wav2Vec2FeatureExtractor"
			SCREAMING_SNAKE_CASE     :   Dict									=				"AutoTokenizer"







			def __init__(     self :				str							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				str							)      ->Dict:
									super().__init__(_UpperCamelCase							,   _UpperCamelCase							)
									snake_case_   =	self.feature_extractor
									snake_case_   =	False







			@classmethod
			def 				snake_case__(     cls :				Union[str, Any]							,   _UpperCamelCase :				str							,   **_UpperCamelCase :				List[str]							)      ->Dict:
									try:
															return super().from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)
									except OSError:
															warnings.warn(
															    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
															    ''' include a `tokenizer_class` attribute is deprecated and will be '''
															    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
															    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
															    '''file to suppress this warning: '''							,   _UpperCamelCase							,   )

															snake_case_   =	WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)
															snake_case_   =	WavaVecaCTCTokenizer.from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)

															return cls(feature_extractor=_UpperCamelCase							,   tokenizer=_UpperCamelCase							)







			def __call__(     self :				Dict							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Dict							)      ->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,   **_UpperCamelCase							)

									if "raw_speech" in kwargs:
															warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
															snake_case_   =	kwargs.pop('''raw_speech'''							)
									else:
															snake_case_   =	kwargs.pop('''audio'''							,   _UpperCamelCase							)
									snake_case_   =	kwargs.pop('''sampling_rate'''							,   _UpperCamelCase							)
									snake_case_   =	kwargs.pop('''text'''							,   _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_   =	args[0]
															snake_case_   =	args[1:]

									if audio is None and text is None:
															raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

									if audio is not None:
															snake_case_   =	self.feature_extractor(_UpperCamelCase							,   *_UpperCamelCase							,   sampling_rate=_UpperCamelCase							,   **_UpperCamelCase							)
									if text is not None:
															snake_case_   =	self.tokenizer(_UpperCamelCase							,   **_UpperCamelCase							)

									if text is None:
															return inputs
									elif audio is None:
															return encodings
									else:
															snake_case_   =	encodings['''input_ids''']
															return inputs







			def 				snake_case__(     self :				Any							,   *_UpperCamelCase :				List[str]							,   **_UpperCamelCase :				Optional[int]							)      ->int:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor.pad(*_UpperCamelCase							,   **_UpperCamelCase							)

									snake_case_   =	kwargs.pop('''input_features'''							,   _UpperCamelCase							)
									snake_case_   =	kwargs.pop('''labels'''							,   _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_   =	args[0]
															snake_case_   =	args[1:]

									if input_features is not None:
															snake_case_   =	self.feature_extractor.pad(_UpperCamelCase							,   *_UpperCamelCase							,   **_UpperCamelCase							)
									if labels is not None:
															snake_case_   =	self.tokenizer.pad(_UpperCamelCase							,   **_UpperCamelCase							)

									if labels is None:
															return input_features
									elif input_features is None:
															return labels
									else:
															snake_case_   =	labels['''input_ids''']
															return input_features







			def 				snake_case__(     self :				Union[str, Any]							,   *_UpperCamelCase :				str							,   **_UpperCamelCase :				Optional[int]							)      ->Optional[int]:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							,   *_UpperCamelCase :				Optional[Any]							,   **_UpperCamelCase :				str							)      ->Optional[Any]:
									return self.tokenizer.decode(*_UpperCamelCase							,   **_UpperCamelCase							)







			@contextmanager
			def 				snake_case__(     self :				str							)      ->List[Any]:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your audio inputs, or in a separate call.'''							)
									snake_case_   =	True
									snake_case_   =	self.tokenizer
									yield
									snake_case_   =	self.feature_extractor
									snake_case_   =	False
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard





@slow
@require_flax
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  super().tearDown()
  gc.collect()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



