import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
	import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__			:				Union[str, Any]		=	logging.get_logger(__name__)

lowercase__			:				Union[str, Any]		=	{'vocab_file': 'vocab.txt'}

lowercase__			:				List[Any]		=	{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

lowercase__			:				Optional[int]		=	{
    'openbmb/cpm-ant-10b': 1_0_2_4,
}

def    SCREAMING_SNAKE_CASE_     (						snake_case__							)     ->	Tuple:
							lowerCAmelCase			=		collections.OrderedDict()
							with open(snake_case__   ,  '''r'''   ,  encoding='''utf-8'''							) as reader:
														lowerCAmelCase			=		reader.readlines()
							for index, token in enumerate(snake_case__							):
														lowerCAmelCase			=		token.rstrip('''\n'''							)
														lowerCAmelCase			=		index
							return vocab




class    lowercase_  (  __snake_case						):



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE="<unk>"		,			__SCREAMING_SNAKE_CASE=200				)						->Union[str, Any]:
								lowerCAmelCase			=		vocab
								lowerCAmelCase			=		unk_token
								lowerCAmelCase			=		max_input_chars_per_word




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->str:
								lowerCAmelCase			=		list(__SCREAMING_SNAKE_CASE				)
								if len(__SCREAMING_SNAKE_CASE				) > self.max_input_chars_per_word:
															return [self.unk_token]

								lowerCAmelCase			=		0
								lowerCAmelCase			=		[]
								while start < len(__SCREAMING_SNAKE_CASE				):
															lowerCAmelCase			=		len(__SCREAMING_SNAKE_CASE				)
															lowerCAmelCase			=		None
															while start < end:
																						lowerCAmelCase			=		''''''.join(chars[start:end]				)
																						if substr in self.vocab:
																													lowerCAmelCase			=		substr
																													break
																						end -= 1
															if cur_substr is None:
																						sub_tokens.append(self.unk_token				)
																						start += 1
															else:
																						sub_tokens.append(__SCREAMING_SNAKE_CASE				)
																						lowerCAmelCase			=		end

								return sub_tokens




class    lowercase_  (  __snake_case						):



	"""simple docstring"""





	UpperCAmelCase_  :					Union[str, Any]   =				VOCAB_FILES_NAMES
	UpperCAmelCase_  :					Optional[int]   =				PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase_  :					Dict   =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase_  :					str   =				["""input_ids""", """attention_mask"""]
	UpperCAmelCase_  :					Any   =				False


	def __init__(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE="<d>"		,			__SCREAMING_SNAKE_CASE="</d>"		,			__SCREAMING_SNAKE_CASE="<s>"		,			__SCREAMING_SNAKE_CASE="</s>"		,			__SCREAMING_SNAKE_CASE="<pad>"		,			__SCREAMING_SNAKE_CASE="<unk>"		,			__SCREAMING_SNAKE_CASE="</n>"		,			__SCREAMING_SNAKE_CASE="</_>"		,			__SCREAMING_SNAKE_CASE="left"		,			**__SCREAMING_SNAKE_CASE		,			)						->Union[str, Any]:
								requires_backends(self		,			['''jieba''']				)
								super().__init__(
								    bod_token=__SCREAMING_SNAKE_CASE		,			eod_token=__SCREAMING_SNAKE_CASE		,			bos_token=__SCREAMING_SNAKE_CASE		,			eos_token=__SCREAMING_SNAKE_CASE		,			pad_token=__SCREAMING_SNAKE_CASE		,			unk_token=__SCREAMING_SNAKE_CASE		,			line_token=__SCREAMING_SNAKE_CASE		,			space_token=__SCREAMING_SNAKE_CASE		,			padding_side=__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE		,			)
								lowerCAmelCase			=		bod_token
								lowerCAmelCase			=		eod_token
								lowerCAmelCase			=		load_vocab(__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		self.encoder[space_token]
								lowerCAmelCase			=		self.encoder[line_token]

								del self.encoder[space_token]
								del self.encoder[line_token]

								lowerCAmelCase			=		collections.OrderedDict(sorted(self.encoder.items()		,			key=lambda __SCREAMING_SNAKE_CASE				: x[1]				)				)
								lowerCAmelCase			=		{v: k for k, v in self.encoder.items()}

								lowerCAmelCase			=		WordpieceTokenizer(vocab=self.encoder		,			unk_token=self.unk_token				)


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[Any]:
								return self.encoder[self.bod_token]


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								return self.encoder[self.eod_token]


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								return self.encoder["\n"]


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								return len(self.encoder				)


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								return dict(self.encoder		,			**self.added_tokens_encoder				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->List[Any]:
								lowerCAmelCase			=		[]
								for x in jieba.cut(__SCREAMING_SNAKE_CASE		,			cut_all=__SCREAMING_SNAKE_CASE				):
															output_tokens.extend(self.wordpiece_tokenizer.tokenize(__SCREAMING_SNAKE_CASE				)				)
								return output_tokens


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)						->str:
								lowerCAmelCase			=		[i for i in token_ids if i >= 0]
								lowerCAmelCase			=		[
								    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
								]
								return super()._decode(__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Optional[Any]:
								return token in self.encoder


