

'''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 	A_			(    snake_case_							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[Any]  		=  ["image_processor", "tokenizer"]
     UpperCAmelCase_						:     Optional[Any]  		=  "LayoutLMv3ImageProcessor"
     UpperCAmelCase_						:     str  		=  ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")


     def __init__(		self			:					int					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      **lowercase_			:					int	)						->     List[str]:
      UpperCAmelCase	:     Any						 =			None
      if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'					,      _A					,      )
       UpperCAmelCase	:     Any						 =			kwargs.pop('feature_extractor'	)

      UpperCAmelCase	:     Optional[int]						 =			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__(_A					,      _A	)


     def __call__(		self			:					List[str]					,      lowercase_			:					Optional[Any]					,      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] = None					,      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_			:					str					,      )						->     BatchEncoding:


      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.'	)

      # first, apply the image processor
      UpperCAmelCase	:     str						 =			self.image_processor(images=_A					,      return_tensors=_A	)

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

      UpperCAmelCase	:     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=_A					,      add_special_tokens=_A					,      padding=_A					,      truncation=_A					,      max_length=_A					,      stride=_A					,      pad_to_multiple_of=_A					,      return_token_type_ids=_A					,      return_attention_mask=_A					,      return_overflowing_tokens=_A					,      return_special_tokens_mask=_A					,      return_offsets_mapping=_A					,      return_length=_A					,      verbose=_A					,      return_tensors=_A					,      **_A					,      )

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

      return encoded_inputs


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					Dict	)						->     Union[str, Any]:
      UpperCAmelCase	:     List[str]						 =			[]
      for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]	)

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

      return images_with_overflow


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      *lowercase_			:					Optional[Any]					,      **lowercase_			:					List[Any]	)						->     List[str]:
      return self.tokenizer.batch_decode(*_A					,      **_A	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      *lowercase_			:					Dict					,      **lowercase_			:					str	)						->     Any:
      return self.tokenizer.decode(*_A					,      **_A	)


     @property
     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     int:
      return ["input_ids", "bbox", "attention_mask", "pixel_values"]


     @property
     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      _A					,      )
      return self.image_processor_class




     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     int:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,      _A					,      )
      return self.image_processor







'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			inspect.getfile(accelerate.test_utils	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(
          mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
      UpperCAmelCase	:     List[Any]						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     Dict						 =			['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()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     int						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(f"""Command: {cmd}"""	)
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)




     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1					,      cuda_visible_devices='0,1'	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


if __name__ == "__main__":
 lowercase__         =      Accelerator()
 lowercase__         =      (accelerator.state.process_index + 2, 10)
 lowercase__         =      torch.randint(0, 10, shape).to(accelerator.device)

 lowercase__         =      ""

 lowercase__         =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowercase__         =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowercase__         =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)




