




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 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					BlenderbotSmallTokenizer
						UpperCamelCase     						=					False

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	Tuple								=["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
										__magic_name__	:	Optional[Any]								=dict(zip(__snake_case    , range(len(__snake_case ) ) ) )

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

										__magic_name__	:	Dict								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										__magic_name__	:	int								=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(__snake_case ) + """\n""" )
										with open(self.merges_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write("""\n""".join(__snake_case ) )

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										kwargs.update(self.special_tokens_map )
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								="""adapt act apte"""
										__magic_name__	:	str								="""adapt act apte"""
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BlenderbotSmallTokenizer(self.vocab_file    , self.merges_file    , **self.special_tokens_map )
										__magic_name__	:	List[str]								="""adapt act apte"""
										__magic_name__	:	Optional[int]								=["""adapt""", """act""", """ap@@""", """te"""]
										__magic_name__	:	List[Any]								=tokenizer.tokenize(__snake_case )
										self.assertListEqual(__snake_case    , __snake_case )

										__magic_name__	:	int								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

										__magic_name__	:	Any								=[0, 1, 2, 3, 4, 5]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case )    , __snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										assert tok("""sam""" ).input_ids == [13_84]
										__magic_name__	:	Tuple								="""I am a small frog."""
										__magic_name__	:	Tuple								=tok([src_text]    , padding=__snake_case    , truncation=__snake_case )["""input_ids"""]
										__magic_name__	:	Optional[Any]								=tok.batch_decode(__snake_case    , skip_special_tokens=__snake_case    , clean_up_tokenization_spaces=__snake_case )[0]
										assert src_text != decoded  # I wish it did!
										assert decoded == "i am a small frog ."


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										__magic_name__	:	int								="""I am a small frog ."""
										__magic_name__	:	List[Any]								="""."""
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]

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










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_		:				Optional[int]									=     {
    "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",
}

UpperCAmelCase_		:				Optional[int]									=     AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Any								=create_model(
				    """HTSAT-tiny"""	,     """roberta"""	,     lowerCamelCase	,     precision="""fp32"""	,     device="""cuda:0""" if torch.cuda.is_available() else """cpu"""	,     enable_fusion=lowerCamelCase	,     fusion_type="""aff_2d""" if enable_fusion else None	,     )
				return model, model_cfg




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								={}

				__magic_name__	:	Union[str, Any]								=R""".*sequential.(\d+).*"""
				__magic_name__	:	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:
																__magic_name__	:	Union[str, Any]								=key.replace(lowerCamelCase	,     lowerCamelCase   )

								if re.match(lowerCamelCase	,     lowerCamelCase   ):
												# replace sequential layers with list
												__magic_name__	:	str								=re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )

												__magic_name__	:	Optional[Any]								=key.replace(F"sequential.{sequential_layer}."	,     F"layers.{int(lowerCamelCase   )//3}.linear."   )
								elif re.match(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Any								=int(re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )   )

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

												__magic_name__	:	List[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
												__magic_name__	:	Dict								=value
												__magic_name__	:	List[str]								=mixed_qkv.size(0   ) // 3

												__magic_name__	:	Optional[Any]								=mixed_qkv[:qkv_dim]
												__magic_name__	:	Any								=mixed_qkv[qkv_dim : qkv_dim * 2]
												__magic_name__	:	Optional[Any]								=mixed_qkv[qkv_dim * 2 :]

												__magic_name__	:	str								=query_layer
												__magic_name__	:	int								=key_layer
												__magic_name__	:	str								=value_layer
								else:
												__magic_name__	:	int								=value

				return model_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Tuple								=init_clap(lowerCamelCase	,     enable_fusion=lowerCamelCase   )

				clap_model.eval()
				__magic_name__	:	int								=clap_model.state_dict()
				__magic_name__	:	Optional[Any]								=rename_state_dict(lowerCamelCase   )

				__magic_name__	:	Any								=ClapConfig()
				__magic_name__	:	Optional[Any]								=enable_fusion
				__magic_name__	:	Tuple								=ClapModel(lowerCamelCase   )

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

				model.save_pretrained(lowerCamelCase   )
				transformers_config.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[int]									=     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")
							UpperCAmelCase_		:				Dict									=     parser.parse_args()

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




