




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_nllb import NllbTokenizer
else:
						__UpperCamelCase      :      Optional[Any]     						=						None


__UpperCamelCase      :      Tuple     						=						logging.get_logger(__name__)


__UpperCamelCase      :      Union[str, Any]     						=						{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase      :      Any     						=						{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase      :      int     						=						{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
__UpperCamelCase      :      Any     						=						['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']


class        __UpperCamelCase    (     snake_case__   ):
	__snake_case	:Dict   =       VOCAB_FILES_NAMES
	__snake_case	:Tuple   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case	:Optional[Any]   =       PRETRAINED_VOCAB_FILES_MAP
	__snake_case	:List[str]   =       ['''input_ids''', '''attention_mask''']
	__snake_case	:Optional[int]   =       NllbTokenizer

	__snake_case	:List[int]   =       []
	__snake_case	:List[int]   =       []







	def __init__(  self      :							Any       ,				_lowerCAmelCase      :							Optional[int]=None       ,				_lowerCAmelCase      :							int=None       ,				_lowerCAmelCase      :							Dict="<s>"       ,				_lowerCAmelCase      :							List[str]="</s>"       ,				_lowerCAmelCase      :							int="</s>"       ,				_lowerCAmelCase      :							Optional[Any]="<s>"       ,				_lowerCAmelCase      :							Any="<unk>"       ,				_lowerCAmelCase      :							Optional[int]="<pad>"       ,				_lowerCAmelCase      :							Tuple="<mask>"       ,				_lowerCAmelCase      :							Any=None       ,				_lowerCAmelCase      :							Any=None       ,				_lowerCAmelCase      :							List[str]=None       ,				_lowerCAmelCase      :							List[Any]=False       ,				**_lowerCAmelCase      :							Optional[int]       ,				)		-> Dict:
		"""simple docstring"""







		__lowercase             =  AddedToken(_A       ,				lstrip=_A       ,				rstrip=_A			) if isinstance(_A       ,				_A			) else mask_token
		__lowercase             =  legacy_behaviour
		super().__init__(
		    vocab_file=_A       ,				tokenizer_file=_A       ,				bos_token=_A       ,				eos_token=_A       ,				sep_token=_A       ,				cls_token=_A       ,				unk_token=_A       ,				pad_token=_A       ,				mask_token=_A       ,				src_lang=_A       ,				tgt_lang=_A       ,				additional_special_tokens=_A       ,				legacy_behaviour=_A       ,				**_A       ,				)

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

		__lowercase             =  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}			)
		__lowercase             =  {
		    lang_code: self.convert_tokens_to_ids(_A			) for lang_code in FAIRSEQ_LANGUAGE_CODES
		}

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







	@property
	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		return self._src_lang







	@src_lang.setter
	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Tuple			)		-> str:
		"""simple docstring"""







		__lowercase             =  new_src_lang
		self.set_src_lang_special_tokens(self._src_lang			)







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							str = None			)		-> Optional[Any]:
		"""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  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Any = None			)		-> Any:
		"""simple docstring"""







		__lowercase             =  [self.sep_token_id]
		__lowercase             =  [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      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Any       ,				**_lowerCAmelCase      :							List[Any]			)		-> Optional[Any]:
		"""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"""			)
		__lowercase             =  src_lang
		__lowercase             =  self(_A       ,				add_special_tokens=_A       ,				return_tensors=_A       ,				**_A			)
		__lowercase             =  self.convert_tokens_to_ids(_A			)
		__lowercase             =  tgt_lang_id
		return inputs







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							int = "eng_Latn"       ,				_lowerCAmelCase      :							Optional[Any] = None       ,				_lowerCAmelCase      :							str = "fra_Latn"       ,				**_lowerCAmelCase      :							Dict       ,				)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  src_lang
		__lowercase             =  tgt_lang
		return super().prepare_seqaseq_batch(_A       ,				_A       ,				**_A			)







	def  _a   (  self      :							Tuple			)		-> Optional[Any]:
		"""simple docstring"""







		return self.set_src_lang_special_tokens(self.src_lang			)







	def  _a   (  self      :							Optional[Any]			)		-> str:
		"""simple docstring"""







		return self.set_tgt_lang_special_tokens(self.tgt_lang			)







