


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

__snake_case									=		logging.get_logger(__name__)


__snake_case									=		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__snake_case									=		{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__snake_case									=		{"facebook/blenderbot-3B": 1_2_8}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						_A						(						) ->      Any:


     """simple docstring"""


     __UpperCamelCase      =       (
         list(range(ord('!'      ) ,   ord('~'      ) + 1      )      ) + list(range(ord('¡'      ) ,   ord('¬'      ) + 1      )      ) + list(range(ord('®'      ) ,   ord('ÿ'      ) + 1      )      )
     )
     __UpperCamelCase      =       bs[:]
     __UpperCamelCase      =       0
     for b in range(2**8      ):
          if b not in bs:
               bs.append(A__      )
               cs.append(2**8 + n      )
               n += 1
     __UpperCamelCase      =       [chr(A__      ) for n in cs]
     return dict(zip(A__ ,   A__      )      )






def 						_A						(						_lowercase      ) ->      Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase      =       set()
     __UpperCamelCase      =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)      )
          __UpperCamelCase      =       char
     return pairs



class      __lowerCamelCase       (_SCREAMING_SNAKE_CASE		):
       _lowercase											=			VOCAB_FILES_NAMES
       _lowercase											=			PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=			["""input_ids""", """attention_mask"""]







       def __init__(       self:      List[Any],A_:      int,A_:      Optional[Any],A_:      Optional[int]="replace",A_:      Dict="<s>",A_:      Tuple="</s>",A_:      Optional[Any]="</s>",A_:      List[Any]="<s>",A_:      List[str]="<unk>",A_:      Optional[Any]="<pad>",A_:      List[str]="<mask>",A_:      List[Any]=False,**A_:      Optional[Any],):






            '''simple docstring'''




            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else bos_token
            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else eos_token
            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else sep_token
            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else cls_token
            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else unk_token
            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else pad_token

            # Mask token behave like a normal word, i.e. include the space before it
            __UpperCamelCase      =       AddedToken(A_,lstrip=A_,rstrip=A_ ) if isinstance(A_,A_ ) else mask_token

            super().__init__(
                errors=A_,bos_token=A_,eos_token=A_,unk_token=A_,sep_token=A_,cls_token=A_,pad_token=A_,mask_token=A_,add_prefix_space=A_,**A_,)

            with open(A_,encoding='utf-8' ) as vocab_handle:
                 __UpperCamelCase      =       json.load(A_ )
            __UpperCamelCase      =       {v: k for k, v in self.encoder.items()}
            __UpperCamelCase      =       errors  # how to handle errors in decoding
            __UpperCamelCase      =       bytes_to_unicode()
            __UpperCamelCase      =       {v: k for k, v in self.byte_encoder.items()}
            with open(A_,encoding='utf-8' ) as merges_handle:
                 __UpperCamelCase      =       merges_handle.read().split('\n' )[1:-1]
            __UpperCamelCase      =       [tuple(merge.split() ) for merge in bpe_merges]
            __UpperCamelCase      =       dict(zip(A_,range(len(A_ ) ) ) )
            __UpperCamelCase      =       {}
            __UpperCamelCase      =       add_prefix_space

            # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
            __UpperCamelCase      =       re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )







       @property
       # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
       def 						snake_case_     (       self:      int ):






            '''simple docstring'''




            return len(self.encoder )







       def 						snake_case_     (       self:      Any ):






            '''simple docstring'''




            return dict(self.encoder,**self.added_tokens_encoder )







       def 						snake_case_     (       self:      str,A_:      int ):






            '''simple docstring'''




            if token in self.cache:
                 return self.cache[token]
            __UpperCamelCase      =       tuple(A_ )
            __UpperCamelCase      =       get_pairs(A_ )

            if not pairs:
                 return token

            while True:
                 __UpperCamelCase      =       min(A_,key=lambda A_ : self.bpe_ranks.get(A_,float('inf' ) ) )
                 if bigram not in self.bpe_ranks:
                      break
                 __UpperCamelCase      =       bigram
                 __UpperCamelCase      =       []
                 __UpperCamelCase      =       0
                 while i < len(A_ ):
                      try:
                           __UpperCamelCase      =       word.index(A_,A_ )
                      except ValueError:
                           new_word.extend(word[i:] )
                           break
                      else:
                           new_word.extend(word[i:j] )
                           __UpperCamelCase      =       j

                      if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
                           new_word.append(first + second )
                           i += 2
                      else:
                           new_word.append(word[i] )
                           i += 1
                 __UpperCamelCase      =       tuple(A_ )
                 __UpperCamelCase      =       new_word
                 if len(A_ ) == 1:
                      break
                 else:
                      __UpperCamelCase      =       get_pairs(A_ )
            __UpperCamelCase      =       """ """.join(A_ )
            __UpperCamelCase      =       word
            return word







       def 						snake_case_     (       self:      List[Any],A_:      Optional[Any] ):






            '''simple docstring'''




            __UpperCamelCase      =       []
            for token in re.findall(self.pat,A_ ):
                 __UpperCamelCase      =       """""".join(
                     self.byte_encoder[b] for b in token.encode('utf-8' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_ ).split(' ' ) )
            return bpe_tokens







       def 						snake_case_     (       self:      int,A_:      Tuple ):






            '''simple docstring'''




            return self.encoder.get(A_,self.encoder.get(self.unk_token ) )







       def 						snake_case_     (       self:      Union[str, Any],A_:      List[str] ):






            '''simple docstring'''




            return self.decoder.get(A_ )







       def 						snake_case_     (       self:      Union[str, Any],A_:      Any ):