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->str:
								return "".join(__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Union[str, Any]:
								return self.encoder.get(__SCREAMING_SNAKE_CASE		,			self.encoder.get(self.unk_token				)				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE				)						->Tuple:
								return self.decoder.get(__SCREAMING_SNAKE_CASE		,			self.unk_token				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None				)						->Tuple[str]:






								if os.path.isdir(__SCREAMING_SNAKE_CASE				):
															lowerCAmelCase			=		os.path.join(
															    __SCREAMING_SNAKE_CASE		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
								else:
															lowerCAmelCase			=		(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
								lowerCAmelCase			=		0
								if " " in self.encoder:
															lowerCAmelCase			=		self.encoder[''' ''']
															del self.encoder[" "]
								if "\n" in self.encoder:
															lowerCAmelCase			=		self.encoder['''\n''']
															del self.encoder["\n"]
								lowerCAmelCase			=		collections.OrderedDict(sorted(self.encoder.items()		,			key=lambda __SCREAMING_SNAKE_CASE				: x[1]				)				)
								with open(__SCREAMING_SNAKE_CASE		,			'''w'''		,			encoding='''utf-8'''				) as writer:
															for token, token_index in self.encoder.items():
																						if index != token_index:
																													logger.warning(
																													    F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
																													    ''' Please check that the vocabulary is not corrupted!'''				)
																													lowerCAmelCase			=		token_index
																						writer.write(token + '''\n'''				)
																						index += 1
								return (vocab_file,)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None				)						->List[int]:






								if token_ids_a is None:
															return [self.bos_token_id] + token_ids_a
								return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = False				)						->List[int]:






								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__SCREAMING_SNAKE_CASE		,			token_ids_a=__SCREAMING_SNAKE_CASE		,			already_has_special_tokens=__SCREAMING_SNAKE_CASE				)

								if token_ids_a is not None:
															return [1] + ([0] * len(__SCREAMING_SNAKE_CASE				)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE				))
								return [1] + ([0] * len(__SCREAMING_SNAKE_CASE				))





'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase			:				Union[str, Any]    		=			{
    'text_branch': 'text_model',
    'audio_branch': 'audio_model.audio_encoder',
    'attn': 'attention.self',
    'self.proj': 'output.dense',
    'attention.self_mask': 'attn_mask',
    'mlp.fc1': 'intermediate.dense',
    'mlp.fc2': 'output.dense',
    'norm1': 'layernorm_before',
    'norm2': 'layernorm_after',
    'bn0': 'batch_norm',
}

lowercase			:				Tuple    		=			AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   Tuple					=						create_model(
        '''HTSAT-tiny'''		,				'''roberta'''		,				snake_case__		,				precision='''fp32'''		,				device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''		,				enable_fusion=snake_case__		,				fusion_type='''aff_2d''' if enable_fusion else None		,				)
    return model, model_cfg







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{}

    A       :   str					=						R'''.*sequential.(\d+).*'''
    A       :   Union[str, Any]					=						R'''.*_projection.(\d+).*'''

    for key, value in state_dict.items():
        # check if any key needs to be modified
        for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
            if key_to_modify in key:
                A       :   Any					=						key.replace(snake_case__		,				snake_case__						)

        if re.match(snake_case__		,				snake_case__						):
            # replace sequential layers with list
            A       :   Any					=						re.match(snake_case__		,				snake_case__						).group(1						)

            A       :   List[str]					=						key.replace(F'sequential.{sequential_layer}.'		,				F'layers.{int(snake_case__						)//3}.linear.'						)
        elif re.match(snake_case__		,				snake_case__						):
            A       :   Union[str, Any]					=						int(re.match(snake_case__		,				snake_case__						).group(1						)						)

            # Because in CLAP they use `nn.Sequential`...
            A       :   str					=						1 if projecton_layer == 0 else 2

            A       :   Optional[Any]					=						key.replace(F'_projection.{projecton_layer}.'		,				F'_projection.linear{transformers_projection_layer}.'						)

        if "audio" and "qkv" in key:
            # split qkv into query key and value
            A       :   int					=						value
            A       :   List[Any]					=						mixed_qkv.size(0						) // 3

            A       :   Union[str, Any]					=						mixed_qkv[:qkv_dim]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim : qkv_dim * 2]
            A       :   Optional[int]					=						mixed_qkv[qkv_dim * 2 :]

            A       :   Tuple					=						query_layer
            A       :   Union[str, Any]					=						key_layer
            A       :   Optional[int]					=						value_layer
        else:
            A       :   Dict					=						value

    return model_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A,  A       :   int					=						init_clap(snake_case__		,				enable_fusion=snake_case__						)

    clap_model.eval()
    A       :   str					=						clap_model.state_dict()
    A       :   Union[str, Any]					=						rename_state_dict(snake_case__						)

    A       :   Tuple					=						ClapConfig()
    A       :   str					=						enable_fusion
    A       :   str					=						ClapModel(snake_case__						)

    # ignore the spectrogram embedding layer
    model.load_state_dict(snake_case__		,				strict=snake_case__						)

    model.save_pretrained(snake_case__						)
    transformers_config.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				List[str]    		=			argparse.ArgumentParser()
      parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
      lowercase			:				Tuple    		=			parser.parse_args()

      convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)




