



"""simple docstring"""




import json
import os
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

_UpperCamelCase    							=  logging.get_logger(__name__)

_UpperCamelCase    							=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

_UpperCamelCase    							=  {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

_UpperCamelCase    							=  {
    '''microsoft/deberta-base''': 5_1_2,
    '''microsoft/deberta-large''': 5_1_2,
    '''microsoft/deberta-xlarge''': 5_1_2,
    '''microsoft/deberta-base-mnli''': 5_1_2,
    '''microsoft/deberta-large-mnli''': 5_1_2,
    '''microsoft/deberta-xlarge-mnli''': 5_1_2,
}

_UpperCamelCase    							=  {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}


def   lowercase__     (			)     ->			int:






      '''simple docstring'''


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


def   lowercase__     (			_UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


      lowercase  :      List[str]					=   set()
      lowercase  :      List[Any]					=   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)				)
            lowercase  :      Union[str, Any]					=   char
      return pairs





class   a__		(						lowercase__     ):
   _lowerCamelCase												=				VOCAB_FILES_NAMES
   _lowerCamelCase												=				PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase												=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase												=				["""input_ids""", """attention_mask""", """token_type_ids"""]


   def __init__(							self      :							Union[str, Any],     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							int,     lowerCAmelCase      :							Optional[Any]="replace",     lowerCAmelCase      :							Optional[int]="[CLS]",     lowerCAmelCase      :							Optional[Any]="[SEP]",     lowerCAmelCase      :							Optional[int]="[SEP]",     lowerCAmelCase      :							int="[CLS]",     lowerCAmelCase      :							str="[UNK]",     lowerCAmelCase      :							Union[str, Any]="[PAD]",     lowerCAmelCase      :							Union[str, Any]="[MASK]",     lowerCAmelCase      :							Optional[Any]=False,     lowerCAmelCase      :							int=False,     **lowerCAmelCase      :							Any,     )    ->       Optional[int]:
         lowercase  :      int					=   AddedToken(_A,     lstrip=_A,     rstrip=_A			) if isinstance(_A,     _A			) else bos_token
         lowercase  :      Tuple					=   AddedToken(_A,     lstrip=_A,     rstrip=_A			) if isinstance(_A,     _A			) else eos_token
         lowercase  :      List[str]					=   AddedToken(_A,     lstrip=_A,     rstrip=_A			) if isinstance(_A,     _A			) else sep_token
         lowercase  :      Optional[int]					=   AddedToken(_A,     lstrip=_A,     rstrip=_A			) if isinstance(_A,     _A			) else cls_token
         lowercase  :      List[str]					=   AddedToken(_A,     lstrip=_A,     rstrip=_A			) if isinstance(_A,     _A			) else unk_token
         lowercase  :      str					=   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
         lowercase  :      Optional[int]					=   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,     add_bos_token=_A,     **_A,     )
         lowercase  :      Optional[int]					=   add_bos_token

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

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         lowercase  :      Union[str, Any]					=   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.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def        lowercase				(							self      :							str			)    ->       List[Any]:
         return len(self.encoder			)


   def        lowercase				(							self      :							Tuple			)    ->       Any:
         return dict(self.encoder,     **self.added_tokens_encoder			)


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							Union[str, Any]			)    ->       str:
         if token in self.cache:
               return self.cache[token]
         lowercase  :      Tuple					=   tuple(_A			)
         lowercase  :      List[Any]					=   get_pairs(_A			)

         if not pairs:
               return token

         while True:
               lowercase  :      Any					=   min(_A,     key=lambda lowerCAmelCase			: self.bpe_ranks.get(_A,     float('inf'			)			)			)
               if bigram not in self.bpe_ranks:
                     break
               lowercase  :      List[Any]					=   bigram
               lowercase  :      Dict					=   []
               lowercase  :      str					=   0
               while i < len(_A			):
                     try:
                           lowercase  :      Any					=   word.index(_A,     _A			)
                     except ValueError:
                           new_word.extend(word[i:]			)
                           break
                     else:
                           new_word.extend(word[i:j]			)
                           lowercase  :      List[Any]					=   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
               lowercase  :      Union[str, Any]					=   tuple(_A			)
               lowercase  :      int					=   new_word
               if len(_A			) == 1:
                     break
               else:
                     lowercase  :      str					=   get_pairs(_A			)
         lowercase  :      Optional[Any]					=   ' '.join(_A			)
         lowercase  :      int					=   word
         return word


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None			)    ->       List[int]:
         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase  :      List[Any]					=   [self.cls_token_id]
         lowercase  :      int					=   [self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None,     lowerCAmelCase      :							bool = False			)    ->       List[int]:
         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] + ([0] * len(_A			)) + [1]


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							List[int],     lowerCAmelCase      :							Optional[List[int]] = None			)    ->       List[int]:
         lowercase  :      Optional[Any]					=   [self.sep_token_id]
         lowercase  :      Tuple					=   [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			) * [0] + len(token_ids_a + sep			) * [1]


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							Optional[int]			)    ->       Any:
         lowercase  :      str					=   []
         for token in re.findall(self.pat,     _A			):
               lowercase  :      Union[str, Any]					=   ''.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        lowercase				(							self      :							Dict,     lowerCAmelCase      :							Optional[Any]			)    ->       str:
         return self.encoder.get(_A,     self.encoder.get(self.unk_token			)			)


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Dict			)    ->       List[str]:
         return self.decoder.get(_A			)


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Dict			)    ->       List[str]:
         lowercase  :      int					=   ''.join(_A			)
         lowercase  :      List[Any]					=   bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8',     errors=self.errors			)
         return text


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							str,     lowerCAmelCase      :							Optional[str] = None			)    ->       Tuple[str]:
         if not os.path.isdir(_A			):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
         lowercase  :      Tuple					=   os.path.join(
             _A,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
         lowercase  :      List[str]					=   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'			)

         lowercase  :      Any					=   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 lowerCAmelCase			: 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!'			)
                           lowercase  :      List[str]					=   token_index
                     writer.write(' '.join(_A			) + '\n'			)
                     index += 1

         return vocab_file, merge_file


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							Union[str, Any]=False,     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      Any					=   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()):
               lowercase  :      List[Any]					=   ' ' + text
         return (text, kwargs)






   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							"Conversation"			)    ->       List[int]:
         lowercase  :      List[str]					=   []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(_A,     add_special_tokens=_A			) + [self.eos_token_id]			)
         if len(_A			) > self.model_max_length:
               lowercase  :      Optional[Any]					=   input_ids[-self.model_max_length :]
         return input_ids





"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2
