

"""simple docstring"""

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_mbartaa import MBartaaTokenizer
else:
    _UpperCamelCase		    =						None


_UpperCamelCase		    =						logging.get_logger(__name__)

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

_UpperCamelCase		    =						{
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCamelCase		    =						{
    """facebook/mbart-large-50-one-to-many-mmt""": 1_0_2_4,
}

# fmt: off
_UpperCamelCase		    =						["""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""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Optional[int]          =	VOCAB_FILES_NAMES
       __UpperCamelCase				:			int          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase				:			Optional[int]          =	PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase				:			List[Any]          =	['input_ids', 'attention_mask']
       __UpperCamelCase				:			List[str]          =	MBartaaTokenizer

       __UpperCamelCase				:			List[int]          =	[]
       __UpperCamelCase				:			List[int]          =	[]






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





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							AddedToken(snake_case ,				lstrip=snake_case ,				rstrip=snake_case      ) if isinstance(snake_case ,				snake_case      ) else mask_token

        lowerCAmelCase__      :							Tuple    =							kwargs.get("additional_special_tokens" ,				[]      )
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            snake_case ,				src_lang=snake_case ,				tgt_lang=snake_case ,				tokenizer_file=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 ,				**snake_case ,				)

        lowerCAmelCase__      :							Tuple    =							vocab_file
        lowerCAmelCase__      :							Tuple    =							False if not self.vocab_file else True

        lowerCAmelCase__      :							str    =							{
            lang_code: self.convert_tokens_to_ids(snake_case      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowerCAmelCase__      :							Optional[Any]    =							src_lang if src_lang is not None else "en_XX"
        lowerCAmelCase__      :							Dict    =							tgt_lang
        lowerCAmelCase__      :							Dict    =							self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang      )






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return self._src_lang






       @src_lang.setter
       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							new_src_lang
        self.set_src_lang_special_tokens(self._src_lang      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = 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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = "en_XX" ,				snake_case = None ,				snake_case = "ro_RO" ,				**snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							src_lang
        lowerCAmelCase__      :							Dict    =							tgt_lang
        return super().prepare_seqaseq_batch(snake_case ,				snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return self.set_src_lang_special_tokens(self.src_lang      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return self.set_tgt_lang_special_tokens(self.tgt_lang      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							self.convert_tokens_to_ids(snake_case      )
        lowerCAmelCase__      :							Dict    =							[self.cur_lang_code_id]
        lowerCAmelCase__      :							List[str]    =							[self.eos_token_id]

        lowerCAmelCase__      :							List[str]    =							self.convert_ids_to_tokens(self.prefix_tokens      )
        lowerCAmelCase__      :							Dict    =							self.convert_ids_to_tokens(self.suffix_tokens      )

        lowerCAmelCase__      :							Dict    =							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 ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							self.convert_tokens_to_ids(snake_case      )
        lowerCAmelCase__      :							List[Any]    =							[self.cur_lang_code_id]
        lowerCAmelCase__      :							int    =							[self.eos_token_id]

        lowerCAmelCase__      :							Optional[int]    =							self.convert_ids_to_tokens(self.prefix_tokens      )
        lowerCAmelCase__      :							Dict    =							self.convert_ids_to_tokens(self.suffix_tokens      )

        lowerCAmelCase__      :							List[Any]    =							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 ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				**snake_case      ):





        """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"      )
        lowerCAmelCase__      :							Union[str, Any]    =							src_lang
        lowerCAmelCase__      :							Tuple    =							self(snake_case ,				add_special_tokens=snake_case ,				return_tensors=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							Union[str, Any]    =							self.convert_tokens_to_ids(snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							tgt_lang_id
        return inputs






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = 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
        lowerCAmelCase__      :							str    =							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"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Optional[int]          =	(VQDiffusionScheduler,)






       def  SCREAMING_SNAKE_CASE_  (      self ,				**snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							{
            "num_vec_classes": 4_097,
            "num_train_timesteps": 100,
        }

        config.update(**snake_case      )
        return config






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[int]    =							4
        lowerCAmelCase__      :							Optional[Any]    =							8
        lowerCAmelCase__      :							Union[str, Any]    =							8

        lowerCAmelCase__      :							Optional[Any]    =							torch.randint(0 ,				snake_case ,				(batch_size, height * width)      )

        return sample






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        assert False






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        def model(snake_case ,				snake_case ,				*snake_case      ):
         lowerCAmelCase__ ,	lowerCAmelCase__      :							str    =							sample.shape
         lowerCAmelCase__      :							Dict    =							torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
         lowerCAmelCase__      :							int    =							F.log_softmax(logits.double() ,				dim=1      ).float()
         return return_value

        return model






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for timesteps in [2, 5, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for num_vec_classes in [5, 100, 1_000, 4_000]:
         self.check_over_configs(num_vec_classes=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for t in [0, 50, 99]:
         self.check_over_forward(time_step=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






