
'''simple docstring'''



import argparse

import torch

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


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


def A (__lowerCamelCase :Tuple					,      __lowerCamelCase :Dict					,      __lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					UniSpeechSatForSequenceClassification.from_pretrained(__lowerCamelCase					,      config=__lowerCamelCase						)
						_lowerCAmelCase    		=					downstream_dict["""projector.weight"""]
						_lowerCAmelCase    		=					downstream_dict["""projector.bias"""]
						_lowerCAmelCase    		=					downstream_dict["""model.post_net.linear.weight"""]
						_lowerCAmelCase    		=					downstream_dict["""model.post_net.linear.bias"""]
						return model


def A (__lowerCamelCase :int					,      __lowerCamelCase :Optional[int]					,      __lowerCamelCase :Any						):
						_lowerCAmelCase    		=					UniSpeechSatForAudioFrameClassification.from_pretrained(__lowerCamelCase					,      config=__lowerCamelCase						)
						_lowerCAmelCase    		=					downstream_dict["""model.linear.weight"""]
						_lowerCAmelCase    		=					downstream_dict["""model.linear.bias"""]
						return model


def A (__lowerCamelCase :Any					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :List[str]						):
						_lowerCAmelCase    		=					UniSpeechSatForXVector.from_pretrained(__lowerCamelCase					,      config=__lowerCamelCase						)
						_lowerCAmelCase    		=					downstream_dict["""connector.weight"""]
						_lowerCAmelCase    		=					downstream_dict["""connector.bias"""]
						for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
												_lowerCAmelCase    		=					downstream_dict[
												    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
												]
												_lowerCAmelCase    		=					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

						_lowerCAmelCase    		=					downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
						_lowerCAmelCase    		=					downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
						_lowerCAmelCase    		=					downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
						_lowerCAmelCase    		=					downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
						_lowerCAmelCase    		=					downstream_dict["""objective.W"""]
						return model


@torch.no_grad()
def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any					,      __lowerCamelCase :Dict						):
						_lowerCAmelCase    		=					torch.load(__lowerCamelCase					,      map_location="""cpu"""						)

						_lowerCAmelCase    		=					checkpoint["""Downstream"""]

						_lowerCAmelCase    		=					UniSpeechSatConfig.from_pretrained(__lowerCamelCase						)
						_lowerCAmelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
						    __lowerCamelCase					,      return_attention_mask=__lowerCamelCase					,      do_normalize=__lowerCamelCase						)

						_lowerCAmelCase    		=					hf_config.architectures[0]
						if arch.endswith("""ForSequenceClassification"""						):
												_lowerCAmelCase    		=					convert_classification(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
						elif arch.endswith("""ForAudioFrameClassification"""						):
												_lowerCAmelCase    		=					convert_diarization(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
						elif arch.endswith("""ForXVector"""						):
												_lowerCAmelCase    		=					convert_xvector(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
						else:
												raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'						)

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

						hf_feature_extractor.save_pretrained(__lowerCamelCase						)
						hf_model.save_pretrained(__lowerCamelCase						)


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


'''simple docstring'''



import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_lowercase          =						False
try:
						_lowercase          =						_is_package_available("""google.colab""")
except ModuleNotFoundError:
						pass




@input.register
class     UpperCAmelCase_	:






						'''simple docstring'''






						def __init__(   self       ,  _lowercase = None       ,  _lowercase = [] ):

												"""simple docstring"""



												_lowerCAmelCase    		=					0
												_lowerCAmelCase    		=					choices
												_lowerCAmelCase    		=					prompt
												if sys.platform == "win32":
																		_lowerCAmelCase    		=					"""*"""
												else:
																		_lowerCAmelCase    		=					"""➔ """




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = "" ):

												"""simple docstring"""



												if sys.platform != "win32":
																		writeColor(self.choices[index]       ,  32       ,  _lowercase )
												else:
																		forceWrite(self.choices[index]       ,  _lowercase )




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												if index == self.position:
																		forceWrite(F' {self.arrow_char} ' )
																		self.write_choice(_lowercase )
												else:
																		forceWrite(F'    {self.choices[index]}' )
												reset_cursor()




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase = 1 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.position
												if direction == Direction.DOWN:
																		if self.position + 1 >= len(self.choices ):
																								return
																		self.position += num_spaces
												else:
																		if self.position - 1 < 0:
																								return
																		self.position -= num_spaces
												clear_line()
												self.print_choice(_lowercase )
												move_cursor(_lowercase       ,  direction.name )
												self.print_choice(self.position )




						@input.mark(KEYMAP["""up"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.move_direction(Direction.UP )




						@input.mark(KEYMAP["""down"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.move_direction(Direction.DOWN )




						@input.mark(KEYMAP["""newline"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												move_cursor(len(self.choices ) - self.position       ,  """DOWN""" )
												return self.position




						@input.mark(KEYMAP["""interrupt"""] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												move_cursor(len(self.choices ) - self.position       ,  """DOWN""" )
												raise KeyboardInterrupt




						@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					int(chr(self.current_selection ) )
												_lowerCAmelCase    		=					index - self.position
												if index == self.position:
																		return
												if index < len(self.choices ):
																		if self.position > index:
																								self.move_direction(Direction.UP       ,  -movement )
																		elif self.position < index:
																								self.move_direction(Direction.DOWN       ,  _lowercase )
																		else:
																								return
												else:
																		return




						def    _lowercase      (   self       ,  _lowercase = 0 ):

												"""simple docstring"""



												if self.prompt:
																		linebreak()
																		forceWrite(self.prompt       ,  """\n""" )
																		if in_colab:
																								forceWrite("""Please input a choice index (starting from 0), and press enter"""       ,  """\n""" )
																		else:
																								forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter"""       ,  """\n""" )
												_lowerCAmelCase    		=					default_choice
												for i in range(len(self.choices ) ):
																		self.print_choice(_lowercase )
																		forceWrite("""\n""" )
												move_cursor(len(self.choices ) - self.position       ,  """UP""" )
												with cursor.hide():
																		while True:
																								if in_colab:
																														try:
																																				_lowerCAmelCase    		=					int(builtins.input() )
																														except ValueError:
																																				_lowerCAmelCase    		=					default_choice
																								else:
																														_lowerCAmelCase    		=					self.handle_input()
																								if choice is not None:
																														reset_cursor()
																														for _ in range(len(self.choices ) + 1 ):
																																				move_cursor(1       ,  """UP""" )
																																				clear_line()
																														self.write_choice(_lowercase       ,  """\n""" )
																														return choice
