
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_024,
    "facebook/mbart-large-cc25": 1_024,
}

# 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 						lowercase_				(				A__ ):
 A__  : List[str]      					=			VOCAB_FILES_NAMES
 A__  : List[str]      					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__  : Optional[Any]      					=			PRETRAINED_VOCAB_FILES_MAP
 A__  : Optional[int]      					=			["""input_ids""", """attention_mask"""]
 A__  : Dict      					=			MBartTokenizer

 A__  : List[str]      					=			[]
 A__  : int      					=			[]

 def __init__(					self   ,		__UpperCamelCase=None   ,		__UpperCamelCase=None   ,		__UpperCamelCase="<s>"   ,		__UpperCamelCase="</s>"   ,		__UpperCamelCase="</s>"   ,		__UpperCamelCase="<s>"   ,		__UpperCamelCase="<unk>"   ,		__UpperCamelCase="<pad>"   ,		__UpperCamelCase="<mask>"   ,		__UpperCamelCase=None   ,		__UpperCamelCase=None   ,		__UpperCamelCase=None   ,		**__UpperCamelCase   ,		):




        """simple docstring"""


        UpperCamelCase_											=				AddedToken(__snake_case   ,		lstrip=__snake_case   ,		rstrip=__snake_case   ) if isinstance(__snake_case   ,		__snake_case   ) else mask_token

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

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

        UpperCamelCase_											=				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}   )
        UpperCamelCase_											=				{
            lang_code: self.convert_tokens_to_ids(__snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

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

 @property
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return self._src_lang

 @src_lang.setter
 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = None   ):




        """simple docstring"""


        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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = None   ):




        """simple docstring"""


        UpperCamelCase_											=				[self.sep_token_id]
        UpperCamelCase_											=				[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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		**__UpperCamelCase   ):




        """simple docstring"""


        if src_lang is None or tgt_lang is None:
               raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""   )
        UpperCamelCase_											=				src_lang
        UpperCamelCase_											=				self(__snake_case   ,		add_special_tokens=__snake_case   ,		return_tensors=__snake_case   ,		**__snake_case   )
        UpperCamelCase_											=				self.convert_tokens_to_ids(__snake_case   )
        UpperCamelCase_											=				tgt_lang_id
        return inputs

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = "en_XX"   ,		__UpperCamelCase = None   ,		__UpperCamelCase = "ro_RO"   ,		**__UpperCamelCase   ,		):




        """simple docstring"""


        UpperCamelCase_											=				src_lang
        UpperCamelCase_											=				tgt_lang
        return super().prepare_seqaseq_batch(__snake_case   ,		__snake_case   ,		**__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return self.set_src_lang_special_tokens(self.src_lang   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return self.set_tgt_lang_special_tokens(self.tgt_lang   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.convert_tokens_to_ids(__snake_case   )
        UpperCamelCase_											=				[]
        UpperCamelCase_											=				[self.eos_token_id, self.cur_lang_code]

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

        UpperCamelCase_											=				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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.convert_tokens_to_ids(__snake_case   )
        UpperCamelCase_											=				[]
        UpperCamelCase_											=				[self.eos_token_id, self.cur_lang_code]

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

        UpperCamelCase_											=				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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase = None   ):




        """simple docstring"""


        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(__snake_case   ):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''   )
               return
        UpperCamelCase_											=				os.path.join(
            __snake_case   ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

        return (out_vocab_file,)








'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

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

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







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







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





