



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 	a__       (       a__       ):


					"""simple docstring"""



					A__  :				int					 =				['''image_processor''', '''tokenizer''']
					A__  :				Dict					 =				'''LayoutLMv2ImageProcessor'''
					A__  :				Optional[int]					 =				('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')

					def __init__(		self     :Union[str, Any]      ,  lowercase__     :Optional[Any]=None      ,  lowercase__     :Union[str, Any]=None      ,  **lowercase__     :Tuple    ):
										lowercase						   =       None
										if "feature_extractor" in kwargs:
															warnings.warn(
															    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
															    ' instead.'      ,  _lowerCamelCase      ,  )
															lowercase						   =       kwargs.pop('feature_extractor'    )

										lowercase						   =       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__(_lowerCamelCase      ,  _lowerCamelCase    )

					def __call__(		self     :Optional[int]      ,  lowercase__     :Optional[int]      ,  lowercase__     :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,  lowercase__     :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,  lowercase__     :Union[List[List[int]], List[List[List[int]]]] = None      ,  lowercase__     :Optional[Union[List[int], List[List[int]]]] = None      ,  lowercase__     :bool = True      ,  lowercase__     :Union[bool, str, PaddingStrategy] = False      ,  lowercase__     :Union[bool, str, TruncationStrategy] = False      ,  lowercase__     :Optional[int] = None      ,  lowercase__     :int = 0      ,  lowercase__     :Optional[int] = None      ,  lowercase__     :Optional[bool] = None      ,  lowercase__     :Optional[bool] = None      ,  lowercase__     :bool = False      ,  lowercase__     :bool = False      ,  lowercase__     :bool = False      ,  lowercase__     :bool = False      ,  lowercase__     :bool = True      ,  lowercase__     :Optional[Union[str, TensorType]] = None      ,  **lowercase__     :Optional[Any]      ,  ):







										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
										lowercase						   =       self.image_processor(images=_lowerCamelCase      ,  return_tensors=_lowerCamelCase    )

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

										lowercase						   =       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=_lowerCamelCase      ,  add_special_tokens=_lowerCamelCase      ,  padding=_lowerCamelCase      ,  truncation=_lowerCamelCase      ,  max_length=_lowerCamelCase      ,  stride=_lowerCamelCase      ,  pad_to_multiple_of=_lowerCamelCase      ,  return_token_type_ids=_lowerCamelCase      ,  return_attention_mask=_lowerCamelCase      ,  return_overflowing_tokens=_lowerCamelCase      ,  return_special_tokens_mask=_lowerCamelCase      ,  return_offsets_mapping=_lowerCamelCase      ,  return_length=_lowerCamelCase      ,  verbose=_lowerCamelCase      ,  return_tensors=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

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

										return encoded_inputs

					def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :Any      ,  lowercase__     :int    ):
										lowercase						   =       []
										for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]    )

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

										return images_with_overflow

					def 				__UpperCAmelCase		(		self     :Union[str, Any]      ,  *lowercase__     :List[Any]      ,  **lowercase__     :List[Any]    ):
										return self.tokenizer.batch_decode(*_lowerCamelCase      ,  **_lowerCamelCase    )

					def 				__UpperCAmelCase		(		self     :Optional[int]      ,  *lowercase__     :Optional[int]      ,  **lowercase__     :Dict    ):
										return self.tokenizer.decode(*_lowerCamelCase      ,  **_lowerCamelCase    )

					@property
					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]

					@property
					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,  _lowerCamelCase      ,  )
										return self.image_processor_class



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












'''simple docstring'''




import io
import json
import subprocess


snake_case__ 							=    [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

snake_case__ 							=    8




def     snake_case__     (			lowerCamelCase__					: Union[str, Any]      ,		lowerCamelCase__					: Optional[int]      ) -> List[Any]:
					A_			:  Tuple			=				{}
					for src, tgt in pairs:
										A_			:  List[str]			=				f'{src}-{tgt}'
										A_			:  Optional[int]			=				f'sacrebleu -t wmt19 -l {pair} --echo src'.split()
										A_			:  int			=				subprocess.run(lowerCamelCase__      ,		stdout=subprocess.PIPE      ).stdout.decode('''utf-8'''      ).splitlines()
										A_			:  List[str]			=				f'sacrebleu -t wmt19 -l {pair} --echo ref'.split()
										A_			:  Any			=				subprocess.run(lowerCamelCase__      ,		stdout=subprocess.PIPE      ).stdout.decode('''utf-8'''      ).splitlines()
										A_			:  List[Any]			=				{'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


snake_case__ 							=    get_all_data(pairs, n_objs)
snake_case__ 							=    """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
							snake_case__ 							=    json.dump(text, f, indent=2, ensure_ascii=False)



