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

lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)


lowerCAmelCase			:							List[str]     ={
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCAmelCase			:							List[str]     ={
    "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"
    },
}

lowerCAmelCase			:							Any     ={"facebook/blenderbot-3B": 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						A__  (					):







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







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						set()
    _lowerCamelCase     :		Tuple 						=						word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)	)
        _lowerCamelCase     :		Tuple 						=						char
    return pairs




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       ['input_ids', 'attention_mask']



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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else bos_token
          _lowerCamelCase     :		Union[str, Any] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else eos_token
          _lowerCamelCase     :		Optional[int] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else sep_token
          _lowerCamelCase     :		Union[str, Any] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else cls_token
          _lowerCamelCase     :		Tuple 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else unk_token
          _lowerCamelCase     :		str 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          _lowerCamelCase     :		Union[str, Any] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else mask_token

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

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

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          _lowerCamelCase     :		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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->str:


          """simple docstring"""


          return len(self.encoder)



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


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Tuple)				->Optional[Any]:


          """simple docstring"""


          if token in self.cache:
              return self.cache[token]
          _lowerCamelCase     :		List[str] 						=						tuple(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						get_pairs(_UpperCamelCase)

          if not pairs:
              return token

          while True:
              _lowerCamelCase     :		List[Any] 						=						min(_UpperCamelCase    ,  key=lambda _UpperCamelCase: self.bpe_ranks.get(_UpperCamelCase    ,  float("""inf""")))
              if bigram not in self.bpe_ranks:
                  break
              _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						bigram
              _lowerCamelCase     :		Dict 						=						[]
              _lowerCamelCase     :		str 						=						0
              while i < len(_UpperCamelCase):
                  try:
                      _lowerCamelCase     :		int 						=						word.index(_UpperCamelCase    ,  _UpperCamelCase)
                  except ValueError:
                      new_word.extend(word[i:])
                      break
                  else:
                      new_word.extend(word[i:j])
                      _lowerCamelCase     :		List[str] 						=						j

                  if word[i] == first and i < len(_UpperCamelCase) - 1 and word[i + 1] == second:
                      new_word.append(first + second)
                      i += 2
                  else:
                      new_word.append(word[i])
                      i += 1
              _lowerCamelCase     :		Any 						=						tuple(_UpperCamelCase)
              _lowerCamelCase     :		List[str] 						=						new_word
              if len(_UpperCamelCase) == 1:
                  break
              else:
                  _lowerCamelCase     :		Optional[Any] 						=						get_pairs(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						""" """.join(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						word
          return word



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[Any])				->str:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						[]
          for token in re.findall(self.pat    ,  _UpperCamelCase):
              _lowerCamelCase     :		Tuple 						=						"""""".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(_UpperCamelCase).split(""" """))
          return bpe_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Optional[int])				->Optional[int]:


          """simple docstring"""


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



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


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		str 						=						"""""".join(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""    ,  errors=self.errors)
          return text



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
              return
          _lowerCamelCase     :		Union[str, Any] 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
          _lowerCamelCase     :		Dict 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

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

          _lowerCamelCase     :		Optional[Any] 						=						0
          with open(_UpperCamelCase    ,  """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 _UpperCamelCase: 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!""")
                      _lowerCamelCase     :		Optional[Any] 						=						token_index
                  writer.write(""" """.join(_UpperCamelCase) + """\n""")
                  index += 1

          return vocab_file, merge_file



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None    ,  _UpperCamelCase     :				bool = False)				->List[int]:


          """simple docstring"""


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

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						[self.sep_token_id]
          _lowerCamelCase     :		str 						=						[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 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[Any]=False    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						kwargs.pop("""add_prefix_space"""    ,  self.add_prefix_space)
          if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase) > 0 and not text[0].isspace()):
              _lowerCamelCase     :		List[str] 						=						""" """ + text
          return (text, kwargs)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->Any:


          """simple docstring"""


          return token_ids_a + [self.eos_token_id]




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				"Conversation")				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						[]
          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(_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""  """.join(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						self.encode(_UpperCamelCase)
          if len(_UpperCamelCase) > self.model_max_length:
              _lowerCamelCase     :		List[str] 						=						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





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase			:							Any     =logging.get_logger(__name__)

lowerCAmelCase			:							List[Any]     ={
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'swin'

      _snake_case    		=       {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				List[str]=224    ,  _UpperCamelCase     :				List[str]=4    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				Dict=96    ,  _UpperCamelCase     :				Any=[2, 2, 6, 2]    ,  _UpperCamelCase     :				Any=[3, 6, 12, 24]    ,  _UpperCamelCase     :				Tuple=7    ,  _UpperCamelCase     :				Tuple=4.0    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Tuple=0.0    ,  _UpperCamelCase     :				Any=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Any="gelu"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				Dict=1E-5    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				Optional[Any]=None    ,  _UpperCamelCase     :				List[Any]=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->Tuple:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		Tuple 						=						patch_size
          _lowerCamelCase     :		Dict 						=						num_channels
          _lowerCamelCase     :		Union[str, Any] 						=						embed_dim
          _lowerCamelCase     :		str 						=						depths
          _lowerCamelCase     :		str 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						num_heads
          _lowerCamelCase     :		Tuple 						=						window_size
          _lowerCamelCase     :		int 						=						mlp_ratio
          _lowerCamelCase     :		Optional[int] 						=						qkv_bias
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Tuple 						=						drop_path_rate
          _lowerCamelCase     :		List[str] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						use_absolute_embeddings
          _lowerCamelCase     :		int 						=						layer_norm_eps
          _lowerCamelCase     :		str 						=						initializer_range
          _lowerCamelCase     :		Dict 						=						encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          _lowerCamelCase     :		int 						=						int(embed_dim * 2 ** (len(_UpperCamelCase) - 1))
          _lowerCamelCase     :		Dict 						=						["""stem"""] + [F"""stage{idx}""" for idx in range(1    ,  len(_UpperCamelCase) + 1)]
          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						get_aligned_output_features_output_indices(
              out_features=_UpperCamelCase    ,  out_indices=_UpperCamelCase    ,  stage_names=self.stage_names)





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       version.parse('1.11'		)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ])




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->float:


          """simple docstring"""


          return 1E-4




