



'''simple docstring'''




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a   :			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""",
}

a   :			Union[str, Any]					  =    AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")



def       __lowerCamelCase  (					_lowercase	,				_lowercase=False		)      ->			Optional[int]:
							UpperCAmelCase     ,     UpperCAmelCase     :		Dict							     =		create_model(
							    """HTSAT-tiny"""	,				"""roberta"""	,				_lowercase	,				precision="""fp32"""	,				device="""cuda:0""" if torch.cuda.is_available() else """cpu"""	,				enable_fusion=_lowercase	,				fusion_type="""aff_2d""" if enable_fusion else None	,				)
							return model, model_cfg



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		str							     =		{}

							UpperCAmelCase     :		Dict							     =		R""".*sequential.(\d+).*"""
							UpperCAmelCase     :		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:
																												UpperCAmelCase     :		str							     =		key.replace(_lowercase	,				_lowercase		)

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

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

																					# Because in CLAP they use `nn.Sequential`...
																					UpperCAmelCase     :		List[Any]							     =		1 if projecton_layer == 0 else 2

																					UpperCAmelCase     :		str							     =		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
																					UpperCAmelCase     :		Any							     =		value
																					UpperCAmelCase     :		Tuple							     =		mixed_qkv.size(0		) // 3

																					UpperCAmelCase     :		Any							     =		mixed_qkv[:qkv_dim]
																					UpperCAmelCase     :		List[str]							     =		mixed_qkv[qkv_dim : qkv_dim * 2]
																					UpperCAmelCase     :		Dict							     =		mixed_qkv[qkv_dim * 2 :]

																					UpperCAmelCase     :		Optional[Any]							     =		query_layer
																					UpperCAmelCase     :		str							     =		key_layer
																					UpperCAmelCase     :		str							     =		value_layer
														else:
																					UpperCAmelCase     :		Any							     =		value

							return model_state_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase=False		)      ->			Any:
							UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		init_clap(_lowercase	,				enable_fusion=_lowercase		)

							clap_model.eval()
							UpperCAmelCase     :		Optional[int]							     =		clap_model.state_dict()
							UpperCAmelCase     :		Tuple							     =		rename_state_dict(_lowercase		)

							UpperCAmelCase     :		str							     =		ClapConfig()
							UpperCAmelCase     :		int							     =		enable_fusion
							UpperCAmelCase     :		List[str]							     =		ClapModel(_lowercase		)

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

							model.save_pretrained(_lowercase		)
							transformers_config.save_pretrained(_lowercase		)


if __name__ == "__main__":
							a   :			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""")
							a   :			Union[str, Any]					  =    parser.parse_args()

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






'''simple docstring'''




from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

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

@add_end_docstrings(__magic_name__			)
class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		**A    )			->			List[str]:
								super().__init__(**A    )

								requires_backends(self		,		"""vision"""    )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == """tf"""
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





	def __call__(  self		,		A		,		**A    )			->			Optional[Any]:
								return super().__call__(A		,		**A    )





	def  _lowercase(  self		,		**A    )			->			Optional[Any]:
								UpperCAmelCase     :		List[Any]							     =		{}
								if "candidate_labels" in kwargs:
															UpperCAmelCase     :		Dict							     =		kwargs["""candidate_labels"""]
								if "hypothesis_template" in kwargs:
															UpperCAmelCase     :		Optional[Any]							     =		kwargs["""hypothesis_template"""]

								return preprocess_params, {}, {}





	def  _lowercase(  self		,		A		,		A=None		,		A="This is a photo of {}."    )			->			Optional[Any]:
								UpperCAmelCase     :		int							     =		load_image(A    )
								UpperCAmelCase     :		List[str]							     =		self.image_processor(images=[image]		,		return_tensors=self.framework    )
								UpperCAmelCase     :		List[str]							     =		candidate_labels
								UpperCAmelCase     :		Tuple							     =		[hypothesis_template.format(A    ) for x in candidate_labels]
								UpperCAmelCase     :		Union[str, Any]							     =		self.tokenizer(A		,		return_tensors=self.framework		,		padding=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		[text_inputs]
								return inputs





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		List[Any]							     =		model_inputs.pop("""candidate_labels"""    )
								UpperCAmelCase     :		Optional[Any]							     =		model_inputs.pop("""text_inputs"""    )
								if isinstance(text_inputs[0]		,		A    ):
															UpperCAmelCase     :		Optional[Any]							     =		text_inputs[0]
								else:
															# Batching case.
															UpperCAmelCase     :		Any							     =		text_inputs[0][0]

								UpperCAmelCase     :		Dict							     =		self.model(**A		,		**A    )

								UpperCAmelCase     :		List[Any]							     =		{
								    """candidate_labels""": candidate_labels,
								    """logits""": outputs.logits_per_image,
								}
								return model_outputs






	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		int							     =		model_outputs.pop("""candidate_labels"""    )
								UpperCAmelCase     :		int							     =		model_outputs["""logits"""][0]
								if self.framework == "pt":
															UpperCAmelCase     :		Optional[int]							     =		logits.softmax(dim=-1    ).squeeze(-1    )
															UpperCAmelCase     :		Any							     =		probs.tolist()
															if not isinstance(A		,		A    ):
																						UpperCAmelCase     :		Any							     =		[scores]
								elif self.framework == "tf":
															UpperCAmelCase     :		List[str]							     =		stable_softmax(A		,		axis=-1    )
															UpperCAmelCase     :		Union[str, Any]							     =		probs.numpy().tolist()
								else:
															raise ValueError(f'''Unsupported framework: {self.framework}'''    )

								UpperCAmelCase     :		Any							     =		[
								    {"""score""": score, """label""": candidate_label}
								    for score, candidate_label in sorted(zip(A		,		A    )		,		key=lambda A    : -x[0]    )
								]
								return result

