




'''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      UpperCamelCase       (	snake_case__       ):
  a     :Union[str, Any]          =					['image_processor', 'tokenizer']
  a     :List[str]          =					'LayoutLMv2ImageProcessor'
  a     :List[str]          =					('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



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







      '''simple docstring'''


      _a				:							List[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."""		,     __lowerCAmelCase		,     )
          _a				:							List[str]							       =      kwargs.pop("""feature_extractor""" )

      _a				:							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__(__lowerCAmelCase		,     __lowerCAmelCase )



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







      '''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				:							int							       =      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 ):
              _a				:							List[Any]							       =      [text]  # add batch dimension (as the image processor always adds a batch dimension)
          _a				:							Tuple							       =      features["""words"""]

      _a				:							Optional[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=__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
      _a				:							Any							       =      features.pop("""pixel_values""" )
      if return_overflowing_tokens is True:
          _a				:							Any							       =      self.get_overflowing_images(__lowerCAmelCase		,     encoded_inputs["""overflow_to_sample_mapping"""] )
      _a				:							List[Any]							       =      images

      return encoded_inputs



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      []
      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     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


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



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


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



  @property
  def     a_							(			self ):







      '''simple docstring'''


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



  @property
  def     a_							(			self ):







      '''simple docstring'''


      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     a_							(			self ):







      '''simple docstring'''


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










'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment






class        a__     (      unittest.TestCase    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						int       )	->							Optional[int]:
           __A		 =   inspect.getfile(accelerate.test_utils       )
           __A		 =   os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ["scripts", "test_sync.py"]       )






     @require_cpu
     def      _UpperCamelCase (self     :						str       )	->							Optional[Any]:
           debug_launcher(test_sync.main					,					num_processes=1       )






     @require_cpu
     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							Optional[Any]:
           debug_launcher(test_sync.main       )






     @require_single_gpu
     def      _UpperCamelCase (self     :						Union[str, Any]       )	->							List[str]:
           test_sync.main()






     @require_multi_gpu
     def      _UpperCamelCase (self     :						Optional[int]       )	->							List[str]:
           print(f"Found {torch.cuda.device_count()} devices."       )
           __A		 =   ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
           with patch_environment(omp_num_threads=1       ):
                 execute_subprocess_async(lowercase__					,					env=os.environ.copy()       )


