

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_UpperCamelCase			       =			["UtteranceLevel"]
@torch.no_grad()
def 						UpperCamelCase_(			snake_case__:  Optional[Any]  ,    snake_case__:  List[Any]  ,    snake_case__:  Tuple  ,    snake_case__:  Dict   )       -> Optional[int]:
 UpperCAmelCase__							=				torch.load(UpperCamelCase_  ,    map_location='cpu'   )
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"   )

 UpperCAmelCase__							=				checkpoint["""Downstream"""]

 UpperCAmelCase__							=				HubertConfig.from_pretrained(UpperCamelCase_   )
 UpperCAmelCase__							=				HubertForSequenceClassification.from_pretrained(UpperCamelCase_  ,    config=UpperCamelCase_   )
 UpperCAmelCase__							=				WavaVecaFeatureExtractor.from_pretrained(
     UpperCamelCase_  ,    return_attention_mask=UpperCamelCase_  ,    do_normalize=UpperCamelCase_   )

 if hf_congfig.use_weighted_layer_sum:
  UpperCAmelCase__							=				checkpoint["""Featurizer"""]["""weights"""]

 UpperCAmelCase__							=				downstream_dict["""projector.weight"""]
 UpperCAmelCase__							=				downstream_dict["""projector.bias"""]
 UpperCAmelCase__							=				downstream_dict["""model.post_net.linear.weight"""]
 UpperCAmelCase__							=				downstream_dict["""model.post_net.linear.bias"""]

 hf_feature_extractor.save_pretrained(UpperCamelCase_   )
 hf_model.save_pretrained(UpperCamelCase_   )


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)









def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					len(UpperCamelCase_				)
				# Create a stack and push index of fist element to it
				_lowerCAmelCase			:			Tuple        =					[]
				st.append(0				)

				# Span value of first element is always 1
				_lowerCAmelCase			:			List[Any]        =					1

				# Calculate span values for rest of the elements
				for i in range(1					,			UpperCamelCase_				):
								# Pop elements from stack while stack is not
								# empty and top of stack is smaller than price[i]
								while len(UpperCamelCase_				) > 0 and price[st[0]] <= price[i]:
												st.pop()

								# If stack becomes empty, then price[i] is greater
								# than all elements on left of it, i.e. price[0],
								# price[1], ..price[i-1]. Else the price[i]  is
								# greater than elements after top of stack
								_lowerCAmelCase			:			Union[str, Any]        =					i + 1 if len(UpperCamelCase_				) <= 0 else (i - st[0])

								# Push this element to stack
								st.append(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				for i in range(0					,			UpperCamelCase_				):
								print(arr[i]					,			end=""" """				)


# Driver program to test above function
_lowerCamelCase					:     Union[str, Any]					  =  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
_lowerCamelCase					:     List[Any]					  =  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))

