




import copy

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


__SCREAMING_SNAKE_CASE			:      Dict      			=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:      Tuple      			={}






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]   ="fsmt_decoder"




     def __init__(			self     :      str		,     A__     :      Any=0		,     A__     :      Optional[int]=0	) ->				List[Any]:
           super().__init__()
           lowerCamelCase__					     = vocab_size
           lowerCamelCase__					     = bos_token_id






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		str   ="fsmt"
     _snake_case:		Optional[Any]   ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




     def __init__(			self     :      List[str]		,     A__     :      Optional[Any]=["en", "de"]		,     A__     :      Union[str, Any]=4_2_0_2_4		,     A__     :      Any=4_2_0_2_4		,     A__     :      List[str]="relu"		,     A__     :      str=1_0_2_4		,     A__     :      List[str]=2_0_0		,     A__     :      List[Any]=1_0_2_4		,     A__     :      Optional[int]=4_0_9_6		,     A__     :      Optional[int]=1_2		,     A__     :      Union[str, Any]=1_6		,     A__     :      int=0.0		,     A__     :      Optional[Any]=4_0_9_6		,     A__     :      List[str]=1_2		,     A__     :      List[Any]=1_6		,     A__     :      Any=0.0		,     A__     :      str=0.0		,     A__     :      str=0.1		,     A__     :      List[Any]=0.0		,     A__     :      Any=0.02		,     A__     :      Optional[int]=2		,     A__     :      Union[str, Any]=True		,     A__     :      Dict=True		,     A__     :      List[Any]=False		,     A__     :      Union[str, Any]=5		,     A__     :      Union[str, Any]=1.0		,     A__     :      str=False		,     A__     :      int=True		,     A__     :      Dict=1		,     A__     :      Any=0		,     A__     :      Union[str, Any]=2		,     A__     :      List[str]=2		,     **A__     :      Any		,     ) ->				Dict:
           lowerCamelCase__					     = langs
           lowerCamelCase__					     = src_vocab_size
           lowerCamelCase__					     = tgt_vocab_size
           lowerCamelCase__					     = d_model  # encoder_embed_dim and decoder_embed_dim

           lowerCamelCase__					     = encoder_ffn_dim
           lowerCamelCase__					     = lowerCamelCase__					     = encoder_layers
           lowerCamelCase__					     = encoder_attention_heads
           lowerCamelCase__					     = encoder_layerdrop
           lowerCamelCase__					     = decoder_layerdrop
           lowerCamelCase__					     = decoder_ffn_dim
           lowerCamelCase__					     = decoder_layers
           lowerCamelCase__					     = decoder_attention_heads
           lowerCamelCase__					     = max_position_embeddings
           lowerCamelCase__					     = init_std  # Normal(0, this parameter)
           lowerCamelCase__					     = activation_function

           lowerCamelCase__					     = DecoderConfig(vocab_size=A__		,     bos_token_id=A__	)
           if "decoder" in common_kwargs:
                 del common_kwargs["decoder"]

           lowerCamelCase__					     = scale_embedding  # scale factor will be sqrt(d_model) if True

           # 3 Types of Dropout
           lowerCamelCase__					     = attention_dropout
           lowerCamelCase__					     = activation_dropout
           lowerCamelCase__					     = dropout

           lowerCamelCase__					     = use_cache
           super().__init__(
               pad_token_id=A__		,     bos_token_id=A__		,     eos_token_id=A__		,     decoder_start_token_id=A__		,     is_encoder_decoder=A__		,     tie_word_embeddings=A__		,     forced_eos_token_id=A__		,     max_length=A__		,     num_beams=A__		,     length_penalty=A__		,     early_stopping=A__		,     **A__		,     )




     def        __snake_case      (			self     :      List[str]	) ->				Any:
           lowerCamelCase__					     = copy.deepcopy(self.__dict__	)
           lowerCamelCase__					     = self.decoder.to_dict()
           lowerCamelCase__					     = self.__class__.model_type
           return output





from typing import List, Optional, Tuple, Union

import torch

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






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

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

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

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)