




# 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 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )     ->			List[str]:
       if name != "training_function" and name != "main":
              raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''   )
       lowerCamelCase			,				lowerCamelCase       :				Tuple      			= [], False
       for line in lines:
              if not found_start and f'''def {name}''' in line:
                     lowerCamelCase       :				Optional[Any]      			= True
                     good_lines.append(_SCREAMING_SNAKE_CASE   )
                     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(_SCREAMING_SNAKE_CASE   )

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			int:
       return [line for line in lines if not line.lstrip().startswith("#"   ) and line != "\n"]


def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = None   )     ->			Any:
       with open(_SCREAMING_SNAKE_CASE			,"r"   ) as f:
              lowerCamelCase       :				List[str]      			= f.readlines()
       with open(os.path.abspath(os.path.join("examples"			,"nlp_example.py"   )   )			,"r"   ) as f:
              lowerCamelCase       :				Optional[Any]      			= f.readlines()
       with open(_SCREAMING_SNAKE_CASE			,"r"   ) as f:
              lowerCamelCase       :				Dict      			= f.readlines()
       if secondary_filename is not None:
              with open(_SCREAMING_SNAKE_CASE			,"r"   ) as f:
                     lowerCamelCase       :				List[Any]      			= 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:
              lowerCamelCase       :				Optional[int]      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"main"   )   )
              lowerCamelCase       :				Any      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"main"   )   )
              lowerCamelCase       :				Optional[int]      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"main"   )   )
              if secondary_filename is not None:
                     lowerCamelCase       :				Dict      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"main"   )   )
       else:
              lowerCamelCase       :				Any      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"training_function"   )   )
              lowerCamelCase       :				Dict      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"training_function"   )   )
              lowerCamelCase       :				Tuple      			= clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"training_function"   )   )
              if secondary_filename is not None:
                     lowerCamelCase       :				Optional[int]      			= clean_lines(
                         get_function_contents_by_name(_SCREAMING_SNAKE_CASE			,"training_function"   )   )

       lowerCamelCase       :				List[str]      			= "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
       lowerCamelCase       :				Tuple      			= []
       lowerCamelCase       :				Union[str, Any]      			= []  # We keep track of the idxs just in case it's a repeated statement
       lowerCamelCase       :				List[str]      			= iter(_SCREAMING_SNAKE_CASE   )
       for i in range(len(_SCREAMING_SNAKE_CASE   ) - 1   ):
              if i not in passed_idxs:
                     lowerCamelCase       :				Dict      			= next(_SCREAMING_SNAKE_CASE   )
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_feature_code.append(_SCREAMING_SNAKE_CASE   )
                                   passed_idxs.append(_SCREAMING_SNAKE_CASE   )
                            else:
                                   # Skip over the `config['num_epochs'] = 2` statement
                                   lowerCamelCase       :				str      			= next(_SCREAMING_SNAKE_CASE   )

    # Extract out just the new parts from the full_file_training_func
       lowerCamelCase       :				int      			= []
       lowerCamelCase       :				Any      			= []  # We keep track of the idxs just in case it's a repeated statement
       for i, line in enumerate(_SCREAMING_SNAKE_CASE   ):
              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(_SCREAMING_SNAKE_CASE   )
                                   passed_idxs.append(_SCREAMING_SNAKE_CASE   )

    # Finally, get the overall diff
       lowerCamelCase       :				List[str]      			= [line for line in new_feature_code if line not in new_full_example_parts]
       if secondary_filename is not None:
              lowerCamelCase       :				str      			= [line for line in full_file_contents if line not in secondary_file_func]
              lowerCamelCase       :				List[Any]      			= [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_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__	:				Dict  =       {
    'configuration_oneformer': ['ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OneFormerConfig'],
    'processing_oneformer': ['OneFormerProcessor'],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       ['OneFormerImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__	:				Optional[int]  =       [
           'ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OneFormerForUniversalSegmentation',
           'OneFormerModel',
           'OneFormerPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
       from .processing_oneformer import OneFormerProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_oneformer import OneFormerImageProcessor
       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_oneformer import (
                  ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OneFormerForUniversalSegmentation,
                  OneFormerModel,
                  OneFormerPreTrainedModel,
              )


else:
       import sys

       SCREAMING_SNAKE_CASE__	:				str  =       _LazyModule(__name__, globals()['__file__'], _import_structure)






