





import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{
    """allenai/led-base-16384""": 1_6_3_8_4,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def       _A  (      ):
   a__	:						Any      		=  (
       list(range(ord("!"   )							, ord("~"   ) + 1   )   ) + list(range(ord("¡"   )							, ord("¬"   ) + 1   )   ) + list(range(ord("®"   )							, ord("ÿ"   ) + 1   )   )
   )
   a__	:						Union[str, Any]      		=  bs[:]
   a__	:						Tuple      		=  0
   for b in range(2**8   ):
      if b not in bs:
         bs.append(lowerCamelCase   )
         cs.append(2**8 + n   )
         n += 1
   a__	:						Optional[Any]      		=  [chr(lowerCamelCase   ) for n in cs]
   return dict(zip(lowerCamelCase							, lowerCamelCase   )   )






def       _A  (      lowerCamelCase   ):
   a__	:						List[Any]      		=  set()
   a__	:						List[str]      		=  word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)   )
      a__	:						Optional[Any]      		=  char
   return pairs







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						int     		=				PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						int     		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						Optional[Any]     		=				["""input_ids""", """attention_mask"""]





   def __init__( self					,			snake_case					,			snake_case					,			snake_case="replace"					,			snake_case="<s>"					,			snake_case="</s>"					,			snake_case="</s>"					,			snake_case="<s>"					,			snake_case="<unk>"					,			snake_case="<pad>"					,			snake_case="<mask>"					,			snake_case=False					,			**snake_case					,			)				->  Tuple:
      """simple docstring"""





      a__	:						Optional[Any]      		=  AddedToken(snake_case					,			lstrip=snake_case					,			rstrip=snake_case      ) if isinstance(snake_case					,			snake_case      ) else bos_token
      a__	:						str      		=  AddedToken(snake_case					,			lstrip=snake_case					,			rstrip=snake_case      ) if isinstance(snake_case					,			snake_case      ) else eos_token
      a__	:						List[Any]      		=  AddedToken(snake_case					,			lstrip=snake_case					,			rstrip=snake_case      ) if isinstance(snake_case					,			snake_case      ) else sep_token
      a__	:						Union[str, Any]      		=  AddedToken(snake_case					,			lstrip=snake_case					,			rstrip=snake_case      ) if isinstance(snake_case					,			snake_case      ) else cls_token
      a__	:						str      		=  AddedToken(snake_case					,			lstrip=snake_case					,			rstrip=snake_case      ) if isinstance(snake_case					,			snake_case      ) else unk_token
      a__	:						Union[str, Any]      		=  AddedToken(snake_case					,			lstrip=snake_case					,			rstrip=snake_case      ) if isinstance(snake_case					,			snake_case      ) else pad_token

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

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      a__	:						List[str]      		=  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.bart.tokenization_bart.BartTokenizer.vocab_size
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      return len(self.encoder      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





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





   def 	_snake_case ( self					,			snake_case      )				->  Tuple:
      """simple docstring"""





      if token in self.cache:
         return self.cache[token]
      a__	:						Optional[Any]      		=  tuple(snake_case      )
      a__	:						Optional[int]      		=  get_pairs(snake_case      )

      if not pairs:
         return token

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

            if word[i] == first and i < len(snake_case      ) - 1 and word[i + 1] == second:
               new_word.append(first + second      )
               i += 2
            else:
               new_word.append(word[i]      )
               i += 1
         a__	:						str      		=  tuple(snake_case      )
         a__	:						str      		=  new_word
         if len(snake_case      ) == 1:
            break
         else:
            a__	:						Dict      		=  get_pairs(snake_case      )
      a__	:						Optional[int]      		=  " ".join(snake_case      )
      a__	:						int      		=  word
      return word





   def 	_snake_case ( self					,			snake_case      )				->  Tuple:
      """simple docstring"""





      a__	:						Dict      		=  []
      for token in re.findall(self.pat					,			snake_case      ):
         a__	:						Optional[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(snake_case      ).split(" "      )      )
      return bpe_tokens





   def 	_snake_case ( self					,			snake_case      )				->  List[Any]:
      """simple docstring"""





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





   def 	_snake_case ( self					,			snake_case      )				->  List[str]:
      """simple docstring"""





      return self.decoder.get(snake_case      )





   def 	_snake_case ( self					,			snake_case      )				->  Dict:
      """simple docstring"""