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							Dict			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.convert_tokens_to_ids(_A			)

		if self.legacy_behaviour:
			__lowercase             =  []
			__lowercase             =  [self.eos_token_id, self.cur_lang_code]
		else:
			__lowercase             =  [self.cur_lang_code]
			__lowercase             =  [self.eos_token_id]

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

		__lowercase             =  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      :							Any       ,				_lowerCAmelCase      :							List[Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.convert_tokens_to_ids(_A			)
		if self.legacy_behaviour:
			__lowercase             =  []
			__lowercase             =  [self.eos_token_id, self.cur_lang_code]
		else:
			__lowercase             =  [self.cur_lang_code]
			__lowercase             =  [self.eos_token_id]

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

		__lowercase             =  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      :							List[Any]       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Dict = None			)		-> Union[str, Any]:
		"""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(_A			):
			logger.error(F'Vocabulary path ({save_directory}) should be a directory.'			)
			return
		__lowercase             =  os.path.join(
		    _A       ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

		return (out_vocab_file,)









import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:str   =       (UnCLIPScheduler,)







	def  _a   (  self      :							Optional[int]       ,				**_lowerCAmelCase      :							Any			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**_lowerCAmelCase			)
		return config







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=_lowerCAmelCase			)







	def  _a   (  self      :							List[str]			)		-> Tuple:
		"""simple docstring"""







		for variance in ["fixed_small_log", "learned_range"]:
			self.check_over_configs(variance_type=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Any:
		"""simple docstring"""







		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=_lowerCAmelCase			)







	def  _a   (  self      :							Any			)		-> Optional[Any]:
		"""simple docstring"""







		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Tuple:
		"""simple docstring"""







		for prediction_type in ["epsilon", "sample"]:
			self.check_over_configs(prediction_type=_lowerCAmelCase			)







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

				self.check_over_forward(time_step=_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""fixed_small_log"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0000e-10			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_549_625			)			) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_994_987			)			) < 1e-5







	def  _a   (  self      :							str			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config(variance_type="""learned_range"""			)
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  0.5

		assert scheduler._get_variance(1       ,				predicted_variance=_lowerCAmelCase			) - -10.1_712_790 < 1e-5
		assert scheduler._get_variance(487       ,				predicted_variance=_lowerCAmelCase			) - -5.7_998_052 < 1e-5
		assert scheduler._get_variance(999       ,				predicted_variance=_lowerCAmelCase			) - -0.0_010_011 < 1e-5







	def  _a   (  self      :							Optional[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 252.2_682_495			) < 1e-2
		assert abs(result_mean.item() - 0.3_284_743			) < 1e-3







	def  _a   (  self      :							List[str]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.scheduler_classes[0]
		__lowercase             =  self.get_scheduler_config()
		__lowercase             =  scheduler_class(**_lowerCAmelCase			)

		scheduler.set_timesteps(25			)

		__lowercase             =  scheduler.timesteps

		__lowercase             =  self.dummy_model()
		__lowercase             =  self.dummy_sample_deter
		__lowercase             =  torch.manual_seed(0			)

		for i, t in enumerate(_lowerCAmelCase			):
			# 1. predict noise residual
			__lowercase             =  model(_lowerCAmelCase       ,				_lowerCAmelCase			)

			if i + 1 == timesteps.shape[0]:
				__lowercase             =  None
			else:
				__lowercase             =  timesteps[i + 1]

			# 2. predict previous mean of sample x_t-1
			__lowercase             =  scheduler.step(
			    _lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				prev_timestep=_lowerCAmelCase       ,				generator=_lowerCAmelCase			).prev_sample

			__lowercase             =  pred_prev_sample

		__lowercase             =  torch.sum(torch.abs(_lowerCAmelCase			)			)
		__lowercase             =  torch.mean(torch.abs(_lowerCAmelCase			)			)

		assert abs(result_sum.item() - 258.2_044_983			) < 1e-2
		assert abs(result_mean.item() - 0.3_362_038			) < 1e-3







	def  _a   (  self      :							str			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							int			)		-> List[str]:
		"""simple docstring"""







		pass

