from typing import List, Optional, Tuple, Union

import torch

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





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : UNetaDModel
   __UpperCAmelCase : KarrasVeScheduler


   def __init__(self     :						int       ,  __UpperCAmelCase     :						UNetaDModel       ,  __UpperCAmelCase     :						KarrasVeScheduler		)						->				List[Any]:
         """simple docstring"""



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





   @torch.no_grad()
   def __call__(self     :						Optional[int]       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						int = 5_0       ,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  __UpperCAmelCase     :						Optional[str] = "pil"       ,  __UpperCAmelCase     :						bool = True       ,  **__UpperCAmelCase     :						List[Any]       ,  )						->				Union[Tuple, ImagePipelineOutput]:
         """simple docstring"""



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

         UpperCAmelCase__								=  self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         UpperCAmelCase__								=  randn_tensor(__UpperCAmelCase       ,  generator=__UpperCAmelCase       ,  device=self.device		) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__UpperCAmelCase		)

         for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               UpperCAmelCase__								=  self.scheduler.schedule[t]
               UpperCAmelCase__								=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               UpperCAmelCase__		,      UpperCAmelCase__								=  self.scheduler.add_noise_to_input(__UpperCAmelCase       ,  __UpperCAmelCase       ,  generator=__UpperCAmelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               UpperCAmelCase__								=  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,  sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               UpperCAmelCase__								=  self.scheduler.step(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)

               if sigma_prev != 0:
                     # 6. Apply 2nd order correction
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     UpperCAmelCase__								=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,  sigma_prev / 2		).sample
                     UpperCAmelCase__								=  self.scheduler.step_correct(
                         __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  step_output.prev_sample       ,  step_output["derivative"]       ,  )
               UpperCAmelCase__								=  step_output.prev_sample

         UpperCAmelCase__								=  (sample / 2 + 0.5).clamp(0       ,  1		)
         UpperCAmelCase__								=  sample.cpu().permute(0       ,  2       ,  3       ,  1		).numpy()
         if output_type == "pil":
               UpperCAmelCase__								=  self.numpy_to_pil(__UpperCAmelCase		)

         if not return_dict:
               return (image,)

         return ImagePipelineOutput(images=__UpperCAmelCase		)








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


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Optional[Any]        =  "speech_to_text_2"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					Any        =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
       def __init__(			self,			SCREAMING_SNAKE_CASE_=1_0000,			SCREAMING_SNAKE_CASE_=6,			SCREAMING_SNAKE_CASE_=2048,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_="relu",			SCREAMING_SNAKE_CASE_=256,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=1024,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          UpperCamelCase    :  Optional[int]							=	vocab_size
          UpperCamelCase    :  List[str]							=	d_model
          UpperCamelCase    :  List[str]							=	decoder_ffn_dim
          UpperCamelCase    :  Optional[Any]							=	decoder_layers
          UpperCamelCase    :  Any							=	decoder_attention_heads
          UpperCamelCase    :  Tuple							=	dropout
          UpperCamelCase    :  str							=	attention_dropout
          UpperCamelCase    :  str							=	activation_dropout
          UpperCamelCase    :  Union[str, Any]							=	activation_function
          UpperCamelCase    :  Optional[int]							=	init_std
          UpperCamelCase    :  Tuple							=	decoder_layerdrop
          UpperCamelCase    :  Dict							=	use_cache
          UpperCamelCase    :  Any							=	decoder_layers
          UpperCamelCase    :  Union[str, Any]							=	scale_embedding  # scale factor will be sqrt(d_model) if True
          UpperCamelCase    :  Union[str, Any]							=	max_target_positions

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



