
# 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			(   lowercase       ,			lowercase					)  ->    Any:
		'''simple docstring'''

		if name != "training_function" and name != "main":
				raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''					)
		UpperCamelCase      ,    UpperCamelCase            =	[], False
		for line in lines:
				if not found_start and f'''def {name}''' in line:
						UpperCamelCase            =	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  A			(   lowercase					)  ->    Tuple:
		'''simple docstring'''

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


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase = None					)  ->    Tuple:
		'''simple docstring'''

		with open(lowercase       ,			'r'					) as f:
				UpperCamelCase            =	f.readlines()
		with open(os.path.abspath(os.path.join('examples'       ,			'nlp_example.py'					)					)       ,			'r'					) as f:
				UpperCamelCase            =	f.readlines()
		with open(lowercase       ,			'r'					) as f:
				UpperCamelCase            =	f.readlines()
		if secondary_filename is not None:
				with open(lowercase       ,			'r'					) as f:
						UpperCamelCase            =	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            =	clean_lines(get_function_contents_by_name(lowercase       ,			'main'					)					)
				UpperCamelCase            =	clean_lines(get_function_contents_by_name(lowercase       ,			'main'					)					)
				UpperCamelCase            =	clean_lines(get_function_contents_by_name(lowercase       ,			'main'					)					)
				if secondary_filename is not None:
						UpperCamelCase            =	clean_lines(get_function_contents_by_name(lowercase       ,			'main'					)					)
		else:
				UpperCamelCase            =	clean_lines(get_function_contents_by_name(lowercase       ,			'training_function'					)					)
				UpperCamelCase            =	clean_lines(get_function_contents_by_name(lowercase       ,			'training_function'					)					)
				UpperCamelCase            =	clean_lines(get_function_contents_by_name(lowercase       ,			'training_function'					)					)
				if secondary_filename is not None:
						UpperCamelCase            =	clean_lines(
						    get_function_contents_by_name(lowercase       ,			'training_function'					)					)

		UpperCamelCase            =	'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            =	[]
		UpperCamelCase            =	[]  # We keep track of the idxs just in case it's a repeated statement
		UpperCamelCase            =	iter(lowercase					)
		for i in range(len(lowercase					) - 1					):
				if i not in passed_idxs:
						UpperCamelCase            =	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
										UpperCamelCase            =	next(lowercase					)

    # Extract out just the new parts from the full_file_training_func
		UpperCamelCase            =	[]
		UpperCamelCase            =	[]  # 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
		UpperCamelCase            =	[line for line in new_feature_code if line not in new_full_example_parts]
		if secondary_filename is not None:
				UpperCamelCase            =	[line for line in full_file_contents if line not in secondary_file_func]
				UpperCamelCase            =	[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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__				:				Dict						=      {
    "configuration_switch_transformers": [
        "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwitchTransformersConfig",
        "SwitchTransformersOnnxConfig",
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__				:				List[Any]						=      [
				    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "SwitchTransformersEncoderModel",
				    "SwitchTransformersForConditionalGeneration",
				    "SwitchTransformersModel",
				    "SwitchTransformersPreTrainedModel",
				    "SwitchTransformersTop1Router",
				    "SwitchTransformersSparseMLP",
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowercase__				:				List[Any]						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




