
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}






class 	snake_case__       (      snake_case_					):







		"""simple docstring"""

		lowerCamelCase     							=						"""speech_to_text"""
		lowerCamelCase     							=						["""past_key_values"""]
		lowerCamelCase     							=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			Tuple=1_0000  ,	UpperCamelCase__				:			str=12  ,	UpperCamelCase__				:			int=2048  ,	UpperCamelCase__				:			int=4  ,	UpperCamelCase__				:			int=6  ,	UpperCamelCase__				:			List[str]=2048  ,	UpperCamelCase__				:			List[Any]=4  ,	UpperCamelCase__				:			List[str]=0.0  ,	UpperCamelCase__				:			Tuple=0.0  ,	UpperCamelCase__				:			int=True  ,	UpperCamelCase__				:			List[Any]=True  ,	UpperCamelCase__				:			List[str]="relu"  ,	UpperCamelCase__				:			Union[str, Any]=256  ,	UpperCamelCase__				:			Optional[Any]=0.1  ,	UpperCamelCase__				:			Any=0.0  ,	UpperCamelCase__				:			List[str]=0.0  ,	UpperCamelCase__				:			List[str]=0.02  ,	UpperCamelCase__				:			Dict=2  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			Optional[int]=1  ,	UpperCamelCase__				:			str=0  ,	UpperCamelCase__				:			List[str]=2  ,	UpperCamelCase__				:			Tuple=6000  ,	UpperCamelCase__				:			Dict=1024  ,	UpperCamelCase__				:			str=2  ,	UpperCamelCase__				:			Tuple=(5, 5)  ,	UpperCamelCase__				:			Dict=1024  ,	UpperCamelCase__				:			Any=80  ,	UpperCamelCase__				:			List[Any]=1  ,	**UpperCamelCase__				:			List[Any]  ,	)						->			Any:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						vocab_size
				snake_case  :       int     			=						d_model
				snake_case  :       Any     			=						encoder_ffn_dim
				snake_case  :       Any     			=						encoder_layers
				snake_case  :       List[Any]     			=						encoder_attention_heads
				snake_case  :       str     			=						decoder_ffn_dim
				snake_case  :       Union[str, Any]     			=						decoder_layers
				snake_case  :       str     			=						decoder_attention_heads
				snake_case  :       int     			=						dropout
				snake_case  :       int     			=						attention_dropout
				snake_case  :       int     			=						activation_dropout
				snake_case  :       Any     			=						activation_function
				snake_case  :       Union[str, Any]     			=						init_std
				snake_case  :       Union[str, Any]     			=						encoder_layerdrop
				snake_case  :       str     			=						decoder_layerdrop
				snake_case  :       Union[str, Any]     			=						use_cache
				snake_case  :       List[str]     			=						encoder_layers
				snake_case  :       Union[str, Any]     			=						scale_embedding  # scale factor will be sqrt(d_model) if True
				snake_case  :       int     			=						max_source_positions
				snake_case  :       List[Any]     			=						max_target_positions
				snake_case  :       Any     			=						num_conv_layers
				snake_case  :       List[Any]     			=						list(UpperCamelCase__      )
				snake_case  :       str     			=						conv_channels
				snake_case  :       Optional[Any]     			=						input_feat_per_channel
				snake_case  :       str     			=						input_channels

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

				super().__init__(
				    pad_token_id=UpperCamelCase__  ,	bos_token_id=UpperCamelCase__  ,	eos_token_id=UpperCamelCase__  ,	is_encoder_decoder=UpperCamelCase__  ,	decoder_start_token_id=UpperCamelCase__  ,	**UpperCamelCase__  ,	)










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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

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

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).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
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