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





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  Tuple[str]:
      """simple docstring"""





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

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

      a__	:						Union[str, Any]      		=  0
      with open(snake_case					,			"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 snake_case      : 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!"      )
               a__	:						List[Any]      		=  token_index
            writer.write(" ".join(snake_case      ) + "\n"      )
            index += 1

      return vocab_file, merge_file





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  List[int]:
      """simple docstring"""





      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      a__	:						Dict      		=  [self.cls_token_id]
      a__	:						Tuple      		=  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep





   def 	_snake_case ( self					,			snake_case					,			snake_case = None					,			snake_case = False      )				->  List[int]:
      """simple docstring"""





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

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





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  List[int]:
      """simple docstring"""





      a__	:						int      		=  [self.sep_token_id]
      a__	:						Union[str, Any]      		=  [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					,			snake_case					,			snake_case=False					,			**snake_case      )				->  List[Any]:
      """simple docstring"""





      a__	:						Tuple      		=  kwargs.pop("add_prefix_space"					,			self.add_prefix_space      )
      if (is_split_into_words or add_prefix_space) and (len(snake_case      ) > 0 and not text[0].isspace()):
         a__	:						List[Any]      		=  " " + text
      return (text, kwargs)





   def 	_snake_case ( self					,			snake_case					,			snake_case = None					,			snake_case = PaddingStrategy.DO_NOT_PAD					,			snake_case = None					,			snake_case = None					,			)				->  dict:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  super()._pad(
          encoded_inputs=snake_case					,			max_length=snake_case					,			padding_strategy=snake_case					,			pad_to_multiple_of=snake_case					,			return_attention_mask=snake_case					,			)

      # Load from model defaults
      if return_attention_mask is None:
         a__	:						str      		=  "attention_mask" in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
         a__	:						int      		=  encoded_inputs[self.model_input_names[0]]
         # `global_attention_mask` need to have the same length as other (sequential) inputs.
         a__	:						Optional[Any]      		=  len(encoded_inputs["global_attention_mask"]      ) != len(snake_case      )

         if needs_to_be_padded:
            a__	:						Optional[int]      		=  len(snake_case      ) - len(encoded_inputs["global_attention_mask"]      )

            if self.padding_side == "right":
               # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
               a__	:						Tuple      		=  (
                   encoded_inputs["global_attention_mask"] + [-1] * difference
               )
            elif self.padding_side == "left":
               a__	:						Any      		=  [-1] * difference + encoded_inputs[
                   "global_attention_mask"
               ]
            else:
               raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

      return encoded_inputs








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


SCREAMING_SNAKE_CASE__       :			Dict			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Optional[int]     		=				"""mobilenet_v2"""





   def __init__( self					,			snake_case=3					,			snake_case=224					,			snake_case=1.0					,			snake_case=8					,			snake_case=8					,			snake_case=6					,			snake_case=32					,			snake_case=True					,			snake_case=True					,			snake_case="relu6"					,			snake_case=True					,			snake_case=0.8					,			snake_case=0.02					,			snake_case=0.001					,			snake_case=255					,			**snake_case					,			)				->  int:
      """simple docstring"""





      super().__init__(**snake_case      )

      if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."      )

      a__	:						str      		=  num_channels
      a__	:						Dict      		=  image_size
      a__	:						Any      		=  depth_multiplier
      a__	:						str      		=  depth_divisible_by
      a__	:						Optional[int]      		=  min_depth
      a__	:						Dict      		=  expand_ratio
      a__	:						str      		=  output_stride
      a__	:						Optional[int]      		=  first_layer_is_expansion
      a__	:						Union[str, Any]      		=  finegrained_output
      a__	:						Union[str, Any]      		=  hidden_act
      a__	:						str      		=  tf_padding
      a__	:						List[Any]      		=  classifier_dropout_prob
      a__	:						List[Any]      		=  initializer_range
      a__	:						Optional[Any]      		=  layer_norm_eps
      a__	:						str      		=  semantic_loss_ignore_index







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						Any     		=				version.parse("""1.11"""  )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      return OrderedDict([("pixel_values", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
      """simple docstring"""





      if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]      )
      else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]      )





   @property
   def 	_snake_case ( self      )				->  float:
      """simple docstring"""





      return 1E-4

