

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase							:  List[Any]   	=						logging.get_logger(__name__)

lowerCAmelCase							:  Union[str, Any]   	=						{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     '''gpt_neo'''
			_UpperCAmelCase   :							Dict										=     ['''past_key_values''']
			_UpperCAmelCase   :							List[Any]										=     {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     List[Any]=5_0257   ,						lowerCAmelCase__  :     Dict=2048   ,						lowerCAmelCase__  :     List[Any]=2048   ,						lowerCAmelCase__  :     int=24   ,						lowerCAmelCase__  :     List[Any]=[[["global", "local"], 12]]   ,						lowerCAmelCase__  :     Optional[Any]=16   ,						lowerCAmelCase__  :     Any=None   ,						lowerCAmelCase__  :     int=256   ,						lowerCAmelCase__  :     List[Any]="gelu_new"   ,						lowerCAmelCase__  :     List[Any]=0.0   ,						lowerCAmelCase__  :     Union[str, Any]=0.0   ,						lowerCAmelCase__  :     Optional[Any]=0.0   ,						lowerCAmelCase__  :     List[str]=0.1   ,						lowerCAmelCase__  :     List[str]=1E-5   ,						lowerCAmelCase__  :     Dict=0.02   ,						lowerCAmelCase__  :     str=True   ,						lowerCAmelCase__  :     int=5_0256   ,						lowerCAmelCase__  :     Tuple=5_0256   ,						**lowerCAmelCase__  :     List[str]   ,						):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       vocab_size
									SCREAMING_SNAKE_CASE_: Dict  		=       max_position_embeddings
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hidden_size
									SCREAMING_SNAKE_CASE_: Any  		=       num_layers
									SCREAMING_SNAKE_CASE_: Any  		=       num_heads
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       intermediate_size
									SCREAMING_SNAKE_CASE_: int  		=       window_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       activation_function
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       resid_dropout
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       embed_dropout
									SCREAMING_SNAKE_CASE_: Any  		=       attention_dropout
									SCREAMING_SNAKE_CASE_: Tuple  		=       classifier_dropout
									SCREAMING_SNAKE_CASE_: int  		=       layer_norm_epsilon
									SCREAMING_SNAKE_CASE_: Tuple  		=       initializer_range
									SCREAMING_SNAKE_CASE_: Tuple  		=       use_cache

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       bos_token_id
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       eos_token_id

									SCREAMING_SNAKE_CASE_: List[Any]  		=       attention_types
									SCREAMING_SNAKE_CASE_: int  		=       self.expand_attention_types_params(lowerCAmelCase__)

									if len(self.attention_layers) != self.num_layers:
															raise ValueError(
															    "Configuration for convolutional module is incorrect. "
															    "It is required that `len(config.attention_layers)` == `config.num_layers` "
															    F"but is `len(config.attention_layers) = {len(self.attention_layers)}`, "
															    F"`config.num_layers = {self.num_layers}`. "
															    "`config.attention_layers` is prepared using `config.attention_types`. "
															    "Please verify the value of `config.attention_types` argument.")

									super().__init__(bos_token_id=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__   ,						**lowerCAmelCase__)




			@staticmethod
			def        _SCREAMING_SNAKE_CASE			( lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       []
									for item in attention_types:
															for _ in range(item[1]):
																					attentions.extend(item[0])
									return attentions







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						import torch

						SCREAMING_SNAKE_CASE_: str  		=       input.size()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       shape[dimension]

						SCREAMING_SNAKE_CASE_: List[str]  		=       torch.arange(0				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       torch.div(sizedim - size				,	_UpperCAmelCase				,	rounding_mode="floor"    ) + 1
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.arange(_UpperCAmelCase    ) + low_indices[:min_length][:, None]

						SCREAMING_SNAKE_CASE_: List[str]  		=       [slice(_UpperCAmelCase    )] * rank
						SCREAMING_SNAKE_CASE_: List[str]  		=       indices
						SCREAMING_SNAKE_CASE_: List[Any]  		=       input[s]

						SCREAMING_SNAKE_CASE_: List[Any]  		=       list(range(0				,	rank + 1    )    )
						perm.append(perm.pop(dimension + 1    )    )

						return sliced.permute(_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						import torch

						SCREAMING_SNAKE_CASE_: Dict  		=       torch.arange(1				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Dict  		=       torch.remainder(_UpperCAmelCase				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       remainders == 0
						SCREAMING_SNAKE_CASE_: List[Any]  		=       candidates[divisor_indices]
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.max(_UpperCAmelCase    )
						return largest_divisor, torch.div(_UpperCAmelCase				,	_UpperCAmelCase				,	rounding_mode="floor"    )



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
									if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase__   ,						direction="inputs")
															SCREAMING_SNAKE_CASE_: Dict  		=       {0: "batch", 1: "past_sequence + sequence"}
									else:
															SCREAMING_SNAKE_CASE_: Tuple  		=       {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self._config.num_heads



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     PreTrainedTokenizer   ,						lowerCAmelCase__  :     int = -1   ,						lowerCAmelCase__  :     int = -1   ,						lowerCAmelCase__  :     bool = False   ,						lowerCAmelCase__  :     Optional[TensorType] = None   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       super(lowerCAmelCase__   ,						self).generate_dummy_inputs(
									    lowerCAmelCase__   ,						batch_size=lowerCAmelCase__   ,						seq_length=lowerCAmelCase__   ,						is_pair=lowerCAmelCase__   ,						framework=lowerCAmelCase__)

									# We need to order the input in the way they appears in the forward()
									SCREAMING_SNAKE_CASE_: Tuple  		=       OrderedDict({"input_ids": common_inputs["input_ids"]})

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
															else:
																					import torch

																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					SCREAMING_SNAKE_CASE_: int  		=       seqlen + 2
																					SCREAMING_SNAKE_CASE_: List[str]  		=       (
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       [
																					    (torch.zeros(lowerCAmelCase__), torch.zeros(lowerCAmelCase__)) for _ in range(self.num_layers)
																					]

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       common_inputs["attention_mask"]
									if self.use_past:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ordered_inputs["attention_mask"].dtype
															SCREAMING_SNAKE_CASE_: Tuple  		=       torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(lowerCAmelCase__   ,						lowerCAmelCase__   ,						dtype=lowerCAmelCase__)]   ,						dim=1)

									return ordered_inputs




			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return 13









import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  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""",
}

lowerCAmelCase							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       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:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

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

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

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

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  Tuple   	=						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""")
						lowerCAmelCase							:  int   	=						parser.parse_args()

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






