





'''simple docstring'''







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
snake_case_     :       List[Any]						=  logging.get_logger(__name__)





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	Optional[int]:
						UpperCAmelCase_			:      Optional[int]     						=  UniSpeechSatForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[Any]     						=  downstream_dict['''projector.weight''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''projector.bias''']
						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.post_net.linear.weight''']
						UpperCAmelCase_			:      str     						=  downstream_dict['''model.post_net.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Optional[int]:
						UpperCAmelCase_			:      Optional[Any]     						=  UniSpeechSatForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.linear.weight''']
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''model.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Tuple     						=  UniSpeechSatForXVector.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      int     						=  downstream_dict['''connector.weight''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''connector.bias''']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
												UpperCAmelCase_			:      Any     						=  downstream_dict[
												    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												UpperCAmelCase_			:      Dict     						=  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
						UpperCAmelCase_			:      Optional[int]     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
						UpperCAmelCase_			:      int     						=  downstream_dict['''objective.W''']
						return model





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Dict     						=  torch.load(SCREAMING_SNAKE_CASE__,			map_location='''cpu'''   )

						UpperCAmelCase_			:      int     						=  checkpoint['''Downstream''']

						UpperCAmelCase_			:      List[Any]     						=  UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Tuple     						=  WavaVecaFeatureExtractor.from_pretrained(
						    SCREAMING_SNAKE_CASE__,			return_attention_mask=SCREAMING_SNAKE_CASE__,			do_normalize=SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Union[str, Any]     						=  hf_config.architectures[0]
						if arch.endswith('''ForSequenceClassification'''   ):
												UpperCAmelCase_			:      Any     						=  convert_classification(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						elif arch.endswith('''ForAudioFrameClassification'''   ):
												UpperCAmelCase_			:      Tuple     						=  convert_diarization(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						elif arch.endswith('''ForXVector'''   ):
												UpperCAmelCase_			:      Optional[int]     						=  convert_xvector(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						else:
												raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""   )

						if hf_config.use_weighted_layer_sum:
												UpperCAmelCase_			:      Tuple     						=  checkpoint['''Featurizer''']['''weights''']

						hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__   )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       Dict						=  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.")
					snake_case_     :       str						=  parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












'''simple docstring'''







# 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 typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
					from PIL import Image


class 					__a      (lowerCamelCase     ):
							__a					:   int									=       "dandelin/vilt-b32-finetuned-vqa"
							__a					:   Any									=       (
							    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
							    "image containing the information, as well as a `question` which should be the question in English. It "
							    "returns a text that is the answer to the question."
							)
							__a					:   Any									=       "image_qa"
							__a					:   str									=       AutoProcessor
							__a					:   Any									=       AutoModelForVisualQuestionAnswering

							__a					:   List[Any]									=       ["image", "text"]
							__a					:   int									=       ["text"]

							def __init__(							self							:     Tuple       ,     *__magic_name__							:     Any       ,     **__magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													requires_backends(self       ,     ['''vision''']   )
													super().__init__(*__magic_name__       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     "Image"       ,     __magic_name__							:     str   )					->       Tuple:







													"""simple docstring"""
													return self.pre_processor(__magic_name__       ,     __magic_name__       ,     return_tensors='''pt'''   )

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													with torch.no_grad():
																			return self.model(**__magic_name__   ).logits



							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     int   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  outputs.argmax(-1   ).item()
													return self.model.config.idalabel[idx]





