


'''simple docstring'''

import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)


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

lowercase__				     =		{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowercase__				     =		{"facebook/blenderbot_small-90M": 512}






def __UpperCamelCase   (			__lowerCamelCase		: Tuple				)      ->     List[str]:







     '''simple docstring'''






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

     _a						=		set(__lowerCamelCase				)
     return pairs






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				VOCAB_FILES_NAMES
     UpperCAmelCase 					=				PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase 					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase 					=				['''input_ids''', '''attention_mask''']






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase="__start__"     ,							__UpperCamelCase="__end__"     ,							__UpperCamelCase="__unk__"     ,							__UpperCamelCase="__null__"     ,							**__UpperCamelCase     ,							)       ->    str:
          super().__init__(unk_token=__UpperCamelCase     ,							bos_token=__UpperCamelCase     ,							eos_token=__UpperCamelCase     ,							pad_token=__UpperCamelCase     ,							**__UpperCamelCase		)

          with open(__UpperCamelCase     ,							encoding="utf-8"		) as vocab_handle:
               _a						=		json.load(__UpperCamelCase		)
          _a						=		{v: k for k, v in self.encoder.items()}
          with open(__UpperCamelCase     ,							encoding="utf-8"		) as merges_handle:
               _a						=		merges_handle.read().split("\n"		)[1:-1]
          _a						=		[tuple(merge.split()		) for merge in merges]
          _a						=		dict(zip(__UpperCamelCase     ,							range(len(__UpperCamelCase		)		)		)		)
          _a						=		{}






     @property
     def  a_					(			self		)       ->    int:
          return len(self.encoder		)






     def  a_					(			self		)       ->    Dict:
          return dict(self.encoder     ,							**self.added_tokens_encoder		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    str:
          if token in self.cache:
               return self.cache[token]
          _a						=		re.sub("([.,!?()])"     ,							R" \1"     ,							__UpperCamelCase		)
          _a						=		re.sub("(')"     ,							R" \1 "     ,							__UpperCamelCase		)
          _a						=		re.sub(R"\s{2,}"     ,							" "     ,							__UpperCamelCase		)
          if "\n" in token:
               _a						=		token.replace("\n"     ,							" __newln__"		)

          _a						=		token.split(" "		)
          _a						=		[]
          for token in tokens:
               if not len(__UpperCamelCase		):
                    continue

               _a						=		token.lower()
               _a						=		tuple(__UpperCamelCase		)
               _a						=		tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
               _a						=		get_pairs(__UpperCamelCase		)

               if not pairs:
                    words.append(__UpperCamelCase		)
                    continue

               while True:
                    _a						=		min(__UpperCamelCase     ,							key=lambda __UpperCamelCase		: self.bpe_ranks.get(__UpperCamelCase     ,							float("inf"		)		)		)
                    if bigram not in self.bpe_ranks:
                         break
                    _a			,			_a						=		bigram
                    _a						=		[]
                    _a						=		0

                    while i < len(__UpperCamelCase		):
                         try:
                              _a						=		word.index(__UpperCamelCase     ,							__UpperCamelCase		)
                              new_word.extend(word[i:j]		)
                              _a						=		j
                         except ValueError:
                              new_word.extend(word[i:]		)
                              break

                         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
                    _a						=		tuple(__UpperCamelCase		)
                    _a						=		new_word
                    if len(__UpperCamelCase		) == 1:
                         break
                    else:
                         _a						=		get_pairs(__UpperCamelCase		)
               _a						=		"@@ ".join(__UpperCamelCase		)
               _a						=		word[:-4]

               _a						=		word
               words.append(__UpperCamelCase		)
          return " ".join(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		[]

          _a						=		re.findall(R"\S+\n?"     ,							__UpperCamelCase		)

          for token in words:
               split_tokens.extend(list(self.bpe(__UpperCamelCase		).split(" "		)		)		)
          return split_tokens






     def  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          _a						=		token.lower()
          return self.encoder.get(__UpperCamelCase     ,							self.encoder.get(self.unk_token		)		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    str:
          return self.decoder.get(__UpperCamelCase     ,							self.unk_token		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    str:
          _a						=		" ".join(__UpperCamelCase		).replace("@@ "     ,							""		).strip()
          return out_string






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    Tuple[str]:
          if not os.path.isdir(__UpperCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          _a						=		os.path.join(
              __UpperCamelCase     ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
          _a						=		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"		)

          _a						=		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!"		)
                         _a						=		token_index
                    writer.write(" ".join(__UpperCamelCase		) + "\n"		)
                    index += 1

          return vocab_file, merge_file





'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,							__UpperCamelCase     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."		)
          if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."		)

          super().__init__(__UpperCamelCase     ,							__UpperCamelCase		)






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    BatchEncoding:
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   "You cannot provide bounding boxes "
                   "if you initialized the image processor with apply_ocr set to True."		)

          if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

          # first, apply the image processor
          _a						=		self.image_processor(images=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _a						=		features["words"]

          _a						=		self.tokenizer(
              text=text if text is not None else features["words"]     ,							text_pair=text_pair if text_pair is not None else None     ,							boxes=boxes if boxes is not None else features["boxes"]     ,							word_labels=__UpperCamelCase     ,							add_special_tokens=__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							stride=__UpperCamelCase     ,							pad_to_multiple_of=__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							return_overflowing_tokens=__UpperCamelCase     ,							return_special_tokens_mask=__UpperCamelCase     ,							return_offsets_mapping=__UpperCamelCase     ,							return_length=__UpperCamelCase     ,							verbose=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          # add pixel values
          _a						=		features.pop("pixel_values"		)
          if return_overflowing_tokens is True:
               _a						=		self.get_overflowing_images(__UpperCamelCase     ,							encoded_inputs["overflow_to_sample_mapping"]		)
          _a						=		images

          return encoded_inputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a						=		[]
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]		)

          if len(__UpperCamelCase		) != len(__UpperCamelCase		):
               raise ValueError(
                   "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                   f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

          return images_with_overflow






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def  a_					(			self		)       ->    Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor_class






     @property
     def  a_					(			self		)       ->    int:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor

