


'''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 						_lowerCamelCase					(    _a				):




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						["""image_processor""", """tokenizer"""]
 __lowercase				:		Tuple    							=						"""LayoutLMv2ImageProcessor"""
 __lowercase				:		Union[str, Any]    							=						("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")


 def __init__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      **__lowercase						):

  """simple docstring"""







  if "feature_extractor" in kwargs:
   warnings.warn(
       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
       ' instead.'      ,      snake_case_      ,      )
   __A  :		int       =			kwargs.pop('feature_extractor'						)

  __A  :		List[Any]       =			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__(snake_case_      ,      snake_case_						)


 def __call__(      self      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = True      ,      __lowercase = False      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = 0      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = False      ,      __lowercase = False      ,      __lowercase = False      ,      __lowercase = False      ,      __lowercase = True      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  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  :		Any       =			self.image_processor(images=snake_case_      ,      return_tensors=snake_case_						)

  # second, apply the tokenizer
  if text is not None and self.image_processor.apply_ocr and text_pair is None:
   if isinstance(snake_case_      ,      snake_case_						):
    __A  :		List[str]       =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
   __A  :		Any       =			features['words']

  __A  :		Optional[Any]       =			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=snake_case_      ,      add_special_tokens=snake_case_      ,      padding=snake_case_      ,      truncation=snake_case_      ,      max_length=snake_case_      ,      stride=snake_case_      ,      pad_to_multiple_of=snake_case_      ,      return_token_type_ids=snake_case_      ,      return_attention_mask=snake_case_      ,      return_overflowing_tokens=snake_case_      ,      return_special_tokens_mask=snake_case_      ,      return_offsets_mapping=snake_case_      ,      return_length=snake_case_      ,      verbose=snake_case_      ,      return_tensors=snake_case_      ,      **snake_case_      ,      )

  # add pixel values
  __A  :		Dict       =			features.pop('pixel_values'						)
  if return_overflowing_tokens is True:
   __A  :		List[str]       =			self.get_overflowing_images(snake_case_      ,      encoded_inputs['overflow_to_sample_mapping']						)
  __A  :		Optional[int]       =			images

  return encoded_inputs


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			[]
  for sample_idx in overflow_to_sample_mapping:
   images_with_overflow.append(images[sample_idx]						)

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

  return images_with_overflow


 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







  return self.tokenizer.batch_decode(*snake_case_      ,      **snake_case_						)


 def  snake_case__   (      self      ,      *__lowercase      ,      **__lowercase						):

  """simple docstring"""







  return self.tokenizer.decode(*snake_case_      ,      **snake_case_						)


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return ["input_ids", "bbox", "attention_mask", "image"]


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  warnings.warn(
      '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,      snake_case_      ,      )
  return self.image_processor_class







 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  warnings.warn(
      '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,      snake_case_      ,      )
  return self.image_processor





'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


UpperCAmelCase_ :     int								=      '\\n    Text data.\n    Second line of data.'

UpperCAmelCase_ :     Tuple								=      'file'


@pytest.fixture(scope='session'			)
def 			_lowercase       (				UpperCamelCase__       :					Any			):
 __A  :		Optional[Any]       =			tmp_path_factory.mktemp('data'			) / (FILE_PATH + '.zstd')
 __A  :		Optional[int]       =			bytes(UpperCamelCase__,				'utf-8'			)
 with zstd.open(UpperCamelCase__,				'wb'			) as f:
  f.write(UpperCamelCase__			)
 return path


@pytest.fixture
def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 with open(os.path.join(tmpfs.local_root_dir,				UpperCamelCase__			),				'w'			) as f:
  f.write(UpperCamelCase__			)
 return FILE_PATH


@pytest.mark.parametrize('compression_format',				['gzip', 'xz', 'zstd']			)
def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[str]			):
 __A  :		Any       =			{'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
 __A  :		str       =			input_paths[compression_format]
 __A  :		str       =			tmp_path / 'cache'
 __A  :		int       =			DownloadConfig(cache_dir=UpperCamelCase__,				extract_compressed_file=UpperCamelCase__			)
 __A  :		Tuple       =			cached_path(UpperCamelCase__,				download_config=UpperCamelCase__			)
 with open(UpperCamelCase__			) as f:
  __A  :		Union[str, Any]       =			f.read()
 with open(UpperCamelCase__			) as f:
  __A  :		int       =			f.read()
 assert extracted_file_content == expected_file_content


