






'''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       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  ['''image_processor''', '''tokenizer''']
	_lowerCamelCase              =  '''LayoutLMv3ImageProcessor'''
	_lowerCamelCase              =  ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')

	def __init__(      self   :						Any  ,					UpperCAmelCase_   :						Any=None  ,					UpperCAmelCase_   :						Tuple=None  ,					**UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					str					=					None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'  ,					UpperCAmelCase_  ,					)
					UpperCamelCase__				:					Dict					=					kwargs.pop('feature_extractor')

			UpperCamelCase__				:					Optional[int]					=					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   :						Dict  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,					UpperCAmelCase_   :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,					UpperCAmelCase_   :						Union[List[List[int]], List[List[List[int]]]] = None  ,					UpperCAmelCase_   :						Optional[Union[List[int], List[List[int]]]] = None  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						Union[bool, str, PaddingStrategy] = False  ,					UpperCAmelCase_   :						Union[bool, str, TruncationStrategy] = None  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						int = 0  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						Optional[bool] = None  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = False  ,					UpperCAmelCase_   :						bool = True  ,					UpperCAmelCase_   :						Optional[Union[str, TensorType]] = None  ,					**UpperCAmelCase_   :						List[str]  ,					):
			# 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.')

			# first, apply the image processor
			UpperCamelCase__				:					Optional[Any]					=					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_):
							UpperCamelCase__				:					Optional[Any]					=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
					UpperCamelCase__				:					int					=					features['words']

			UpperCamelCase__				:					int					=					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
			UpperCamelCase__				:					Any					=					features.pop('pixel_values')
			if return_overflowing_tokens is True:
					UpperCamelCase__				:					List[Any]					=					self.get_overflowing_images(UpperCAmelCase_  ,					encoded_inputs['overflow_to_sample_mapping'])
			UpperCamelCase__				:					Any					=					images

			return encoded_inputs

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						List[str]):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			UpperCamelCase__				:					List[str]					=					[]
			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      __UpperCamelCase							(      self   :						Tuple  ,					*UpperCAmelCase_   :						str  ,					**UpperCAmelCase_   :						int):
			return self.tokenizer.batch_decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Optional[Any]  ,					*UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						Optional[Any]):
			return self.tokenizer.decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Dict):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			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      __UpperCamelCase							(      self   :						Any):
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,					UpperCAmelCase_  ,					)
			return self.image_processor








'''simple docstring'''




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__          =						logging.get_logger(__name__)


lowerCAmelCase__          =						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase__          =						{
    '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__          =						{'facebook/blenderbot-3B': 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       __UpperCAmelCase						(  )							->     Union[str, Any]:
		UpperCamelCase__				:					Optional[Any]					=					(
		    list(range(ord('!')     ,      ord('~') + 1)) + list(range(ord('¡')     ,      ord('¬') + 1)) + list(range(ord('®')     ,      ord('ÿ') + 1))
		)
		UpperCamelCase__				:					List[Any]					=					bs[:]
		UpperCamelCase__				:					Optional[int]					=					0
		for b in range(2**8):
				if b not in bs:
						bs.append(lowerCamelCase_)
						cs.append(2**8 + n)
						n += 1
		UpperCamelCase__				:					Union[str, Any]					=					[chr(lowerCamelCase_) for n in cs]
		return dict(zip(lowerCamelCase_     ,      lowerCamelCase_))







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Any					=					set()
		UpperCamelCase__				:					Dict					=					word[0]
		for char in word[1:]:
				pairs.add((prev_char, char))
				UpperCamelCase__				:					str					=					char
		return pairs







