


"""simple docstring"""


from ...processing_utils import ProcessorMixin


class       A__						(    lowercase_):


     """simple docstring"""
     snake_case__						:       Optional[Any]				    ='''SpeechT5FeatureExtractor'''
     snake_case__						:       List[str]				    ='''SpeechT5Tokenizer'''

     def __init__(						self:    Tuple  ,		__a:    Optional[int]  ,		__a:    List[str]						)->     Tuple:
           super().__init__(lowerCamelCase_  ,		lowerCamelCase_						)

     def __call__(						self:    Optional[Any]  ,		*__a:    Optional[Any]  ,		**__a:    Optional[Any]						)->     Union[str, Any]:
           lowerCamelCase       :       Optional[Any]   =						kwargs.pop("""audio"""  ,		lowerCamelCase_						)
           lowerCamelCase       :       int   =						kwargs.pop("""text"""  ,		lowerCamelCase_						)
           lowerCamelCase       :       Tuple   =						kwargs.pop("""text_target"""  ,		lowerCamelCase_						)
           lowerCamelCase       :       Dict   =						kwargs.pop("""audio_target"""  ,		lowerCamelCase_						)
           lowerCamelCase       :       Union[str, 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       :       Optional[Any]   =						self.feature_extractor(lowerCamelCase_  ,		*lowerCamelCase_  ,		sampling_rate=lowerCamelCase_  ,		**lowerCamelCase_						)
           elif text is not None:
                 lowerCamelCase       :       Dict   =						self.tokenizer(lowerCamelCase_  ,		**lowerCamelCase_						)
           else:
                 lowerCamelCase       :       List[str]   =						None

           if audio_target is not None:
                 lowerCamelCase       :       Optional[Any]   =						self.feature_extractor(audio_target=lowerCamelCase_  ,		*lowerCamelCase_  ,		sampling_rate=lowerCamelCase_  ,		**lowerCamelCase_						)
                 lowerCamelCase       :       Tuple   =						targets["""input_values"""]
           elif text_target is not None:
                 lowerCamelCase       :       Any   =						self.tokenizer(lowerCamelCase_  ,		**lowerCamelCase_						)
                 lowerCamelCase       :       List[str]   =						targets["""input_ids"""]
           else:
                 lowerCamelCase       :       List[str]   =						None

           if inputs is None:
                 return targets

           if targets is not None:
                 lowerCamelCase       :       Tuple   =						labels

                 lowerCamelCase       :       Optional[Any]   =						targets.get("""attention_mask"""						)
                 if decoder_attention_mask is not None:
                       lowerCamelCase       :       Union[str, Any]   =						decoder_attention_mask

           return inputs

     def 		a__    (						self:    str  ,		*__a:    Dict  ,		**__a:    Optional[Any]						)->     int:
           lowerCamelCase       :       List[str]   =						kwargs.pop("""input_values"""  ,		lowerCamelCase_						)
           lowerCamelCase       :       List[Any]   =						kwargs.pop("""input_ids"""  ,		lowerCamelCase_						)
           lowerCamelCase       :       List[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       :       Optional[Any]   =						self.feature_extractor.pad(lowerCamelCase_  ,		*lowerCamelCase_  ,		**lowerCamelCase_						)
           elif input_ids is not None:
                 lowerCamelCase       :       Optional[int]   =						self.tokenizer.pad(lowerCamelCase_  ,		**lowerCamelCase_						)
           else:
                 lowerCamelCase       :       str   =						None

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

           if inputs is None:
                 return targets

           if targets is not None:
                 lowerCamelCase       :       Tuple   =						labels

                 lowerCamelCase       :       Tuple   =						targets.get("""attention_mask"""						)
                 if decoder_attention_mask is not None:
                       lowerCamelCase       :       Union[str, Any]   =						decoder_attention_mask

           return inputs

     def 		a__    (						self:    Optional[Any]  ,		*__a:    Any  ,		**__a:    str						)->     Any:
           return self.tokenizer.batch_decode(*lowerCamelCase_  ,		**lowerCamelCase_						)







     def 		a__    (						self:    int  ,		*__a:    Optional[int]  ,		**__a:    int						)->     List[str]:
           return self.tokenizer.decode(*lowerCamelCase_  ,		**lowerCamelCase_						)











'''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()



