



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


a__      :				Dict   	=			logging.get_logger(__name__)

a__      :				List[str]   	=			"▁"

a__      :				Dict   	=			{"vocab_file": "sentencepiece.bpe.model"}

a__      :				List[str]   	=			{
    "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"
        ),
    }
}

a__      :				List[Any]   	=			{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
a__      :				int   	=			["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       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Optional[int]       				=    VOCAB_FILES_NAMES
   A    :  int       				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A    :  Any       				=    PRETRAINED_VOCAB_FILES_MAP
   A    :  List[str]       				=    ["input_ids", "attention_mask"]

   A    :  List[int]       				=    []
   A    :  List[int]       				=    []


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




         """simple docstring"""

         lowercase__     =  AddedToken(lowerCAmelCase			,				lstrip=lowerCAmelCase			,				rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase			,				lowerCAmelCase) else mask_token

         lowercase__     =  {} if sp_model_kwargs is None else sp_model_kwargs

         lowercase__     =  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__(
             src_lang=lowerCAmelCase			,				tgt_lang=lowerCAmelCase			,				eos_token=lowerCAmelCase			,				unk_token=lowerCAmelCase			,				sep_token=lowerCAmelCase			,				cls_token=lowerCAmelCase			,				pad_token=lowerCAmelCase			,				mask_token=lowerCAmelCase			,				sp_model_kwargs=self.sp_model_kwargs			,				**lowerCAmelCase			,				)

         lowercase__     =  spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(str(lowerCAmelCase))
         lowercase__     =  vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         lowercase__     =  {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         lowercase__     =  1

         lowercase__     =  len(self.sp_model)
         lowercase__     =  {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase)
         }
         lowercase__     =  {v: k for k, v in self.lang_code_to_id.items()}
         lowercase__     =  len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
         lowercase__     =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

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


   @property
   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token


   @property
   def 			UpperCAmelCase		(						self  :			str)	->				str:




         """simple docstring"""

         return self._src_lang


   @src_lang.setter
   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str)	->				None:




         """simple docstring"""

         lowercase__     =  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)


   def __getstate__(						self  :			List[str])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.__dict__.copy()
         lowercase__     =  None
         return state


   def __setstate__(						self  :			List[str]			,				lowerCAmelCase  :			Dict)	->				None:




         """simple docstring"""

         lowercase__     =  d

         # for backward compatibility
         if not hasattr(self			,				'sp_model_kwargs'):
               lowercase__     =  {}

         lowercase__     =  spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  {self.convert_ids_to_tokens(lowerCAmelCase): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				List[str]:




         """simple docstring"""

         return self.sp_model.encode(lowerCAmelCase			,				out_type=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				lowerCAmelCase  :			str)	->				int:




         """simple docstring"""

         if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
         lowercase__     =  self.sp_model.PieceToId(lowerCAmelCase)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			int)	->				str:




         """simple docstring"""

         if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			Optional[Any])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  []
         lowercase__     =  ''
         lowercase__     =  False
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     if not prev_is_special:
                           out_string += " "
                     out_string += self.sp_model.decode(lowerCAmelCase) + token
                     lowercase__     =  True
                     lowercase__     =  []
               else:
                     current_sub_tokens.append(lowerCAmelCase)
                     lowercase__     =  False
         out_string += self.sp_model.decode(lowerCAmelCase)
         return out_string.strip()


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str] = None)	->				Tuple[str]:




         """simple docstring"""

         if not os.path.isdir(lowerCAmelCase):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
               return
         lowercase__     =  os.path.join(
             lowerCAmelCase			,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

         if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase) and os.path.isfile(self.vocab_file):
               copyfile(self.vocab_file			,				lowerCAmelCase)
         elif not os.path.isfile(self.vocab_file):
               with open(lowerCAmelCase			,				'wb') as fi:
                     lowercase__     =  self.sp_model.serialized_model_proto()
                     fi.write(lowerCAmelCase)

         return (out_vocab_file,)


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None			,				lowerCAmelCase  :			bool = False)	->				List[int]:




         """simple docstring"""