@pytest.mark.parametrize('default_extracted',				[True, False]			)
@pytest.mark.parametrize('default_cache_dir',				[True, False]			)
def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					str,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Dict			):
 __A  :		Optional[Any]       =			'custom_cache'
 __A  :		Dict       =			'custom_extracted_dir'
 __A  :		int       =			tmp_path / 'custom_extracted_path'
 if default_extracted:
  __A  :		List[str]       =			('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
 else:
  monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR',				UpperCamelCase__			)
  monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH',				str(UpperCamelCase__			)			)
  __A  :		Optional[int]       =			custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

 __A  :		Optional[Any]       =			xz_file
 __A  :		List[Any]       =			(
     DownloadConfig(extract_compressed_file=UpperCamelCase__			)
     if default_cache_dir
     else DownloadConfig(cache_dir=tmp_path / custom_cache_dir,				extract_compressed_file=UpperCamelCase__			)
 )
 __A  :		Tuple       =			cached_path(UpperCamelCase__,				download_config=UpperCamelCase__			)
 assert Path(UpperCamelCase__			).parent.parts[-2:] == expected


def 			_lowercase       (				UpperCamelCase__       :					Optional[int]			):
 # absolute path
 __A  :		Any       =			str(Path(UpperCamelCase__			).resolve()			)
 assert cached_path(UpperCamelCase__			) == text_file
 # relative path
 __A  :		Optional[Any]       =			str(Path(UpperCamelCase__			).resolve().relative_to(Path(os.getcwd()			)			)			)
 assert cached_path(UpperCamelCase__			) == text_file


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 # absolute path
 __A  :		Any       =			str(tmp_path.resolve() / '__missing_file__.txt'			)
 with pytest.raises(UpperCamelCase__			):
  cached_path(UpperCamelCase__			)
 # relative path
 __A  :		Tuple       =			'./__missing_file__.txt'
 with pytest.raises(UpperCamelCase__			):
  cached_path(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Tuple			):
 __A  :		Optional[int]       =			get_from_cache(f"""tmp://{tmpfs_file}"""			)
 with open(UpperCamelCase__			) as f:
  __A  :		Tuple       =			f.read()
 assert output_file_content == FILE_CONTENT


@patch('datasets.config.HF_DATASETS_OFFLINE',				UpperCamelCase__			)
def 			_lowercase       (				):
 with pytest.raises(UpperCamelCase__			):
  cached_path('https://huggingface.co'			)


@patch('datasets.config.HF_DATASETS_OFFLINE',				UpperCamelCase__			)
def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Optional[int]       =			tmp_path_factory.mktemp('data'			) / 'file.html'
 with pytest.raises(UpperCamelCase__			):
  http_get('https://huggingface.co',				temp_file=UpperCamelCase__			)
 with pytest.raises(UpperCamelCase__			):
  http_head('https://huggingface.co'			)


@patch('datasets.config.HF_DATASETS_OFFLINE',				UpperCamelCase__			)
def 			_lowercase       (				UpperCamelCase__       :					Any			):
 __A  :		Union[str, Any]       =			tmp_path_factory.mktemp('data'			) / 'file.html'
 with pytest.raises(UpperCamelCase__			):
  ftp_get('ftp://huggingface.co',				temp_file=UpperCamelCase__			)
 with pytest.raises(UpperCamelCase__			):
  ftp_head('ftp://huggingface.co'			)


@patch('datasets.config.HF_DATASETS_OFFLINE',				UpperCamelCase__			)
def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Optional[int]       =			tmp_path_factory.mktemp('data'			) / 'file.html'
 with pytest.raises(UpperCamelCase__			):
  fsspec_get('s3://huggingface.co',				temp_file=UpperCamelCase__			)
 with pytest.raises(UpperCamelCase__			):
  fsspec_head('s3://huggingface.co'			)






