



"""simple docstring"""






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowercase : List[str]      						=						logging.get_logger(__name__)

_lowercase : Dict      						=						{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							't5'
					_a   			=							['past_key_values']
					_a   			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



					def __init__(       self							:  Tuple,  lowerCamelCase							:  Optional[int]=3_2128,  lowerCamelCase							:  Any=512,  lowerCamelCase							:  Optional[Any]=64,  lowerCamelCase							:  Union[str, Any]=2048,  lowerCamelCase							:  str=6,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Union[str, Any]=8,  lowerCamelCase							:  Optional[int]=32,  lowerCamelCase							:  Union[str, Any]=128,  lowerCamelCase							:  List[Any]=0.1,  lowerCamelCase							:  int=1E-6,  lowerCamelCase							:  Dict=1.0,  lowerCamelCase							:  str="relu",  lowerCamelCase							:  Optional[int]=True,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Any=0,  lowerCamelCase							:  int=1,  **lowerCamelCase							:  Any,  )->					Union[str, Any]:
								lowerCamelCase__		:		int			=vocab_size
								lowerCamelCase__		:		List[Any]			=d_model
								lowerCamelCase__		:		int			=d_kv
								lowerCamelCase__		:		int			=d_ff
								lowerCamelCase__		:		Optional[int]			=num_layers
								lowerCamelCase__		:		Any			=(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								lowerCamelCase__		:		List[str]			=num_heads
								lowerCamelCase__		:		str			=relative_attention_num_buckets
								lowerCamelCase__		:		Optional[Any]			=relative_attention_max_distance
								lowerCamelCase__		:		int			=dropout_rate
								lowerCamelCase__		:		Any			=layer_norm_epsilon
								lowerCamelCase__		:		Optional[int]			=initializer_factor
								lowerCamelCase__		:		Tuple			=feed_forward_proj
								lowerCamelCase__		:		List[str]			=use_cache

								lowerCamelCase__		:		Tuple			=self.feed_forward_proj.split('''-'''     )
								lowerCamelCase__		:		Union[str, Any]			=act_info[-1]
								lowerCamelCase__		:		Tuple			=act_info[0] == '''gated'''

								if len(lowerCamelCase     ) > 1 and act_info[0] != "gated" or len(lowerCamelCase     ) > 2:
											raise ValueError(
											    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
											    '''\'gated-gelu\' or \'relu\''''     )

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
											lowerCamelCase__		:		Dict			='''gelu_new'''

								super().__init__(
								    pad_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  **lowerCamelCase,  )





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					@property
					def 		snake_case       (       self							:  List[str]     )->					Mapping[str, Mapping[int, str]]:
								lowerCamelCase__		:		Optional[Any]			={
								    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
								    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
								}
								if self.use_past:
											lowerCamelCase__		:		Optional[Any]			='''past_encoder_sequence + sequence'''
											lowerCamelCase__		:		Optional[Any]			={0: '''batch'''}
											lowerCamelCase__		:		Tuple			={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
											lowerCamelCase__		:		Optional[int]			={0: '''batch''', 1: '''decoder_sequence'''}
											lowerCamelCase__		:		List[Any]			={0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase,  direction='''inputs'''     )

								return common_inputs




					@property
					def 		snake_case       (       self							:  List[str]     )->					int:
								return 13









"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase : int      						=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




