


"""simple docstring"""



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__snake_case             =   logging.getLogger()







def   __lowerCAmelCase						(						)     ->						Dict:


  """simple docstring"""





  snake_case   :				int				 =   argparse.ArgumentParser()
  parser.add_argument("-f"			)
  snake_case   :				Optional[Any]				 =   parser.parse_args()
  return args.f







class _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__     ):







   def 			lowerCamelCase		(     self						)     ->		Any:

     '''simple docstring'''




     snake_case   :				Dict				 =   logging.StreamHandler(sys.stdout						)
     logger.addHandler(A__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Tuple:

     '''simple docstring'''




     snake_case   :				Dict				 =   get_gpu_count()

     if n_gpu > 1:
       pass
       # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
       # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
       # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
       # cmd = [sys.executable] + distributed_args + args
       # execute_subprocess_async(cmd, env=self.get_env())
       # XXX: test the results - need to save them first into .json file
     else:
       args.insert(0						,     "run_glue_deebert.py"						)
       with patch.object(A__						,     "argv"						,     A__						):
         snake_case   :				List[Any]				 =   run_glue_deebert.main()
         for value in result.values():
           self.assertGreaterEqual(A__						,     0.666						)







   @slow
   @require_torch_non_multi_gpu
   def 			lowerCamelCase		(     self						)     ->		Tuple:

     '''simple docstring'''




     snake_case   :				List[str]				 =   "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
     self.run_and_check(A__						)

     snake_case   :				Union[str, Any]				 =   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
     self.run_and_check(A__						)

     snake_case   :				Tuple				 =   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
     self.run_and_check(A__						)









"""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    (				snake_case_     ):
   __UpperCAmelCase			:	List[str]							  =  ['''image_processor''', '''tokenizer''']
   __UpperCAmelCase			:	str							  =  '''LayoutLMv2ImageProcessor'''
   __UpperCAmelCase			:	Dict							  =  ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''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."						,     UpperCamelCase__						,     )
           snake_case   :				Dict				 =   kwargs.pop("feature_extractor"						)

       snake_case   :				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__(UpperCamelCase__						,     UpperCamelCase__						)







   def __call__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     **UpperCamelCase__						,     )     ->		BatchEncoding:

       '''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
       snake_case   :				Any				 =   self.image_processor(images=UpperCamelCase__						,     return_tensors=UpperCamelCase__						)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				Optional[int]				 =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
           snake_case   :				Optional[Any]				 =   features["words"]

       snake_case   :				Dict				 =   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=UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						,     padding=UpperCamelCase__						,     truncation=UpperCamelCase__						,     max_length=UpperCamelCase__						,     stride=UpperCamelCase__						,     pad_to_multiple_of=UpperCamelCase__						,     return_token_type_ids=UpperCamelCase__						,     return_attention_mask=UpperCamelCase__						,     return_overflowing_tokens=UpperCamelCase__						,     return_special_tokens_mask=UpperCamelCase__						,     return_offsets_mapping=UpperCamelCase__						,     return_length=UpperCamelCase__						,     verbose=UpperCamelCase__						,     return_tensors=UpperCamelCase__						,     **UpperCamelCase__						,     )

       # add pixel values
       snake_case   :				Dict				 =   features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           snake_case   :				Any				 =   self.get_overflowing_images(UpperCamelCase__						,     encoded_inputs["overflow_to_sample_mapping"]						)
       snake_case   :				str				 =   images

       return encoded_inputs







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		str:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]						)

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

       return images_with_overflow







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




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







   @property
   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




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







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




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







   @property
   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




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





