





"""simple docstring"""
from typing import List, Optional, Tuple, Union

import torch

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






class    _UpperCAmelCase  (      lowercase_					):


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :Dict						):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase	,			scheduler=__UpperCamelCase						)


					@torch.no_grad()
					def __call__(   self     :int	,			__UpperCamelCase     :int = 1	,			__UpperCamelCase     :Optional[Union[torch.Generator, List[torch.Generator]]] = None	,			__UpperCamelCase     :int = 10_00	,			__UpperCamelCase     :Optional[str] = "pil"	,			__UpperCamelCase     :bool = True	,			):
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size	,			__UpperCamelCase						):
																			A						=    (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
												else:
																			A						=    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase						)









"""simple docstring"""
import copy

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


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

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

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


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


