


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        _a							(			UpperCamelCase__			):

							def __init__(							self:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int							)			->   Optional[int]:





									"""simple docstring"""







									super().__init__()

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

									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_:	float = 0.0    ,						UpperCamelCase_:	int = 50    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	Optional[str] = "pil"    ,						UpperCamelCase_:	bool = True    ,						)			->   Union[ImagePipelineOutput, Tuple]:





									"""simple docstring"""







									if isinstance(self.unet.config.sample_size    ,						UpperCamelCase_							):
											lowercase__            =      (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
									else:
											lowercase__            =      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

									lowercase__            =      randn_tensor(UpperCamelCase_    ,						generator=UpperCamelCase_    ,						device=self.device    ,						dtype=self.unet.dtype							)

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

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

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=UpperCamelCase_							)




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			__A				=None


__A				=logging.get_logger(__name__)


__A				={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

__A				={
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
__A				=['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = MBartTokenizer

	lowerCAmelCase__         = []
	lowerCAmelCase__         = []



	def __init__(    self						,  lowercase=None						,  lowercase=None						,  lowercase="<s>"						,  lowercase="</s>"						,  lowercase="</s>"						,  lowercase="<s>"						,  lowercase="<unk>"						,  lowercase="<pad>"						,  lowercase="<mask>"						,  lowercase=None						,  lowercase=None						,  lowercase=None						,  **lowercase						,  )				->       Dict:
					# Mask token behave like a normal word, i.e. include the space before it
					lowerCamelCase_						=			AddedToken(lowercase						,  lstrip=lowercase						,  rstrip=lowercase ) if isinstance(lowercase						,  lowercase ) else mask_token

					super().__init__(
					    vocab_file=lowercase						,  tokenizer_file=lowercase						,  bos_token=lowercase						,  eos_token=lowercase						,  sep_token=lowercase						,  cls_token=lowercase						,  unk_token=lowercase						,  pad_token=lowercase						,  mask_token=lowercase						,  src_lang=lowercase						,  tgt_lang=lowercase						,  additional_special_tokens=lowercase						,  **lowercase						,  )

					lowerCamelCase_						=			vocab_file
					lowerCamelCase_						=			False if not self.vocab_file else True

					lowerCamelCase_						=			FAIRSEQ_LANGUAGE_CODES.copy()

					if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens] )

					self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
					lowerCamelCase_						=			{
					    lang_code: self.convert_tokens_to_ids(lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					lowerCamelCase_						=			src_lang if src_lang is not None else "en_XX"
					lowerCamelCase_						=			self.convert_tokens_to_ids(self._src_lang )
					lowerCamelCase_						=			tgt_lang
					self.set_src_lang_special_tokens(self._src_lang )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self._src_lang



	@src_lang.setter
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			new_src_lang
					self.set_src_lang_special_tokens(self._src_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep ) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       List[Any]:
					if src_lang is None or tgt_lang is None:
									raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			self(lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  **lowercase )
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			tgt_lang_id
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = "en_XX"						,  lowercase = None						,  lowercase = "ro_RO"						,  **lowercase						,  )				->       BatchEncoding:
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			tgt_lang
					return super().prepare_seqaseq_batch(lowercase						,  lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return self.set_src_lang_special_tokens(self.src_lang )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.set_tgt_lang_special_tokens(self.tgt_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

					lowerCamelCase_						=			self.convert_ids_to_tokens(self.prefix_tokens )
					lowerCamelCase_						=			self.convert_ids_to_tokens(self.suffix_tokens )

					lowerCamelCase_						=			processors.TemplateProcessing(
					    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,  self.prefix_tokens + self.suffix_tokens ) )						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

					lowerCamelCase_						=			self.convert_ids_to_tokens(self.prefix_tokens )
					lowerCamelCase_						=			self.convert_ids_to_tokens(self.suffix_tokens )

					lowerCamelCase_						=			processors.TemplateProcessing(
					    single=prefix_tokens_str + ["$A"] + suffix_tokens_str						,  pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str						,  special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str						,  self.prefix_tokens + self.suffix_tokens ) )						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer." )

					if not os.path.isdir(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
									copyfile(self.vocab_file						,  lowercase )

					return (out_vocab_file,)


