
"""simple docstring"""

import os
from typing import List



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



def 					UpperCamelCase__       (						lowercase__					:       List[str]				):
		return [line for line in lines if not line.lstrip().startswith("#"				) and line != "\n"]



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       str    ,       lowercase__					:       bool    ,       lowercase__					:       str = None				):
		with open(lowercase__    ,       "r"				) as f:
				snake_case						:  Union[str, Any]        =			f.readlines()
		with open(os.path.abspath(os.path.join("examples"    ,       "nlp_example.py"				)				)    ,       "r"				) as f:
				snake_case						:  Union[str, Any]        =			f.readlines()
		with open(lowercase__    ,       "r"				) as f:
				snake_case						:  List[str]        =			f.readlines()
		if secondary_filename is not None:
				with open(lowercase__    ,       "r"				) as f:
						snake_case						:  int        =			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						:  int        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "main"				)				)
				snake_case						:  Any        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "main"				)				)
				snake_case						:  Optional[Any]        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "main"				)				)
				if secondary_filename is not None:
						snake_case						:  Any        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "main"				)				)
		else:
				snake_case						:  Any        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "training_function"				)				)
				snake_case						:  Any        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "training_function"				)				)
				snake_case						:  List[Any]        =			clean_lines(get_function_contents_by_name(lowercase__    ,       "training_function"				)				)
				if secondary_filename is not None:
						snake_case						:  Tuple        =			clean_lines(
						    get_function_contents_by_name(lowercase__    ,       "training_function"				)				)

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

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

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

		return diff_from_example







"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A							=     {
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     ["FlavaFeatureExtractor"]
							__A							=     ["FlavaImageProcessor"]
							__A							=     ["FlavaProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "FlavaForPreTraining",
							    "FlavaImageCodebook",
							    "FlavaImageModel",
							    "FlavaModel",
							    "FlavaMultimodalModel",
							    "FlavaPreTrainedModel",
							    "FlavaTextModel",
							]

if TYPE_CHECKING:
							from .configuration_flava import (
							    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    FlavaConfig,
							    FlavaImageCodebookConfig,
							    FlavaImageConfig,
							    FlavaMultimodalConfig,
							    FlavaTextConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_flava import FlavaFeatureExtractor
														from .image_processing_flava import FlavaImageProcessor
														from .processing_flava import FlavaProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flava import (
														    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlavaForPreTraining,
														    FlavaImageCodebook,
														    FlavaImageModel,
														    FlavaModel,
														    FlavaMultimodalModel,
														    FlavaPreTrainedModel,
														    FlavaTextModel,
														)

else:
							import sys

							__A							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

