# 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 					snake_case						(__lowercase					,			__lowercase						)       ->       Dict:

						'''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						:     int				=    [], False
						for line in lines:
												if not found_start and F"""def {name}""" in line:
																		_snake_case						:     str				=    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 					snake_case						(__lowercase						)       ->       Union[str, Any]:

						'''simple docstring'''



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

def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = None						)       ->       List[Any]:

						'''simple docstring'''



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

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

    # Extract out just the new parts from the full_file_training_func
						_snake_case						:     Optional[Any]				=    []
						_snake_case						:     Any				=    []  # 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						:     List[Any]				=    [line for line in new_feature_code if line not in new_full_example_parts]
						if secondary_filename is not None:
												_snake_case						:     Union[str, Any]				=    [line for line in full_file_contents if line not in secondary_file_func]
												_snake_case						:     Dict				=    [line for line in diff_from_example if line not in diff_from_two]

						return diff_from_example
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)