"""simple docstring"""







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image


__SCREAMING_SNAKE_CASE								=["text", "image", "audio"]



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]   ):
     lowercase_			:       Optional[Any]									=			[]

     for input_type in input_types:
          if input_type == "text":
               inputs.append('Text input'   )
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'   )   ) / '000000039769.png'   ).resize((5_12, 5_12)   )   )
          elif input_type == "audio":
               inputs.append(torch.ones(30_00   )   )
          elif isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
               inputs.append(create_inputs(__SCREAMING_SNAKE_CASE   )   )
          else:
               raise ValueError(F'''Invalid type requested: {input_type}'''   )

     return inputs



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List   ):
     lowercase_			:       Optional[Any]									=			[]

     for output in outputs:
          if isinstance(__SCREAMING_SNAKE_CASE  ,    (str, AgentText)   ):
               output_types.append('text'   )
          elif isinstance(__SCREAMING_SNAKE_CASE  ,    (Image.Image, AgentImage)   ):
               output_types.append('image'   )
          elif isinstance(__SCREAMING_SNAKE_CASE  ,    (torch.Tensor, AgentAudio)   ):
               output_types.append('audio'   )
          else:
               raise ValueError(F'''Invalid output: {output}'''   )

     return output_types







@is_tool_test
class  UpperCamelCase	:
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        self.assertTrue(hasattr(self.tool  ,'inputs'    )    )
        self.assertTrue(hasattr(self.tool  ,'outputs'    )    )

        lowercase_			:       Union[str, Any]									=			self.tool.inputs
        for _input in inputs:
             if isinstance(_input  ,__UpperCamelCase    ):
                  for __input in _input:
                       self.assertTrue(__input in authorized_types    )
             else:
                  self.assertTrue(_input in authorized_types    )

        lowercase_			:       str									=			self.tool.outputs
        for _output in outputs:
             self.assertTrue(_output in authorized_types    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       int									=			create_inputs(self.tool.inputs    )
        lowercase_			:       Optional[int]									=			self.tool(*__UpperCamelCase    )

        # There is a single output
        if len(self.tool.outputs    ) == 1:
             lowercase_			:       List[Any]									=			[outputs]

        self.assertListEqual(output_types(__UpperCamelCase    )  ,self.tool.outputs    )
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        self.assertTrue(hasattr(self.tool  ,'description'    )    )
        self.assertTrue(hasattr(self.tool  ,'default_checkpoint'    )    )
        self.assertTrue(self.tool.description.startswith('This is a tool that'    )    )
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Any									=			create_inputs(self.tool.inputs    )
        lowercase_			:       List[str]									=			self.tool(*__UpperCamelCase    )

        if not isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Optional[int]									=			[outputs]

        self.assertEqual(len(__UpperCamelCase    )  ,len(self.tool.outputs    )    )

        for output, output_type in zip(__UpperCamelCase  ,self.tool.outputs    ):
             lowercase_			:       Any									=			AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(__UpperCamelCase  ,__UpperCamelCase    )    )







   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       str									=			create_inputs(self.tool.inputs    )

        lowercase_			:       int									=			[]

        for _input, input_type in zip(__UpperCamelCase  ,self.tool.inputs    ):
             if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
                  _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
             else:
                  _inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
        lowercase_			:       Dict									=			self.tool(*__UpperCamelCase    )

        if not isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
             lowercase_			:       Union[str, Any]									=			[outputs]

        self.assertEqual(len(__UpperCamelCase    )  ,len(self.tool.outputs    )    )


"""simple docstring"""







import argparse

import torch

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


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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[str]									=			UniSpeechSatForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			downstream_dict['projector.weight']
     lowercase_			:       Optional[Any]									=			downstream_dict['projector.bias']
     lowercase_			:       Optional[int]									=			downstream_dict['model.post_net.linear.weight']
     lowercase_			:       Tuple									=			downstream_dict['model.post_net.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[Any]									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			downstream_dict['model.linear.weight']
     lowercase_			:       Union[str, Any]									=			downstream_dict['model.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       Any									=			UniSpeechSatForXVector.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			downstream_dict['connector.weight']
     lowercase_			:       List[str]									=			downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase_			:       Dict									=			downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_			:       List[Any]									=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       int									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )

     lowercase_			:       str									=			checkpoint['Downstream']

     lowercase_			:       str									=			UniSpeechSatConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Tuple									=			WavaVecaFeatureExtractor.from_pretrained(
         __SCREAMING_SNAKE_CASE  ,    return_attention_mask=__SCREAMING_SNAKE_CASE  ,    do_normalize=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       List[str]									=			hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'   ):
          lowercase_			:       List[Any]									=			convert_classification(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForAudioFrameClassification'   ):
          lowercase_			:       List[str]									=			convert_diarization(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     elif arch.endswith('ForXVector'   ):
          lowercase_			:       Optional[Any]									=			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:
          lowercase_			:       Dict									=			checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )
     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


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

