
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


SCREAMING_SNAKE_CASE				:int									=							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE				:Optional[Any]									=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

SCREAMING_SNAKE_CASE				:List[str]									=							{
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE				:int									=							{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		UpperCAmelCase     (      )						->      List[str]:







				"""simple docstring"""



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


def 		UpperCAmelCase     (      a_		)						->      Tuple:







				"""simple docstring"""



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





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




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

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






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return dict(self.encoder				,**self.added_tokens_encoder					)






	def 							UpperCamelCase_    (			self   :					int				,A   :					Any					):
					if token in self.cache:
									return self.cache[token]
					__A       =					tuple(A					)
					__A       =					get_pairs(A					)

					if not pairs:
									return token

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






	def 							UpperCamelCase_    (			self   :					int				,A   :					Dict					):
					__A       =					[]
					for token in re.findall(self.pat				,A					):
									__A       =					"".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   :					List[str]				,A   :					Union[str, Any]					):
					return self.encoder.get(A				,self.encoder.get(self.unk_token					)					)






	def 							UpperCamelCase_    (			self   :					Any				,A   :					str					):
					return self.decoder.get(A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Dict					):
					__A       =					"".join(A					)
					__A       =					bytearray([self.byte_decoder[c] for c in text]					).decode("utf-8"				,errors=self.errors					)
					return text






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					str				,A   :					Optional[str] = None					):
					if not os.path.isdir(A					):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
									return
					__A       =					os.path.join(
					    A				,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
					__A       =					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"					)

					__A       =					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!"					)
																	__A       =					token_index
													writer.write(" ".join(A					) + "\n"					)
													index += 1

					return vocab_file, merge_file






	def 							UpperCamelCase_    (			self   :					Any				,A   :					List[int]				,A   :					Optional[List[int]] = None					):
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A       =					[self.cls_token_id]
					__A       =					[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






	def 							UpperCamelCase_    (			self   :					int				,A   :					List[int]				,A   :					Optional[List[int]] = None				,A   :					bool = False					):
					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   :					int				,A   :					List[int]				,A   :					Optional[List[int]] = None					):
					__A       =					[self.sep_token_id]
					__A       =					[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   :					Tuple				,A   :					Optional[Any]				,A   :					Optional[Any]=False				,**A   :					Optional[int]					):
					__A       =					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()):
									__A       =					" " + text
					return (text, kwargs)


"""simple docstring"""




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       __magic_name__							(							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]			,					)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  parent
         _lowercase:      str   			=  batch_size
         _lowercase:      Any   			=  num_channels
         _lowercase:      Any   			=  image_size
         _lowercase:      Any   			=  min_resolution
         _lowercase:      int   			=  max_resolution
         _lowercase:      Union[str, Any]   			=  do_resize
         _lowercase:      List[Any]   			=  size if size is not None else {'''height''': 18, '''width''': 20}
         _lowercase:      Optional[int]   			=  do_thumbnail
         _lowercase:      List[str]   			=  do_align_axis
         _lowercase:      int   			=  do_pad
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[Any]   			=  image_mean
         _lowercase:      Union[str, Any]   			=  image_std



       def 				lowercase_				(  self							)  ->						List[str]:





         """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       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		DonutImageProcessor if is_vision_available() else None
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      str   			=  DonutImageProcessingTester(self							)
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         return self.image_processor_tester.prepare_image_processor_dict()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  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 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size			,					{'''height''': 18, '''width''': 20}							)

         _lowercase:      List[str]   			=  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
         _lowercase:      Any   			=  self.image_processing_class.from_dict(self.image_processor_dict			,					size=(42, 84)							)
         self.assertEqual(image_processor.size			,					{'''height''': 84, '''width''': 42}							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         pass
       @is_flaky()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _lowercase:      Dict   			=  prepare_image_inputs(self.image_processor_tester			,					equal_resolution=A_							)
         for image in image_inputs:
           self.assertIsInstance(A_			,					Image.Image							)

         # Test not batched input
         _lowercase:      Tuple   			=  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
         _lowercase:      List[Any]   			=  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 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      str   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _lowercase:      str   			=  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
         _lowercase:      Union[str, Any]   			=  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
         _lowercase:      Dict   			=  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 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _lowercase:      List[Any]   			=  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
         _lowercase:      Union[str, Any]   			=  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
         _lowercase:      Dict   			=  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'''],
             )			,					)
