






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__snake_case												=  logging.get_logger(__name__)

__snake_case												=  """▁"""

__snake_case												=  {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case												=  {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

__snake_case												=  {
    """facebook/m2m100_418M""": 10_24,
}

# fmt: off
__snake_case												=  {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Optional[int]           =VOCAB_FILES_NAMES
      UpperCamelCase_				:					Dict           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_				:					Union[str, Any]           =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_				:					Any           =['input_ids', 'attention_mask']

      UpperCamelCase_				:					Optional[int]           =[]
      UpperCamelCase_				:					int           =[]
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_="<s>"    ,				SCREAMING_SNAKE_CASE_="</s>"    ,				SCREAMING_SNAKE_CASE_="</s>"    ,				SCREAMING_SNAKE_CASE_="<pad>"    ,				SCREAMING_SNAKE_CASE_="<unk>"    ,				SCREAMING_SNAKE_CASE_="m2m100"    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_=8    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      str:
          UpperCamelCase       :Optional[int]								=   {} if sp_model_kwargs is None else sp_model_kwargs

          UpperCamelCase       :Any								=   language_codes
          UpperCamelCase       :str								=   FAIRSEQ_LANGUAGE_CODES[language_codes]
          UpperCamelCase       :Any								=   {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

          UpperCamelCase       :Dict								=   kwargs.get('''additional_special_tokens'''    ,				[] )
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(__lowerCAmelCase )
              for lang_code in fairseq_language_code
              if self.get_lang_token(__lowerCAmelCase ) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=__lowerCAmelCase    ,				tgt_lang=__lowerCAmelCase    ,				bos_token=__lowerCAmelCase    ,				eos_token=__lowerCAmelCase    ,				sep_token=__lowerCAmelCase    ,				unk_token=__lowerCAmelCase    ,				pad_token=__lowerCAmelCase    ,				language_codes=__lowerCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				num_madeup_words=__lowerCAmelCase    ,				**__lowerCAmelCase    ,				)

          UpperCamelCase       :Any								=   vocab_file
          UpperCamelCase       :Union[str, Any]								=   load_json(__lowerCAmelCase )
          UpperCamelCase       :str								=   {v: k for k, v in self.encoder.items()}
          UpperCamelCase       :str								=   spm_file
          UpperCamelCase       :Union[str, Any]								=   load_spm(__lowerCAmelCase    ,				self.sp_model_kwargs )

          UpperCamelCase       :Tuple								=   len(self.encoder )

          UpperCamelCase       :Any								=   {
              self.get_lang_token(__lowerCAmelCase ): self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase )
          }
          UpperCamelCase       :Any								=   {lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCAmelCase )}
          UpperCamelCase       :Dict								=   {v: k for k, v in self.lang_token_to_id.items()}

          UpperCamelCase       :Union[str, Any]								=   src_lang if src_lang is not None else '''en'''
          UpperCamelCase       :Optional[int]								=   tgt_lang
          UpperCamelCase       :str								=   self.get_lang_id(self._src_lang )
          self.set_src_lang_special_tokens(self._src_lang )

          UpperCamelCase       :Optional[int]								=   num_madeup_words
      @property
      def     UpperCAmelCase						(				self )				->      List[str]:
          return len(self.encoder ) + len(self.lang_token_to_id )
      @property
      def     UpperCAmelCase						(				self )				->      Tuple:
          return self._src_lang
      @src_lang.setter
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Tuple:
          UpperCamelCase       :Optional[int]								=   new_src_lang
          self.set_src_lang_special_tokens(self._src_lang )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Tuple:
          return self.sp_model.encode(__lowerCAmelCase    ,				out_type=__lowerCAmelCase )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:







          if token in self.lang_token_to_id:
              return self.lang_token_to_id[token]
          return self.encoder.get(__lowerCAmelCase    ,				self.encoder[self.unk_token] )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Any:







          if index in self.id_to_lang_token:
              return self.id_to_lang_token[index]
          return self.decoder.get(__lowerCAmelCase    ,				self.unk_token )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      str:
          UpperCamelCase       :List[str]								=   []
          UpperCamelCase       :Any								=   ''''''
          for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                  out_string += self.sp_model.decode(__lowerCAmelCase ) + token
                  UpperCamelCase       :Optional[Any]								=   []
              else:
                  current_sub_tokens.append(__lowerCAmelCase )
          out_string += self.sp_model.decode(__lowerCAmelCase )
          return out_string.strip()
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = False )				->      Dict:







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

          UpperCamelCase       :Any								=   [1] * len(self.prefix_tokens )
          UpperCamelCase       :List[str]								=   [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    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      Union[str, Any]:







          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 )				->      Tuple:
          UpperCamelCase       :int								=   {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(				self )				->      List[Any]:
          UpperCamelCase       :Tuple								=   self.__dict__.copy()
          UpperCamelCase       :Dict								=   None
          return state
      def __setstate__(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          UpperCamelCase       :Dict								=   d

          # for backward compatibility
          if not hasattr(self    ,				'''sp_model_kwargs''' ):
              UpperCamelCase       :Optional[Any]								=   {}

          UpperCamelCase       :str								=   load_spm(self.spm_file    ,				self.sp_model_kwargs )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      List[str]:
          UpperCamelCase       :str								=   Path(__lowerCAmelCase )
          if not save_dir.is_dir():
              raise OSError(F'''{save_directory} should be a directory''' )
          UpperCamelCase       :Optional[int]								=   save_dir / (
              (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
          )
          UpperCamelCase       :Optional[Any]								=   save_dir / (
              (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
          )

          save_json(self.encoder    ,				__lowerCAmelCase )

          if os.path.abspath(self.spm_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.spm_file ):
              copyfile(self.spm_file    ,				__lowerCAmelCase )
          elif not os.path.isfile(self.spm_file ):
              with open(__lowerCAmelCase    ,				'''wb''' ) as fi:
                  UpperCamelCase       :List[str]								=   self.sp_model.serialized_model_proto()
                  fi.write(__lowerCAmelCase )

          return (str(__lowerCAmelCase ), str(__lowerCAmelCase ))
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = "en"    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = "ro"    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Union[str, Any]:
          UpperCamelCase       :Optional[int]								=   src_lang
          UpperCamelCase       :Any								=   tgt_lang
          self.set_src_lang_special_tokens(self.src_lang )
          return super().prepare_seqaseq_batch(__lowerCAmelCase    ,				__lowerCAmelCase    ,				**__lowerCAmelCase )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:







          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       :Dict								=   src_lang
          UpperCamelCase       :int								=   self(__lowerCAmelCase    ,				add_special_tokens=__lowerCAmelCase    ,				**__lowerCAmelCase )
          UpperCamelCase       :Optional[Any]								=   self.get_lang_id(__lowerCAmelCase )
          UpperCamelCase       :int								=   tgt_lang_id
          return inputs
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          self.set_src_lang_special_tokens(self.src_lang )
      def     UpperCAmelCase						(				self )				->      Any:
          self.set_tgt_lang_special_tokens(self.tgt_lang )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:
          UpperCamelCase       :Tuple								=   self.get_lang_token(__lowerCAmelCase )
          UpperCamelCase       :int								=   self.lang_token_to_id[lang_token]
          UpperCamelCase       :int								=   [self.cur_lang_id]
          UpperCamelCase       :Any								=   [self.eos_token_id]
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Any:
          UpperCamelCase       :Union[str, Any]								=   self.get_lang_token(__lowerCAmelCase )
          UpperCamelCase       :Union[str, Any]								=   self.lang_token_to_id[lang_token]
          UpperCamelCase       :int								=   [self.cur_lang_id]
          UpperCamelCase       :Optional[Any]								=   [self.eos_token_id]
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Tuple:
          return self.lang_code_to_token[lang]




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Any:
          UpperCamelCase       :Optional[Any]								=   self.get_lang_token(__lowerCAmelCase )
          return self.lang_token_to_id[lang_token]







def   _A   (				SCREAMING_SNAKE_CASE__  :	Any     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]       ):
    UpperCamelCase       :Any								=   sentencepiece.SentencePieceProcessor(**__snake_case       )
    spm.Load(str(__snake_case       )       )
    return spm


def   _A   (				SCREAMING_SNAKE_CASE__  :	List[Any]       ):







    with open(__snake_case     ,  '''r'''       ) as f:
        return json.load(__snake_case       )



def   _A   (				SCREAMING_SNAKE_CASE__  :	Optional[int]     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]       ):







    with open(__snake_case     ,  '''w'''       ) as f:
        json.dump(__snake_case     ,  __snake_case     ,  indent=2       )












import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						(UnCLIPScheduler,)





			def 		__lowerCamelCase				(    self       ,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''num_train_timesteps''': 1_0_0_0,
				    '''variance_type''': '''fixed_small_log''',
				    '''clip_sample''': True,
				    '''clip_sample_range''': 1.0,
				    '''prediction_type''': '''epsilon''',
				}

				config.update(**__lowerCAmelCase							)
				return config





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for clip_sample_range in [1, 5, 1_0, 2_0]:
					self.check_over_configs(clip_sample_range=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				for time_step in [0, 5_0_0, 9_9_9]:
					for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
						if prev_timestep is not None and prev_timestep >= time_step:
							continue

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''fixed_small_log'''							)
				lowerCamelCase__								=		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(4_8_7							) - 0.054_9625							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.999_4987							)							) < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config(variance_type='''learned_range'''							)
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		0.5

				assert scheduler._get_variance(1       ,  predicted_variance=__lowerCAmelCase							) - -10.171_2790 < 1E-5
				assert scheduler._get_variance(4_8_7       ,  predicted_variance=__lowerCAmelCase							) - -5.799_8052 < 1E-5
				assert scheduler._get_variance(9_9_9       ,  predicted_variance=__lowerCAmelCase							) - -0.001_0011 < 1E-5





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				lowerCamelCase__								=		scheduler.timesteps

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

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

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

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 252.268_2495							) < 1E-2
				assert abs(result_mean.item() - 0.328_4743							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.scheduler_classes[0]
				lowerCamelCase__								=		self.get_scheduler_config()
				lowerCamelCase__								=		scheduler_class(**__lowerCAmelCase							)

				scheduler.set_timesteps(2_5							)

				lowerCamelCase__								=		scheduler.timesteps

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

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

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

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

					lowerCamelCase__								=		pred_prev_sample

				lowerCamelCase__								=		torch.sum(torch.abs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		torch.mean(torch.abs(__lowerCAmelCase							)							)

				assert abs(result_sum.item() - 258.204_4983							) < 1E-2
				assert abs(result_mean.item() - 0.336_2038							) < 1E-3





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass



