





import argparse

import torch

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


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






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				WavaVecaForSequenceClassification.from_pretrained(A__,							config=A__						)
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				downstream_dict["projector.weight"]
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				downstream_dict["projector.bias"]
     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				downstream_dict["model.post_net.linear.weight"]
     _SCREAMING_SNAKE_CASE							:						Dict         =				downstream_dict["model.post_net.linear.bias"]
     return model






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				WavaVecaForAudioFrameClassification.from_pretrained(A__,							config=A__						)
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				downstream_dict["model.linear.weight"]
     _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				downstream_dict["model.linear.bias"]
     return model






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				WavaVecaForXVector.from_pretrained(A__,							config=A__						)
     _SCREAMING_SNAKE_CASE							:						List[str]         =				downstream_dict["connector.weight"]
     _SCREAMING_SNAKE_CASE							:						List[str]         =				downstream_dict["connector.bias"]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
          _SCREAMING_SNAKE_CASE							:						Dict         =				downstream_dict[
              f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
          ]
          _SCREAMING_SNAKE_CASE							:						int         =				downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
     _SCREAMING_SNAKE_CASE							:						Any         =				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
     _SCREAMING_SNAKE_CASE							:						int         =				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
     _SCREAMING_SNAKE_CASE							:						List[str]         =				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				downstream_dict["objective.W"]
     return model






@torch.no_grad()
def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase						):
     _SCREAMING_SNAKE_CASE							:						Tuple         =				torch.load(A__,							map_location="cpu"						)

     _SCREAMING_SNAKE_CASE							:						Any         =				checkpoint["Downstream"]

     _SCREAMING_SNAKE_CASE							:						Any         =				WavaVecaConfig.from_pretrained(A__						)
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				WavaVecaFeatureExtractor.from_pretrained(
         A__,							return_attention_mask=A__,							do_normalize=A__						)

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

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

     hf_feature_extractor.save_pretrained(A__						)
     hf_model.save_pretrained(A__						)


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

'''simple docstring'''
def      UpperCamelCase_				(      A__  ,      A__  ,      A__						):
	if len(A__						) != len(A__						):
		raise ValueError("""The length of profit and weight must be same."""						)
	if max_weight <= 0:
		raise ValueError("""max_weight must greater than zero."""						)
	if any(p < 0 for p in profit						):
		raise ValueError("""Profit can not be negative."""						)
	if any(w < 0 for w in weight						):
		raise ValueError("""Weight can not be negative."""						)

	# List created to store profit gained for the 1kg in case of each weight
	# respectively.  Calculate and append profit/weight for each element.
	a_      				=						[p / w for p, w in zip(A__  ,      A__						)]

	# Creating a copy of the list and sorting profit/weight in ascending order
	a_      				=						sorted(A__						)

	# declaring useful variables
	a_      				=						len(A__						)
	a_      				=						0
	a_      				=						0
	a_      				=						0

	# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
	while limit <= max_weight and i < length:
		# flag value for encountered greatest element in sorted_profit_by_weight
		a_      				=						sorted_profit_by_weight[length - i - 1]
		a_      				=						profit_by_weight.index(A__						)
		a_      				=						-1

		# check if the weight encountered is less than the total weight
		# encountered before.
		if max_weight - limit >= weight[index]:
			limit += weight[index]
			# Adding profit gained for the given weight 1 ===
			# weight[index]/weight[index]
			gain += 1 * profit[index]
		else:
			# Since the weight encountered is greater than limit, therefore take the
			# required number of remaining kgs and calculate profit for it.
			# weight remaining / weight[index]
			gain += (max_weight - limit) / weight[index] * profit[index]
			break
		i += 1
	return gain


if __name__ == "__main__":
		print(
		    'Input profits, weights, and then max_weight (all positive ints) separated by '
		    'spaces.'
		)

		lowercase__											=[int(x) for x in input('Input profits separated by spaces: ').split()]
		lowercase__											=[int(x) for x in input('Input weights separated by spaces: ').split()]
		lowercase__											=int(input('Max weight allowed: '))

		# Function Call
		calc_profit(profit, weight, max_weight)
