



import argparse

import torch

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


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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				__a       =		UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE				,     config=_SCREAMING_SNAKE_CASE    )
				__a       =		downstream_dict["""projector.weight"""]
				__a       =		downstream_dict["""projector.bias"""]
				__a       =		downstream_dict["""model.post_net.linear.weight"""]
				__a       =		downstream_dict["""model.post_net.linear.bias"""]
				return model

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				__a       =		UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE				,     config=_SCREAMING_SNAKE_CASE    )
				__a       =		downstream_dict["""model.linear.weight"""]
				__a       =		downstream_dict["""model.linear.bias"""]
				return model

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""simple docstring"""







				__a       =		UniSpeechSatForXVector.from_pretrained(_SCREAMING_SNAKE_CASE				,     config=_SCREAMING_SNAKE_CASE    )
				__a       =		downstream_dict["""connector.weight"""]
				__a       =		downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
								__a       =		downstream_dict[
								    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
								]
								__a       =		downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			List[Any]    ):
				"""simple docstring"""







				__a       =		torch.load(_SCREAMING_SNAKE_CASE				,     map_location="""cpu"""    )

				__a       =		checkpoint["""Downstream"""]

				__a       =		UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
				__a       =		WavaVecaFeatureExtractor.from_pretrained(
				    _SCREAMING_SNAKE_CASE				,     return_attention_mask=_SCREAMING_SNAKE_CASE				,     do_normalize=_SCREAMING_SNAKE_CASE    )

				__a       =		hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""    ):
								__a       =		convert_classification(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				elif arch.endswith("""ForAudioFrameClassification"""    ):
								__a       =		convert_diarization(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				elif arch.endswith("""ForXVector"""    ):
								__a       =		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:
								__a       =		checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE    )
				hf_model.save_pretrained(_SCREAMING_SNAKE_CASE    )


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 json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	Optional[int]    =BlenderbotSmallTokenizer
							__lowerCamelCase	:	str    =False





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											super().setUp()

											__a       =		["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											__a       =		dict(zip(__lowercase      ,				range(len(__lowercase	)	)	)	)

											__a       =		["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											__a       =		{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""merges_file"""]	)
											with open(self.vocab_file      ,				"""w"""      ,				encoding="""utf-8"""	) as fp:
															fp.write(json.dumps(__lowercase	) + """\n"""	)
											with open(self.merges_file      ,				"""w"""      ,				encoding="""utf-8"""	) as fp:
															fp.write("""\n""".join(__lowercase	)	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				**__lowercase		:		Dict	):

											'''simple docstring'''
											kwargs.update(self.special_tokens_map	)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		"""adapt act apte"""
											__a       =		"""adapt act apte"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map	)
											__a       =		"""adapt act apte"""
											__a       =		["""adapt""", """act""", """ap@@""", """te"""]
											__a       =		tokenizer.tokenize(__lowercase	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											__a       =		[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											__a       =		[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				__lowercase	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											assert tok("""sam"""	).input_ids == [1384]
											__a       =		"""I am a small frog."""
											__a       =		tok([src_text]      ,				padding=__lowercase      ,				truncation=__lowercase	)["""input_ids"""]
											__a       =		tok.batch_decode(__lowercase      ,				skip_special_tokens=__lowercase      ,				clean_up_tokenization_spaces=__lowercase	)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."







							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""	)
											__a       =		"""I am a small frog ."""
											__a       =		"""."""
											__a       =		tok(__lowercase	)["""input_ids"""]
											__a       =		tok(__lowercase	)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]
