


# Copyright 2022 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 os
from typing import List
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     if name != "training_function" and name != "main":
          raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''       )
     snake_case				,						snake_case					=       [], False
     for line in lines:
          if not found_start and F'''def {name}''' in line:
               snake_case					=       True
               good_lines.append(UpperCamelCase_       )
               continue
          if found_start:
               if name == "training_function" and "def main" in line:
                    return good_lines
               if name == "main" and "if __name__" in line:
                    return good_lines
               good_lines.append(UpperCamelCase_       )
def     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






     return [line for line in lines if not line.lstrip().startswith('''#'''       ) and line != "\n"]

def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = None       ):



     """simple docstring"""






     with open(UpperCamelCase_							,'''r'''       ) as f:
          snake_case					=       f.readlines()
     with open(os.path.abspath(os.path.join('''examples'''							,'''nlp_example.py'''       )       )							,'''r'''       ) as f:
          snake_case					=       f.readlines()
     with open(UpperCamelCase_							,'''r'''       ) as f:
          snake_case					=       f.readlines()
     if secondary_filename is not None:
          with open(UpperCamelCase_							,'''r'''       ) as f:
               snake_case					=       f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
     if parser_only:
          snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
          snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
          snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
          if secondary_filename is not None:
               snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''main'''       )       )
     else:
          snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
          snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
          snake_case					=       clean_lines(get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )
          if secondary_filename is not None:
               snake_case					=       clean_lines(
                   get_function_contents_by_name(UpperCamelCase_							,'''training_function'''       )       )

     snake_case					=       '''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

     # Specific code in our script that differs from the full version, aka what is new
     snake_case					=       []
     snake_case					=       []  # We keep track of the idxs just in case it's a repeated statement
     snake_case					=       iter(UpperCamelCase_       )
     for i in range(len(UpperCamelCase_       ) - 1       ):
          if i not in passed_idxs:
               snake_case					=       next(UpperCamelCase_       )
               if (line not in full_file_func) and (line.lstrip() != _dl_line):
                    if "TESTING_MOCKED_DATALOADERS" not in line:
                         new_feature_code.append(UpperCamelCase_       )
                         passed_idxs.append(UpperCamelCase_       )
                    else:
                         # Skip over the `config['num_epochs'] = 2` statement
                         snake_case					=       next(UpperCamelCase_       )

    # Extract out just the new parts from the full_file_training_func
     snake_case					=       []
     snake_case					=       []  # We keep track of the idxs just in case it's a repeated statement
     for i, line in enumerate(UpperCamelCase_       ):
          if i not in passed_idxs:
               if (line not in full_file_func) and (line.lstrip() != _dl_line):
                    if "TESTING_MOCKED_DATALOADERS" not in line:
                         new_full_example_parts.append(UpperCamelCase_       )
                         passed_idxs.append(UpperCamelCase_       )

    # Finally, get the overall diff
     snake_case					=       [line for line in new_feature_code if line not in new_full_example_parts]
     if secondary_filename is not None:
          snake_case					=       [line for line in full_file_contents if line not in secondary_file_func]
          snake_case					=       [line for line in diff_from_example if line not in diff_from_two]

     return diff_from_example








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




