

'''simple docstring'''





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


UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					{
    """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       ( lowercase__			):
				lowerCamelCase_							=						'mctct'







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

										# prevents config testing fail with exporting to json
										lowercase   :       Optional[int]								=list(UpperCAmelCase__      )
										lowercase   :       Optional[Any]								=list(UpperCAmelCase__      )

										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}`.'''      )








'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase   :       Any								=DDIMScheduler.from_config(scheduler.config      )

										self.register_modules(unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		List[Any]		,				UpperCAmelCase__    :		int = 1		,				UpperCAmelCase__    :		Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				):
										'''simple docstring'''

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size		,				UpperCAmelCase__      ):
																lowercase   :       Optional[int]								=(
																    batch_size,
																    self.unet.config.in_channels,
																    self.unet.config.sample_size,
																    self.unet.config.sample_size,
																)
										else:
																lowercase   :       Optional[int]								=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) and len(UpperCAmelCase__      ) != batch_size:
																raise ValueError(
																    F'''You have passed a list of generators of length {len(UpperCAmelCase__      )}, but requested an effective batch'''
																    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

										lowercase   :       str								=randn_tensor(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=self.unet.dtype      )

										# set step values
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										for t in self.progress_bar(self.scheduler.timesteps      ):
																# 1. predict noise model_output
																lowercase   :       Dict								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__      ).sample

																# 2. predict previous mean of image x_t-1 and add variance depending on eta
																# eta corresponds to η in paper and should be between [0, 1]
																# do x_t -> x_t-1
																lowercase   :       Dict								=self.scheduler.step(
																    UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				eta=UpperCAmelCase__		,				use_clipped_model_output=UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

										lowercase   :       Optional[Any]								=(image / 2 + 0.5).clamp(0		,				1      )
										lowercase   :       Optional[Any]								=image.cpu().permute(0		,				2		,				3		,				1      ).numpy()
										if output_type == "pil":
																lowercase   :       List[str]								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__      )