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  VOCAB_FILES_NAMES
	_lowerCamelCase              =  PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase              =  ['''input_ids''', '''attention_mask''']

	def __init__(      self   :						Tuple  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Dict="replace"  ,					UpperCAmelCase_   :						int="<s>"  ,					UpperCAmelCase_   :						Tuple="</s>"  ,					UpperCAmelCase_   :						Any="</s>"  ,					UpperCAmelCase_   :						List[Any]="<s>"  ,					UpperCAmelCase_   :						List[str]="<unk>"  ,					UpperCAmelCase_   :						Any="<pad>"  ,					UpperCAmelCase_   :						Optional[Any]="<mask>"  ,					UpperCAmelCase_   :						str=False  ,					**UpperCAmelCase_   :						List[Any]  ,					):
			UpperCamelCase__				:					Union[str, Any]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else bos_token
			UpperCamelCase__				:					List[str]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else eos_token
			UpperCamelCase__				:					Optional[Any]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else sep_token
			UpperCamelCase__				:					int					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else cls_token
			UpperCamelCase__				:					Tuple					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else unk_token
			UpperCamelCase__				:					Optional[Any]					=					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
			UpperCamelCase__				:					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:
					UpperCamelCase__				:					Any					=					json.load(UpperCAmelCase_)
			UpperCamelCase__				:					Dict					=					{v: k for k, v in self.encoder.items()}
			UpperCamelCase__				:					Any					=					errors  # how to handle errors in decoding
			UpperCamelCase__				:					Tuple					=					bytes_to_unicode()
			UpperCamelCase__				:					Union[str, Any]					=					{v: k for k, v in self.byte_encoder.items()}
			with open(UpperCAmelCase_  ,					encoding='utf-8') as merges_handle:
					UpperCamelCase__				:					List[Any]					=					merges_handle.read().split('\n')[1:-1]
			UpperCamelCase__				:					List[Any]					=					[tuple(merge.split()) for merge in bpe_merges]
			UpperCamelCase__				:					Any					=					dict(zip(UpperCAmelCase_  ,					range(len(UpperCAmelCase_))))
			UpperCamelCase__				:					Dict					=					{}
			UpperCamelCase__				:					Dict					=					add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						Tuple):
			return len(self.encoder)

	def      __UpperCamelCase							(      self   :						Tuple):
			return dict(self.encoder  ,					**self.added_tokens_encoder)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Union[str, Any]):
			if token in self.cache:
					return self.cache[token]
			UpperCamelCase__				:					Optional[int]					=					tuple(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					get_pairs(UpperCAmelCase_)

			if not pairs:
					return token

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

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					[]
			for token in re.findall(self.pat  ,					UpperCAmelCase_):
					UpperCamelCase__				:					Optional[int]					=					''.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      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Optional[Any]):
			return self.encoder.get(UpperCAmelCase_  ,					self.encoder.get(self.unk_token))

	def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						Optional[int]):
			return self.decoder.get(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					int					=					''.join(UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'  ,					errors=self.errors)
			return text

	def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
			if not os.path.isdir(UpperCAmelCase_):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory')
					return
			UpperCamelCase__				:					str					=					os.path.join(
			    UpperCAmelCase_  ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
			UpperCamelCase__				:					Optional[Any]					=					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')

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

			return vocab_file, merge_file

	def      __UpperCamelCase							(      self   :						Optional[int]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None  ,					UpperCAmelCase_   :						bool = False):
			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      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			UpperCamelCase__				:					Any					=					[self.sep_token_id]
			UpperCamelCase__				:					Optional[int]					=					[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      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						str=False  ,					**UpperCAmelCase_   :						Optional[Any]):
			UpperCamelCase__				:					Tuple					=					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()):
					UpperCamelCase__				:					str					=					' ' + text
			return (text, kwargs)

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
			return token_ids_a + [self.eos_token_id]





	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						"Conversation"):
			UpperCamelCase__				:					List[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_)

			UpperCamelCase__				:					Optional[Any]					=					'  '.join(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					self.encode(UpperCAmelCase_)
			if len(UpperCAmelCase_) > self.model_max_length:
					UpperCamelCase__				:					Optional[Any]					=					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




