

'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					42
		SCREAMING_SNAKE_CASE_           =					42
		SCREAMING_SNAKE_CASE_           =					None


class  UpperCAmelCase (							UpperCAmelCase__  , UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					2
		@register_to_config
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_ = 0.02   ,			SCREAMING_SNAKE_CASE_ = 100   ,			SCREAMING_SNAKE_CASE_ = 1.007   ,			SCREAMING_SNAKE_CASE_ = 80   ,			SCREAMING_SNAKE_CASE_ = 0.05   ,			SCREAMING_SNAKE_CASE_ = 50   ,			)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      sigma_max

							# setable values
							lowerCamelCase_  				=      None
							lowerCamelCase_  				=      None
							lowerCamelCase_  				=      None  # sigma(t_i)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     torch.FloatTensor:






							'''simple docstring'''



							return sample
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      num_inference_steps
							lowerCamelCase_  				=      np.arange(0   ,			self.num_inference_steps		)[::-1].copy()
							lowerCamelCase_  				=      torch.from_numpy(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      [
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							lowerCamelCase_  				=      torch.tensor(SCREAMING_SNAKE_CASE_   ,			dtype=torch.floataa   ,			device=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = None		)  ->     Tuple[torch.FloatTensor, float]:






							'''simple docstring'''



							if self.config.s_min <= sigma <= self.config.s_max:
												lowerCamelCase_  				=      min(self.config.s_churn / self.num_inference_steps   ,			2**0.5 - 1		)
							else:
												lowerCamelCase_  				=      0

							# sample eps ~ N(0, S_noise^2 * I)
							lowerCamelCase_  				=      self.config.s_noise * randn_tensor(sample.shape   ,			generator=SCREAMING_SNAKE_CASE_		).to(sample.device		)
							lowerCamelCase_  				=      sigma + gamma * sigma
							lowerCamelCase_  				=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = True   ,			)  ->     Union[KarrasVeOutput, Tuple]:






							'''simple docstring'''



							lowerCamelCase_  				=      sample_hat + sigma_hat * model_output
							lowerCamelCase_  				=      (sample_hat - pred_original_sample) / sigma_hat
							lowerCamelCase_  				=      sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
												return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=SCREAMING_SNAKE_CASE_   ,			derivative=SCREAMING_SNAKE_CASE_   ,			pred_original_sample=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = True   ,			)  ->     Union[KarrasVeOutput, Tuple]:






							'''simple docstring'''



							lowerCamelCase_  				=      sample_prev + sigma_prev * model_output
							lowerCamelCase_  				=      (sample_prev - pred_original_sample) / sigma_prev
							lowerCamelCase_  				=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
												return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=SCREAMING_SNAKE_CASE_   ,			derivative=SCREAMING_SNAKE_CASE_   ,			pred_original_sample=SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Any:






							'''simple docstring'''



							raise NotImplementedError()







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:
				lowerCAmelCase       		=    None


lowerCAmelCase       		=    logging.get_logger(__name__)


lowerCAmelCase       		=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase       		=    {
    """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""",
    },
}

lowerCAmelCase       		=    {
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
lowerCAmelCase       		=    ["""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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		List[str]										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase     :		str										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Optional[int]										=						['''input_ids''', '''attention_mask''']
	_lowerCAmelCase     :		Optional[int]										=						MBartTokenizer

	_lowerCAmelCase     :		List[int]										=						[]
	_lowerCAmelCase     :		List[int]										=						[]


	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__				,     ):
							# Mask token behave like a normal word, i.e. include the space before it
							__UpperCAmelCase			:							Any	    =	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__				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	vocab_file
							__UpperCAmelCase			:							int	    =	False if not self.vocab_file else True

							__UpperCAmelCase			:							str	    =	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			:							Union[str, Any]	    =	{
							    lang_code: self.convert_tokens_to_ids(lowercase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							__UpperCAmelCase			:							Any	    =	src_lang if src_lang is not None else '''en_XX'''
							__UpperCAmelCase			:							Optional[int]	    =	self.convert_tokens_to_ids(self._src_lang)
							__UpperCAmelCase			:							List[Any]	    =	tgt_lang
							self.set_src_lang_special_tokens(self._src_lang)


	@property
	def  A(    self):
							return self._src_lang


	@src_lang.setter
	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							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  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							Optional[int]	    =	[self.sep_token_id]
							__UpperCAmelCase			:							int	    =	[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  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__):
							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			:							Optional[Any]	    =	src_lang
							__UpperCAmelCase			:							Union[str, Any]	    =	self(lowercase__				,     add_special_tokens=lowercase__				,     return_tensors=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							int	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	tgt_lang_id
							return inputs


	def  A(    self				,     lowercase__				,     lowercase__ = "en_XX"				,     lowercase__ = None				,     lowercase__ = "ro_RO"				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Any	    =	src_lang
							__UpperCAmelCase			:							Tuple	    =	tgt_lang
							return super().prepare_seqaseq_batch(lowercase__				,     lowercase__				,     **lowercase__)


	def  A(    self):
							return self.set_src_lang_special_tokens(self.src_lang)


	def  A(    self):
							return self.set_tgt_lang_special_tokens(self.tgt_lang)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[]
							__UpperCAmelCase			:							Dict	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							int	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Dict	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							str	    =	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  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	[]
							__UpperCAmelCase			:							List[Any]	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							Optional[int]	    =	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  A(    self				,     lowercase__				,     lowercase__ = None):
							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
							__UpperCAmelCase			:							List[Any]	    =	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,)