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCAmelCase			,				token_ids_a=lowerCAmelCase			,				already_has_special_tokens=lowerCAmelCase)

         lowercase__     =  [1] * len(self.prefix_tokens)
         lowercase__     =  [1] * len(self.suffix_tokens)
         if token_ids_a is None:
               return prefix_ones + ([0] * len(lowerCAmelCase)) + suffix_ones
         return prefix_ones + ([0] * len(lowerCAmelCase)) + ([0] * len(lowerCAmelCase)) + suffix_ones


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			List[int]			,				lowerCAmelCase  :			Optional[List[int]] = None)	->				List[int]:




         """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 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[str]			,				lowerCAmelCase  :			Optional[str]			,				**lowerCAmelCase  :			Optional[Any])	->				List[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(lowerCAmelCase			,				add_special_tokens=lowerCAmelCase			,				return_tensors=lowerCAmelCase			,				**lowerCAmelCase)
         lowercase__     =  self.convert_tokens_to_ids(lowerCAmelCase)
         lowercase__     =  tgt_lang_id
         return inputs


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			str = "en_XX"			,				lowerCAmelCase  :			Optional[List[str]] = None			,				lowerCAmelCase  :			str = "ro_RO"			,				**lowerCAmelCase  :			Optional[Any]			,				)	->				BatchEncoding:




         """simple docstring"""

         lowercase__     =  src_lang
         lowercase__     =  tgt_lang
         return super().prepare_seqaseq_batch(lowerCAmelCase			,				lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				List[str]:




         """simple docstring"""

         return self.set_src_lang_special_tokens(self.src_lang)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Any:




         """simple docstring"""

         return self.set_tgt_lang_special_tokens(self.tgt_lang)


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			str)	->				None:




         """simple docstring"""

         lowercase__     =  self.lang_code_to_id[src_lang]
         lowercase__     =  [self.cur_lang_code_id]
         lowercase__     =  [self.eos_token_id]



   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			str)	->				None:




         """simple docstring"""

         lowercase__     =  self.lang_code_to_id[tgt_lang]
         lowercase__     =  [self.cur_lang_code_id]
         lowercase__     =  [self.eos_token_id]





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  str       				=    (DDIMParallelScheduler,)
   A    :  Any       				=    (("eta", 0.0), ("num_inference_steps", 50))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(**lowerCAmelCase)
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter

         scheduler.set_timesteps(lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

         for timesteps in [1_00, 5_00, 10_00]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps			,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,				[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=lowerCAmelCase			,				beta_end=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

         for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

         for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

         self.check_over_configs(thresholding=lowerCAmelCase)
         for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(
                         thresholding=lowerCAmelCase			,				prediction_type=lowerCAmelCase			,				sample_max_value=lowerCAmelCase			,				)


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[Any]:




         """simple docstring"""

         for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				int:




         """simple docstring"""

         for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 5_00]):
               self.check_over_forward(time_step=lowerCAmelCase			,				num_inference_steps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         for t, eta in zip([1, 10, 49]			,				[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=lowerCAmelCase			,				eta=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20			,				4_00) - 0.1_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80			,				9_60) - 0.3_24_60)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87			,				4_86) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99			,				9_98) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0
         scheduler.set_timesteps(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  self.dummy_sample_deter + 0.1
         lowercase__     =  self.dummy_sample_deter - 0.1

         lowercase__     =  samplea.shape[0]
         lowercase__     =  torch.stack([samplea, samplea, samplea]			,				dim=0)
         lowercase__     =  torch.arange(lowerCAmelCase)[0:3, None].repeat(1			,				lowerCAmelCase)

         lowercase__     =  model(samples.flatten(0			,				1)			,				timesteps.flatten(0			,				1))
         lowercase__     =  scheduler.batch_step_no_noise(lowerCAmelCase			,				timesteps.flatten(0			,				1)			,				samples.flatten(0			,				1)			,				lowerCAmelCase)

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 11_47.79_04) < 1E-2
         assert abs(result_mean.item() - 0.49_82) < 1E-3


   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  self.full_loop()

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3



   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3
