

'''simple docstring'''

import argparse

import torch

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


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





def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]   					=   UniSpeechSatForSequenceClassification.from_pretrained(__UpperCamelCase ,    config=__UpperCamelCase							)
					UpperCAmelCase		:							Optional[Any]   					=   downstream_dict["projector.weight"]
					UpperCAmelCase		:							str   					=   downstream_dict["projector.bias"]
					UpperCAmelCase		:							Optional[int]   					=   downstream_dict["model.post_net.linear.weight"]
					UpperCAmelCase		:							List[Any]   					=   downstream_dict["model.post_net.linear.bias"]
					return model





def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Union[str, Any]   					=   UniSpeechSatForAudioFrameClassification.from_pretrained(__UpperCamelCase ,    config=__UpperCamelCase							)
					UpperCAmelCase		:							List[str]   					=   downstream_dict["model.linear.weight"]
					UpperCAmelCase		:							List[Any]   					=   downstream_dict["model.linear.bias"]
					return model





def       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							str   					=   UniSpeechSatForXVector.from_pretrained(__UpperCamelCase ,    config=__UpperCamelCase							)
					UpperCAmelCase		:							List[str]   					=   downstream_dict["connector.weight"]
					UpperCAmelCase		:							List[str]   					=   downstream_dict["connector.bias"]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
										UpperCAmelCase		:							List[Any]   					=   downstream_dict[
										    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
										]
										UpperCAmelCase		:							Tuple   					=   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

					UpperCAmelCase		:							Dict   					=   downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
					UpperCAmelCase		:							List[str]   					=   downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
					UpperCAmelCase		:							List[str]   					=   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       lowercase					(     __magic_name__ ,    __magic_name__ ,    __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							str   					=   torch.load(__UpperCamelCase ,    map_location="cpu"							)

					UpperCAmelCase		:							Tuple   					=   checkpoint["Downstream"]

					UpperCAmelCase		:							Union[str, Any]   					=   UniSpeechSatConfig.from_pretrained(__UpperCamelCase							)
					UpperCAmelCase		:							int   					=   WavaVecaFeatureExtractor.from_pretrained(
					    __UpperCamelCase ,    return_attention_mask=__UpperCamelCase ,    do_normalize=__UpperCamelCase							)

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

					if hf_config.use_weighted_layer_sum:
										UpperCAmelCase		:							List[Any]   					=   checkpoint["Featurizer"]["weights"]

					hf_feature_extractor.save_pretrained(__UpperCamelCase							)
					hf_model.save_pretrained(__UpperCamelCase							)


if __name__ == "__main__":
				a   :			Any         = 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.")
				a   :			int         = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

# 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

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






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