




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]=None				)   ->					Any:
      if subparsers is not None:
            SCREAMING_SNAKE_CASE_      		=     subparsers.add_parser('test'				)
      else:
            SCREAMING_SNAKE_CASE_      		=     argparse.ArgumentParser('Accelerate test command'				)

      parser.add_argument(
          '--config_file'     ,     default=__UpperCAmelCase     ,     help=(
              'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
              'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
              'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
              'with \'huggingface\'.'
          )     ,     )

      if subparsers is not None:
            parser.set_defaults(func=__UpperCAmelCase				)
      return parser

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					List[Any]:
      SCREAMING_SNAKE_CASE_      		=     os.path.sep.join(__file__.split(os.path.sep				)[:-2] + ['test_utils', 'scripts', 'test_script.py']				)

      if args.config_file is None:
            SCREAMING_SNAKE_CASE_      		=     script_name
      else:
            SCREAMING_SNAKE_CASE_      		=     f"--config_file={args.config_file} {script_name}"

      SCREAMING_SNAKE_CASE_      		=     ['accelerate-launch'] + test_args.split()
      SCREAMING_SNAKE_CASE_      		=     execute_subprocess_async(__UpperCAmelCase     ,     env=os.environ.copy()				)
      if result.returncode == 0:
            print('Test is a success! You are ready for your distributed training!'				)

def    UpperCAmelCase_				( )   ->					Optional[int]:
      SCREAMING_SNAKE_CASE_      		=     test_command_parser()
      SCREAMING_SNAKE_CASE_      		=     parser.parse_args()
      test_command(__UpperCAmelCase				)


if __name__ == "__main__":
   main()



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   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["image_processor", "tokenizer"]
       _a   							=							"LayoutLMv2ImageProcessor"
       _a   							=							("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")



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

          lowerCamelCase_						:   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__(__a		,				__a							)



       def __call__(						self				:				List[str]		,				__a				:				List[Any]		,				__a				:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				__a				:				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None		,				__a				:				Union[List[List[int]], List[List[List[int]]]] = None		,				__a				:				Optional[Union[List[int], List[List[int]]]] = None		,				__a				:				bool = True		,				__a				:				Union[bool, str, PaddingStrategy] = False		,				__a				:				Union[bool, str, TruncationStrategy] = None		,				__a				:				Optional[int] = None		,				__a				:				int = 0		,				__a				:				Optional[int] = None		,				__a				:				Optional[bool] = None		,				__a				:				Optional[bool] = None		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = False		,				__a				:				bool = True		,				__a				:				Optional[Union[str, TensorType]] = None		,				**__a				:				Any		,				)					->BatchEncoding:
          # verify input
          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
          lowerCamelCase_						:   Tuple		= 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							):
                lowerCamelCase_						:   Optional[Any]		= [text]  # add batch dimension (as the image processor always adds a batch dimension)
             lowerCamelCase_						:   Dict		= features["""words"""]

          lowerCamelCase_						:   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
          lowerCamelCase_						:   Any		= features.pop("""pixel_values"""							)
          if return_overflowing_tokens is True:
             lowerCamelCase_						:   Dict		= self.get_overflowing_images(__a		,				encoded_inputs["""overflow_to_sample_mapping"""]							)
          lowerCamelCase_						:   Optional[int]		= images

          return encoded_inputs



       def 				_lowerCAmelCase							(						self				:				Optional[Any]		,				__a				:				str		,				__a				:				Union[str, Any]							)					->List[str]:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          lowerCamelCase_						:   Dict		= []
          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 				_lowerCAmelCase							(						self				:				Dict		,				*__a				:				Dict		,				**__a				:				int							)					->int:
          return self.tokenizer.batch_decode(*__a		,				**__a							)



       def 				_lowerCAmelCase							(						self				:				List[Any]		,				*__a				:				List[str]		,				**__a				:				int							)					->List[Any]:
          return self.tokenizer.decode(*__a		,				**__a							)



       @property
       def 				_lowerCAmelCase							(						self				:				int							)					->List[str]:
          return ["input_ids", "bbox", "attention_mask", "image"]



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





