'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Dict:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :str						    = UNetaDModel(
													    block_out_channels=(32, 64)							,							layers_per_block=2							,							sample_size=32							,							in_channels=3							,							out_channels=3							,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,							)
													return model




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Any						    = self.dummy_uncond_unet
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = ScoreSdeVeScheduler()

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = ScoreSdeVePipeline(unet=_UpperCAmelCase							,							scheduler=_UpperCAmelCase      )
													sde_ve.to(_UpperCAmelCase      )
													sde_ve.set_progress_bar_config(disable=_UpperCAmelCase      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = sde_ve(num_inference_steps=2							,							output_type='numpy'							,							generator=_UpperCAmelCase      ).images

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :int						    = sde_ve(num_inference_steps=2							,							output_type='numpy'							,							generator=_UpperCAmelCase							,							return_dict=_UpperCAmelCase      )[
													    0
													]

													SCREAMING_SNAKE_CASE__     :str						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :Dict						    = image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :str						    = 'google/ncsnpp-church-256'
													SCREAMING_SNAKE_CASE__     :Dict						    = UNetaDModel.from_pretrained(_UpperCAmelCase      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = ScoreSdeVeScheduler.from_pretrained(_UpperCAmelCase      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = ScoreSdeVePipeline(unet=_UpperCAmelCase							,							scheduler=_UpperCAmelCase      )
													sde_ve.to(_UpperCAmelCase      )
													sde_ve.set_progress_bar_config(disable=_UpperCAmelCase      )

													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sde_ve(num_inference_steps=10							,							output_type='numpy'							,							generator=_UpperCAmelCase      ).images

													SCREAMING_SNAKE_CASE__     :Any						    = image[0, -3:, -3:, -1]

													assert image.shape == (1, 2_56, 2_56, 3)

													SCREAMING_SNAKE_CASE__     :str						    = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2












"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''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 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  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       ,				)

