





import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
							if is_torch_available():
														import torch
							if is_tf_available():
														import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase__						:			Optional[Any]									=logging.get_logger(__name__)

UpperCAmelCase__						:			Union[str, Any]									={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__						:			str									={
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__						:			Union[str, Any]									={
    '''Salesforce/codegen-350M-mono''': 20_48,
}

class       __A     (						a    ):
				__A      =				VOCAB_FILES_NAMES
				__A      =				PRETRAINED_VOCAB_FILES_MAP
				__A      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__A      =				["""input_ids""", """attention_mask"""]
				__A      =				CodeGenTokenizer


				def __init__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_="<|endoftext|>"   , UpperCAmelCase_="<|endoftext|>"   , UpperCAmelCase_="<|endoftext|>"   , UpperCAmelCase_=False   , **UpperCAmelCase_   , ):
								super().__init__(
								    UpperCAmelCase_   , UpperCAmelCase_   , tokenizer_file=UpperCAmelCase_   , unk_token=UpperCAmelCase_   , bos_token=UpperCAmelCase_   , eos_token=UpperCAmelCase_   , add_prefix_space=UpperCAmelCase_   , **UpperCAmelCase_   , )

								if kwargs.pop("""add_bos_token"""   , UpperCAmelCase_							):
												lowerCamelCase	 =kwargs.pop("""name_or_path"""   , """"""							)
												raise ValueError(
												    """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
												    """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
												    f"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
												    f"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
												    """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
												    """ so that the fast tokenizer works correctly."""							)

								lowerCamelCase	 =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
								if pre_tok_state.get("""add_prefix_space"""   , UpperCAmelCase_							) != add_prefix_space:
												lowerCamelCase	 =getattr(UpperCAmelCase_   , pre_tok_state.pop("""type"""							)							)
												lowerCamelCase	 =add_prefix_space
												lowerCamelCase	 =pre_tok_class(**UpperCAmelCase_							)

								lowerCamelCase	 =add_prefix_space


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.get("""is_split_into_words"""   , UpperCAmelCase_							)
								assert self.add_prefix_space or not is_split_into_words, (
								    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								lowerCamelCase	 =kwargs.get("""is_split_into_words"""   , UpperCAmelCase_							)

								assert self.add_prefix_space or not is_split_into_words, (
								    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*UpperCAmelCase_   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None							):
								lowerCamelCase	 =self._tokenizer.model.save(UpperCAmelCase_   , name=UpperCAmelCase_							)
								return tuple(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = False   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , **UpperCAmelCase_   , ):
								lowerCamelCase	 =super().decode(
								    token_ids=UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_   , clean_up_tokenization_spaces=UpperCAmelCase_   , **UpperCAmelCase_   , )

								if truncate_before_pattern is not None and len(UpperCAmelCase_							) > 0:
												lowerCamelCase	 =self.truncate(UpperCAmelCase_   , UpperCAmelCase_							)

								return decoded_text


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								def find_re(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
												lowerCamelCase	 =pattern.search(UpperCAmelCase_   , UpperCAmelCase_							)
												return m.start() if m else -1

								lowerCamelCase	 =[re.compile(UpperCAmelCase_   , re.MULTILINE							) for pattern in truncate_before_pattern]

								lowerCamelCase	 =list(re.finditer("""^print"""   , UpperCAmelCase_   , re.MULTILINE							)							)

								if len(UpperCAmelCase_							) > 1:
												lowerCamelCase	 =completion[: prints[1].start()]

								lowerCamelCase	 =list(re.finditer("""^def"""   , UpperCAmelCase_   , re.MULTILINE							)							)

								if len(UpperCAmelCase_							) > 1:
												lowerCamelCase	 =completion[: defs[1].start()]

								lowerCamelCase	 =0

								lowerCamelCase	 =[
								    pos for pos in [find_re(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							) for terminal in terminals] if pos != -1
								]

								if len(UpperCAmelCase_							) > 0:
												return completion[: min(UpperCAmelCase_							)]
								else:
												return completion












import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				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(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=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__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





