



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:
 __snake_case		:List[str] 						=	None


__snake_case		:str 						=	logging.get_logger(__name__)

__snake_case		:List[Any] 						=	{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__snake_case		:Union[str, Any] 						=	{
    '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'
        ),
    },
}

__snake_case		:Dict 						=	{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
__snake_case		:List[Any] 						=	['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			(      UpperCAmelCase__       ):
       UpperCamelCase__    :       List[str]		   =		VOCAB_FILES_NAMES
       UpperCamelCase__    :       Optional[int]		   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__    :       Optional[int]		   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__    :       Dict		   =		["input_ids", "attention_mask"]
       UpperCamelCase__    :       List[Any]		   =		MBartaaTokenizer

       UpperCamelCase__    :       List[int]		   =		[]
       UpperCamelCase__    :       List[int]		   =		[]



       def __init__(						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Any=None	,						__SCREAMING_SNAKE_CASE				:	Tuple=None	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=None	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=None	,						__SCREAMING_SNAKE_CASE				:	int="</s>"	,						__SCREAMING_SNAKE_CASE				:	Dict="</s>"	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]="<s>"	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]="<unk>"	,						__SCREAMING_SNAKE_CASE				:	Any="<pad>"	,						__SCREAMING_SNAKE_CASE				:	int="<mask>"	,						**__SCREAMING_SNAKE_CASE				:	str	,						):


           '''simple docstring'''

           __a					=							AddedToken(snake_case__	,						lstrip=snake_case__	,						rstrip=snake_case__) if isinstance(snake_case__	,						snake_case__) else mask_token

           __a					=							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__	,						)

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

           __a					=							{
               lang_code: self.convert_tokens_to_ids(snake_case__) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           __a					=							src_lang if src_lang is not None else '''en_XX'''
           __a					=							tgt_lang
           __a					=							self.lang_code_to_id[self._src_lang]
           self.set_src_lang_special_tokens(self._src_lang)



       @property
       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           return self._src_lang



       @src_lang.setter
       def 						_lowerCamelCase     (						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	str):


           '''simple docstring'''

           __a					=							new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)



       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	List[int]	,						__SCREAMING_SNAKE_CASE				:	Optional[List[int]] = 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				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	str = "en_XX"	,						__SCREAMING_SNAKE_CASE				:	Optional[List[str]] = None	,						__SCREAMING_SNAKE_CASE				:	str = "ro_RO"	,						**__SCREAMING_SNAKE_CASE				:	Tuple	,						):


           '''simple docstring'''

           __a					=							src_lang
           __a					=							tgt_lang
           return super().prepare_seqaseq_batch(snake_case__	,						snake_case__	,						**snake_case__)



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang)



       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang)



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	str):


           '''simple docstring'''

           __a					=							self.convert_tokens_to_ids(snake_case__)
           __a					=							[self.cur_lang_code_id]
           __a					=							[self.eos_token_id]

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

           __a					=							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				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	str):


           '''simple docstring'''

           __a					=							self.convert_tokens_to_ids(snake_case__)
           __a					=							[self.cur_lang_code_id]
           __a					=							[self.eos_token_id]

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

           __a					=							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				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Optional[str]	,						__SCREAMING_SNAKE_CASE				:	Optional[str]	,						**__SCREAMING_SNAKE_CASE				:	Tuple):


           '''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''')
           __a					=							src_lang
           __a					=							self(snake_case__	,						add_special_tokens=snake_case__	,						return_tensors=snake_case__	,						**snake_case__)
           __a					=							self.convert_tokens_to_ids(snake_case__)
           __a					=							tgt_lang_id
           return inputs





       def 						_lowerCamelCase     (						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Optional[str] = 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
           __a					=							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,)











import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


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

									return model







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




