





import argparse

import torch

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


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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""projector.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""projector.bias"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.post_net.linear.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.post_net.linear.bias"""]
		return model






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				Dict       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__       , config=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.linear.weight"""]
		SCREAMING_SNAKE_CASE__								=   downstream_dict["""model.linear.bias"""]
		return model






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

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






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   torch.load(UpperCamelCase__       , map_location="""cpu"""     )

		SCREAMING_SNAKE_CASE__								=   checkpoint["""Downstream"""]

		SCREAMING_SNAKE_CASE__								=   WavaVecaConfig.from_pretrained(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   WavaVecaFeatureExtractor.from_pretrained(
		    UpperCamelCase__       , return_attention_mask=UpperCamelCase__       , do_normalize=UpperCamelCase__     )

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

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

		hf_feature_extractor.save_pretrained(UpperCamelCase__     )
		hf_model.save_pretrained(UpperCamelCase__     )


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






import argparse
import datetime






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   {
		    """0""": """Sunday""",
		    """1""": """Monday""",
		    """2""": """Tuesday""",
		    """3""": """Wednesday""",
		    """4""": """Thursday""",
		    """5""": """Friday""",
		    """6""": """Saturday""",
		}

		SCREAMING_SNAKE_CASE__								=   {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

		# Validate
		if not 0 < len(UpperCamelCase__     ) < 11:
				raise ValueError("""Must be 10 characters long"""     )

		# Get month
		SCREAMING_SNAKE_CASE__								=   int(date_input[0] + date_input[1]     )
		# Validate
		if not 0 < m < 13:
				raise ValueError("""Month must be between 1 - 12"""     )

		SCREAMING_SNAKE_CASE__								=   date_input[2]
		# Validate
		if sep_a not in ["-", "/"]:
				raise ValueError("""Date separator must be '-' or '/'"""     )

		# Get day
		SCREAMING_SNAKE_CASE__								=   int(date_input[3] + date_input[4]     )
		# Validate
		if not 0 < d < 32:
				raise ValueError("""Date must be between 1 - 31"""     )

		# Get second separator
		SCREAMING_SNAKE_CASE__								=   date_input[5]
		# Validate
		if sep_a not in ["-", "/"]:
				raise ValueError("""Date separator must be '-' or '/'"""     )

		# Get year
		SCREAMING_SNAKE_CASE__								=   int(date_input[6] + date_input[7] + date_input[8] + date_input[9]     )
		# Arbitrary year range
		if not 45 < y < 8_500:
				raise ValueError(
				    """Year out of range. There has to be some sort of limit...right?"""     )

		# Get datetime obj for validation
		SCREAMING_SNAKE_CASE__								=   datetime.date(int(UpperCamelCase__     )       , int(UpperCamelCase__     )       , int(UpperCamelCase__     )     )

		# Start math
		if m <= 2:
				SCREAMING_SNAKE_CASE__								=   y - 1
				SCREAMING_SNAKE_CASE__								=   m + 12
		# maths var
		SCREAMING_SNAKE_CASE__								=   int(str(UpperCamelCase__     )[:2]     )
		SCREAMING_SNAKE_CASE__								=   int(str(UpperCamelCase__     )[2:]     )
		SCREAMING_SNAKE_CASE__								=   int(2.6 * m - 5.3_9     )
		SCREAMING_SNAKE_CASE__								=   int(c / 4     )
		SCREAMING_SNAKE_CASE__								=   int(k / 4     )
		SCREAMING_SNAKE_CASE__								=   int(d + k     )
		SCREAMING_SNAKE_CASE__								=   int(t + u + v + x     )
		SCREAMING_SNAKE_CASE__								=   int(z - (2 * c)     )
		SCREAMING_SNAKE_CASE__								=   round(w % 7     )
		# End math

		# Validate math
		if f != convert_datetime_days[dt_ck.weekday()]:
				raise AssertionError("""The date was evaluated incorrectly. Contact developer."""     )

		# Response
		SCREAMING_SNAKE_CASE__								=   f'''Your date {date_input}, is a {days[str(UpperCamelCase__     )]}!'''
		return response


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_lowerCamelCase 					=      argparse.ArgumentParser(
	    description=(
	        'Find out what day of the week nearly any date is or was. Enter '
	        'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
	    )
	)
	parser.add_argument(
	    'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
	)
	_lowerCamelCase 					=      parser.parse_args()
	zeller(args.date_input)