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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase      :				int          = logging.get_logger(__name__)


_UpperCAmelCase      :				Any          = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_UpperCAmelCase      :				Dict          = {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_UpperCAmelCase      :				Tuple          = {
    "allenai/longformer-base-4096": 4_096,
    "allenai/longformer-large-4096": 4_096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_096,
}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def    A (      )     ->						List[Any]:
		'''simple docstring'''

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


def    A (      lowercase				)     ->						Optional[int]:
		'''simple docstring'''

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


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : List[str]			  =       VOCAB_FILES_NAMES
		__lowercase : Optional[Any]			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : Optional[Any]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : Union[str, Any]			  =       ["input_ids", "attention_mask"]



		def __init__(				self       ,   A_       ,   A_       ,   A_="replace"       ,   A_="<s>"       ,   A_="</s>"       ,   A_="</s>"       ,   A_="<s>"       ,   A_="<unk>"       ,   A_="<pad>"       ,   A_="<mask>"       ,   A_=False       ,   **A_       ,   )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else bos_token
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else eos_token
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else sep_token
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else cls_token
				UpperCamelCase						=  AddedToken(A_       ,   lstrip=A_       ,   rstrip=A_ ) if isinstance(A_       ,   A_ ) else unk_token
				UpperCamelCase						=  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
				UpperCamelCase						=  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_       ,   **A_       ,   )

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

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				UpperCamelCase						=  re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )



		@property
		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return len(self.encoder )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				return dict(self.encoder       ,   **self.added_tokens_encoder )



		def __UpperCamelCase				(				self       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase						=  tuple(A_ )
				UpperCamelCase						=  get_pairs(A_ )

				if not pairs:
						return token

				while True:
						UpperCamelCase						=  min(A_       ,   key=lambda A_ : self.bpe_ranks.get(A_       ,   float('inf' ) ) )
						if bigram not in self.bpe_ranks:
								break
						UpperCamelCase			,						UpperCamelCase						=  bigram
						UpperCamelCase						=  []
						UpperCamelCase						=  0
						while i < len(A_ ):
								try:
										UpperCamelCase						=  word.index(A_       ,   A_ )
								except ValueError:
										new_word.extend(word[i:] )
										break
								else:
										new_word.extend(word[i:j] )
										UpperCamelCase						=  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
						UpperCamelCase						=  tuple(A_ )
						UpperCamelCase						=  new_word
						if len(A_ ) == 1:
								break
						else:
								UpperCamelCase						=  get_pairs(A_ )
				UpperCamelCase						=  ' '.join(A_ )
				UpperCamelCase						=  word
				return word



		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  []
				for token in re.findall(self.pat       ,   A_ ):
						UpperCamelCase						=  ''.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 __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				return self.encoder.get(A_       ,   self.encoder.get(self.unk_token ) )



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				return self.decoder.get(A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  ''.join(A_ )
				UpperCamelCase						=  bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'       ,   errors=self.errors )
				return text



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

				UpperCamelCase						=  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 A_ : 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						=  token_index
								writer.write(' '.join(A_ ) + '\n' )
								index += 1

				return vocab_file, merge_file



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase						=  [self.cls_token_id]
				UpperCamelCase						=  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None       ,   A_ = False )						->      List[int]:
				"""simple docstring"""
				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, 1] + ([0] * len(A_ )) + [1]



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      List[int]:
				"""simple docstring"""
				UpperCamelCase						=  [self.sep_token_id]
				UpperCamelCase						=  [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       ,   A_       ,   A_=False       ,   **A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  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()):
						UpperCamelCase						=  ' ' + text
				return (text, kwargs)


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor


class        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=3       ,   A_=18       ,   A_=30       ,   A_=400       ,   A_=True       ,   A_=None       ,   A_=True       ,   A_=False       ,   A_=True       ,   A_=True       ,   A_=[0.5, 0.5, 0.5]       ,   A_=[0.5, 0.5, 0.5]       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  image_size
				UpperCamelCase						=  min_resolution
				UpperCamelCase						=  max_resolution
				UpperCamelCase						=  do_resize
				UpperCamelCase						=  size if size is not None else {'height': 18, 'width': 20}
				UpperCamelCase						=  do_thumbnail
				UpperCamelCase						=  do_align_axis
				UpperCamelCase						=  do_pad
				UpperCamelCase						=  do_normalize
				UpperCamelCase						=  image_mean
				UpperCamelCase						=  image_std



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_thumbnail": self.do_thumbnail,
				    "do_align_long_axis": self.do_align_axis,
				    "do_pad": self.do_pad,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}






@require_torch
@require_vision
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       DonutImageProcessor if is_vision_available() else None



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  DonutImageProcessingTester(self )



		@property
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(A_       ,   'do_resize' ) )
				self.assertTrue(hasattr(A_       ,   'size' ) )
				self.assertTrue(hasattr(A_       ,   'do_thumbnail' ) )
				self.assertTrue(hasattr(A_       ,   'do_align_long_axis' ) )
				self.assertTrue(hasattr(A_       ,   'do_pad' ) )
				self.assertTrue(hasattr(A_       ,   'do_normalize' ) )
				self.assertTrue(hasattr(A_       ,   'image_mean' ) )
				self.assertTrue(hasattr(A_       ,   'image_std' ) )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 20} )

				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42 )
				self.assertEqual(image_processor.size       ,   {'height': 42, 'width': 42} )

				# Previous config had dimensions in (width, height) order
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=(42, 84) )
				self.assertEqual(image_processor.size       ,   {'height': 84, 'width': 42} )



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



		@is_flaky()
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PIL images
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   Image.Image )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   numpify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   np.ndarray )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_       ,   torchify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   torch.Tensor )

				# Test not batched input
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values
				self.assertEqual(
				    encoded_images.shape       ,   (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )
