




# Copyright 2023 The HuggingFace Inc. 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool

class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''openai/whisper-base'''
							UpperCamelCase__					     =  (
							    '''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
							    '''transcribed text.'''
							)
							UpperCamelCase__					     =  '''transcriber'''
							UpperCamelCase__					     =  WhisperProcessor
							UpperCamelCase__					     =  WhisperForConditionalGeneration

							UpperCamelCase__					     =  ['''audio''']
							UpperCamelCase__					     =  ['''text''']
							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:Dict				):







								'''simple docstring'''







								return self.pre_processor(__magic_name__		,      return_tensors="""pt"""				).input_features
							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								return self.model.generate(inputs=__magic_name__				)





							def       lowerCamelCase__							(						self						:Optional[int]		,      __magic_name__						:List[Any]				):







								'''simple docstring'''







								return self.pre_processor.batch_decode(__magic_name__		,      skip_special_tokens=__magic_name__				)[0]







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""projector.weight"""]
	a										=							downstream_dict["""projector.bias"""]
	a										=							downstream_dict["""model.post_net.linear.weight"""]
	a										=							downstream_dict["""model.post_net.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""model.linear.weight"""]
	a										=							downstream_dict["""model.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[Any]:
	a										=							WavaVecaForXVector.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""connector.weight"""]
	a										=							downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
		a										=							downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		a										=							downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	a										=							downstream_dict["""objective.W"""]
	return model





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )

	a										=							checkpoint["""Downstream"""]

	a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	a										=							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			do_normalize=__lowerCamelCase    )

	a										=							hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""    ):
		a										=							convert_classification(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForAudioFrameClassification"""    ):
		a										=							convert_diarization(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForXVector"""    ):
		a										=							convert_xvector(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	else:
		raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'    )

	if hf_config.use_weighted_layer_sum:
		a										=							checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(__lowerCamelCase    )
	hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[str]    					=      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.")
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

