





'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class      UpperCamelCase__					:




					"""simple docstring"""


					@property
					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							return self.get_dummy_input()


					@property
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							if self.block_type == "down":
									return (4, 32, 16, 16)
							elif self.block_type == "mid":
									return (4, 32, 32, 32)
							elif self.block_type == "up":
									return (4, 32, 64, 64)

							raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''						)


					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			Dict=True      ,						lowerCamelCase_				:			List[Any]=False      ,						lowerCamelCase_				:			str=False      ,						lowerCamelCase_				:			Optional[int]=False      ,						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			4
							SCREAMING_SNAKE_CASE :				Any		=			32
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			(32, 32)

							SCREAMING_SNAKE_CASE :				int		=			torch.manual_seed(0						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.device(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			(batch_size, num_channels) + sizes
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			randn_tensor(lowerCamelCase_      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{"""hidden_states""": hidden_states}

							if include_temb:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1_28
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			randn_tensor((batch_size, temb_channels)      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)

							if include_res_hidden_states_tuple:
									SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(1						)
									SCREAMING_SNAKE_CASE :				int		=			(randn_tensor(lowerCamelCase_      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						),)

							if include_encoder_hidden_states:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			floats_tensor((batch_size, 32, 32)						).to(lowerCamelCase_						)

							if include_skip_sample:
									SCREAMING_SNAKE_CASE :				str		=			randn_tensor(((batch_size, 3) + sizes)      ,						generator=lowerCamelCase_      ,						device=lowerCamelCase_						)

							return dummy_input


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			{
							    """in_channels""": 32,
							    """out_channels""": 32,
							    """temb_channels""": 1_28,
							}
							if self.block_type == "up":
									SCREAMING_SNAKE_CASE :				List[str]		=			32

							if self.block_type == "mid":
									init_dict.pop("""out_channels"""						)

							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_input
							return init_dict, inputs_dict


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.prepare_init_args_and_inputs_for_common()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.block_class(**lowerCamelCase_						)
							unet_block.to(lowerCamelCase_						)
							unet_block.eval()

							with torch.no_grad():
									SCREAMING_SNAKE_CASE :				List[str]		=			unet_block(**lowerCamelCase_						)

							if isinstance(lowerCamelCase_      ,						lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Dict		=			output[0]

							self.assertEqual(output.shape      ,						self.output_shape						)

							SCREAMING_SNAKE_CASE :				Dict		=			output[0, -1, -3:, -3:]
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.tensor(lowerCamelCase_						).to(lowerCamelCase_						)
							assert torch_all_close(output_slice.flatten()      ,						lowerCamelCase_      ,						atol=5e-3						)



					@unittest.skipIf(torch_device == """mps"""      ,						"""Training is not supported in mps"""						)
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.prepare_init_args_and_inputs_for_common()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.block_class(**lowerCamelCase_						)
							model.to(lowerCamelCase_						)
							model.train()
							SCREAMING_SNAKE_CASE :				Tuple		=			model(**lowerCamelCase_						)

							if isinstance(lowerCamelCase_      ,						lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			output[0]

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.device(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			randn_tensor(output.shape      ,						device=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.nn.functional.mse_loss(lowerCamelCase_      ,						lowerCamelCase_						)
							loss.backward()










'''simple docstring'''




import warnings
from contextlib import contextmanager

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




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							try:
									return super().from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
							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: """      ,						lowerCamelCase_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor(*lowerCamelCase_      ,						**lowerCamelCase_						)

							if "raw_speech" in kwargs:
									warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""						)
									SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""raw_speech"""						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			kwargs.pop("""audio"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			kwargs.pop("""sampling_rate"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			kwargs.pop("""text"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args[0]
									SCREAMING_SNAKE_CASE :				Dict		=			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:
									SCREAMING_SNAKE_CASE :				str		=			self.feature_extractor(lowerCamelCase_      ,						*lowerCamelCase_      ,						sampling_rate=lowerCamelCase_      ,						**lowerCamelCase_						)
							if text is not None:
									SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer(lowerCamelCase_      ,						**lowerCamelCase_						)

							if text is None:
									return inputs
							elif audio is None:
									return encodings
							else:
									SCREAMING_SNAKE_CASE :				List[str]		=			encodings["""input_ids"""]
									return inputs


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor.pad(*lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

							if labels is None:
									return input_features
							elif input_features is None:
									return labels
							else:
									SCREAMING_SNAKE_CASE :				Dict		=			labels["""input_ids"""]
									return input_features


					def lowerCamelCase_			(						self				:			Optional[int]      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							return self.tokenizer.batch_decode(*lowerCamelCase_      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							return self.tokenizer.decode(*lowerCamelCase_      ,						**lowerCamelCase_						)



					@contextmanager
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							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."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



