
# 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       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     int:
			if name != "training_function" and name != "main":
						raise ValueError(F"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'""" )
			UpperCamelCase__   ,			UpperCamelCase__    :		List[Any]     =				[], False
			for line in lines:
						if not found_start and F"""def {name}""" in line:
									UpperCamelCase__    :		int     =				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       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     str:
			return [line for line in lines if not line.lstrip().startswith('#' ) and line != "\n"]



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE = None )	->     str:
			with open(__SCREAMING_SNAKE_CASE				,    'r' ) as f:
						UpperCamelCase__    :		Tuple     =				f.readlines()
			with open(os.path.abspath(os.path.join('examples'				,    'nlp_example.py' ) )				,    'r' ) as f:
						UpperCamelCase__    :		Union[str, Any]     =				f.readlines()
			with open(__SCREAMING_SNAKE_CASE				,    'r' ) as f:
						UpperCamelCase__    :		int     =				f.readlines()
			if secondary_filename is not None:
						with open(__SCREAMING_SNAKE_CASE				,    'r' ) as f:
									UpperCamelCase__    :		str     =				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:
						UpperCamelCase__    :		Dict     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'main' ) )
						UpperCamelCase__    :		Union[str, Any]     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'main' ) )
						UpperCamelCase__    :		Tuple     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'main' ) )
						if secondary_filename is not None:
									UpperCamelCase__    :		int     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'main' ) )
			else:
						UpperCamelCase__    :		str     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'training_function' ) )
						UpperCamelCase__    :		Dict     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'training_function' ) )
						UpperCamelCase__    :		Dict     =				clean_lines(get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'training_function' ) )
						if secondary_filename is not None:
									UpperCamelCase__    :		Any     =				clean_lines(
									    get_function_contents_by_name(__SCREAMING_SNAKE_CASE				,    'training_function' ) )

			UpperCamelCase__    :		Optional[Any]     =				'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
			UpperCamelCase__    :		Optional[Any]     =				[]
			UpperCamelCase__    :		Optional[int]     =				[]  # We keep track of the idxs just in case it's a repeated statement
			UpperCamelCase__    :		Any     =				iter(__SCREAMING_SNAKE_CASE )
			for i in range(len(__SCREAMING_SNAKE_CASE ) - 1 ):
						if i not in passed_idxs:
									UpperCamelCase__    :		str     =				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
															UpperCamelCase__    :		List[str]     =				next(__SCREAMING_SNAKE_CASE )

    # Extract out just the new parts from the full_file_training_func
			UpperCamelCase__    :		str     =				[]
			UpperCamelCase__    :		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
			UpperCamelCase__    :		List[Any]     =				[line for line in new_feature_code if line not in new_full_example_parts]
			if secondary_filename is not None:
						UpperCamelCase__    :		str     =				[line for line in full_file_contents if line not in secondary_file_func]
						UpperCamelCase__    :		int     =				[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,
)


UpperCAmelCase__				:  str       					=				{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__				:  List[Any]       					=				[
							    '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MobileViTV2ForImageClassification''',
							    '''MobileViTV2ForSemanticSegmentation''',
							    '''MobileViTV2Model''',
							    '''MobileViTV2PreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_mobilevitva import (
							    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileViTVaConfig,
							    MobileViTVaOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilevitva import (
														    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileViTVaForImageClassification,
														    MobileViTVaForSemanticSegmentation,
														    MobileViTVaModel,
														    MobileViTVaPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__				:  List[str]       					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



