

from ....configuration_utils import PretrainedConfig
from ....utils import logging


__A			:					Optional[Any]						=			logging.get_logger(__name__)

__A			:					List[str]						=			{
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "mctct"





   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			int=8065     , __lowerCamelCase					:			List[Any]=1536     , __lowerCamelCase					:			Dict=36     , __lowerCamelCase					:			Optional[int]=6144     , __lowerCamelCase					:			Union[str, Any]=4     , __lowerCamelCase					:			Any=384     , __lowerCamelCase					:			List[str]=920     , __lowerCamelCase					:			Optional[int]=1e-5     , __lowerCamelCase					:			Tuple=0.3     , __lowerCamelCase					:			Any="relu"     , __lowerCamelCase					:			Optional[int]=0.02     , __lowerCamelCase					:			Any=0.3     , __lowerCamelCase					:			Any=0.3     , __lowerCamelCase					:			List[Any]=1     , __lowerCamelCase					:			str=0     , __lowerCamelCase					:			Dict=2     , __lowerCamelCase					:			Optional[int]=1     , __lowerCamelCase					:			Optional[Any]=0.3     , __lowerCamelCase					:			Any=1     , __lowerCamelCase					:			Dict=(7,)     , __lowerCamelCase					:			Dict=(3,)     , __lowerCamelCase					:			int=80     , __lowerCamelCase					:			Dict=1     , __lowerCamelCase					:			Any=None     , __lowerCamelCase					:			Optional[Any]="sum"     , __lowerCamelCase					:			Dict=False     , **__lowerCamelCase					:			Tuple     , ):
       super().__init__(**__lowerCamelCase     , pad_token_id=__lowerCamelCase     , bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   attention_head_dim
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   layerdrop
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
       SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE										=   pad_token_id
       SCREAMING_SNAKE_CASE										=   bos_token_id
       SCREAMING_SNAKE_CASE										=   eos_token_id
       SCREAMING_SNAKE_CASE										=   conv_glu_dim
       SCREAMING_SNAKE_CASE										=   conv_dropout
       SCREAMING_SNAKE_CASE										=   num_conv_layers
       SCREAMING_SNAKE_CASE										=   input_feat_per_channel
       SCREAMING_SNAKE_CASE										=   input_channels
       SCREAMING_SNAKE_CASE										=   conv_channels
       SCREAMING_SNAKE_CASE										=   ctc_loss_reduction
       SCREAMING_SNAKE_CASE										=   ctc_zero_infinity

       # prevents config testing fail with exporting to json
       SCREAMING_SNAKE_CASE										=   list(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   list(__lowerCamelCase			)

       if len(self.conv_kernel			) != self.num_conv_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
               f"but is `len(config.conv_kernel) = {len(self.conv_kernel			)}`, "
               f"`config.num_conv_layers = {self.num_conv_layers}`."			)





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