            '''simple docstring'''




            __UpperCamelCase      =       """""".join(A_ )
            __UpperCamelCase      =       bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8',errors=self.errors )
            return text







       def 						snake_case_     (       self:      List[str],A_:      str,A_:      Optional[str] = None ):






            '''simple docstring'''




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

            with open(A_,'w',encoding='utf-8' ) as f:
                 f.write(json.dumps(self.encoder,indent=2,sort_keys=A_,ensure_ascii=A_ ) + '\n' )

            __UpperCamelCase      =       0
            with open(A_,'w',encoding='utf-8' ) as writer:
                 writer.write('#version: 0.2\n' )
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),key=lambda A_ : kv[1] ):
                      if index != token_index:
                           logger.warning(
                               F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                               ' Please check that the tokenizer is not corrupted!' )
                           __UpperCamelCase      =       token_index
                      writer.write(' '.join(A_ ) + '\n' )
                      index += 1

            return vocab_file, merge_file







       def 						snake_case_     (       self:      Dict,A_:      List[int],A_:      Optional[List[int]] = None,A_:      bool = False ):






            '''simple docstring'''




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

            if token_ids_a is None:
                 return [1] + ([0] * len(A_ )) + [1]
            return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]







       def 						snake_case_     (       self:      Optional[Any],A_:      List[int],A_:      Optional[List[int]] = None ):






            '''simple docstring'''




            __UpperCamelCase      =       [self.sep_token_id]
            __UpperCamelCase      =       [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 						snake_case_     (       self:      Any,A_:      Tuple,A_:      str=False,**A_:      Tuple ):






            '''simple docstring'''




            __UpperCamelCase      =       kwargs.pop('add_prefix_space',self.add_prefix_space )
            if (is_split_into_words or add_prefix_space) and (len(A_ ) > 0 and not text[0].isspace()):
                 __UpperCamelCase      =       """ """ + text
            return (text, kwargs)







       def 						snake_case_     (       self:      Any,A_:      List[int],A_:      Optional[List[int]] = None ):






            '''simple docstring'''




            return token_ids_a + [self.eos_token_id]







       def 						snake_case_     (       self:      Dict,A_:      "Conversation" ):






            '''simple docstring'''




            __UpperCamelCase      =       []
            for is_user, text in conversation.iter_texts():
                 if is_user:
                      # We need to space prefix as it's being done within blenderbot
                      inputs.append(' ' + text )
                 else:
                      # Generated responses should contain them already.
                      inputs.append(A_ )

            __UpperCamelCase      =       """  """.join(A_ )
            __UpperCamelCase      =       self.encode(A_ )
            if len(A_ ) > self.model_max_length:
                 __UpperCamelCase      =       input_ids[-self.model_max_length :]
                 logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
            return input_ids






'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					(IPNDMScheduler,)
    lowercase    =					(('num_inference_steps', 50),)

    def __lowercase   (   self    :  Optional[Any]    ,						**lowerCamelCase    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   {"""num_train_timesteps""": 10_00}
      config.update(**lowerCamelCase		)
      return config

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Tuple=0    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      List[str]			=   self.get_scheduler_config(**lowerCamelCase		)
        lowerCAmelCase_			:      int			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      List[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          new_scheduler.set_timesteps(lowerCamelCase		)
          # copy over dummy past residuals
          lowerCAmelCase_			:      str			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      int			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Any			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

    def __lowercase   (   self    :  str		)   ->  Tuple:
      pass

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  int=0    ,						**lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      List[str]			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      List[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Optional[int]			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			:      int			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      Optional[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          # copy over dummy past residuals
          new_scheduler.set_timesteps(lowerCamelCase		)

          # copy over dummy past residual (must be after setting timesteps)
          lowerCAmelCase_			:      Union[str, Any]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[Any]			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      str			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

    def __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  Optional[Any]		)   ->  str:
      lowerCAmelCase_			:      Optional[int]			=   self.scheduler_classes[0]
      lowerCAmelCase_			:      List[Any]			=   self.get_scheduler_config(**lowerCamelCase		)
      lowerCAmelCase_			:      str			=   scheduler_class(**lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   10
      lowerCAmelCase_			:      Optional[int]			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      str			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      return sample

    def __lowercase   (   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      List[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      int			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Tuple			=   self.get_scheduler_config()
        lowerCAmelCase_			:      List[str]			=   scheduler_class(**lowerCamelCase		)

        lowerCAmelCase_			:      int			=   self.dummy_sample
        lowerCAmelCase_			:      Tuple			=   0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          scheduler.set_timesteps(lowerCamelCase		)
        elif num_inference_steps is not None and not hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          lowerCAmelCase_			:      Dict			=   num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        lowerCAmelCase_			:      List[str]			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      List[str]			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      str			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Dict			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        self.assertEqual(output_a.shape    ,						sample.shape		)
        self.assertEqual(output_a.shape    ,						output_a.shape		)

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

        self.assertEqual(output_a.shape    ,						sample.shape		)
        self.assertEqual(output_a.shape    ,						output_a.shape		)

    def __lowercase   (   self    :  Any		)   ->  int:
      for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase    ,						time_step=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]		)   ->  Optional[int]:
      for t, num_inference_steps in zip([1, 5, 10]    ,						[10, 50, 1_00]		):
        self.check_over_forward(num_inference_steps=lowerCamelCase    ,						time_step=lowerCamelCase		)





    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop()
      lowerCAmelCase_			:      Any			=   torch.mean(torch.abs(lowerCamelCase		)		)

      assert abs(result_mean.item() - 2_54_05_29		) < 10



