


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_snake_case  :	Tuple			      =       logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase   )
class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						List[Any]					,			**lowerCamelCase :						Any       )			->					Optional[Any]:
														super().__init__(**lowerCamelCase       )
														requires_backends(self					,			"vision"       )
														requires_backends(self					,			"torch"       )

														if self.framework != "pt":
																					raise ValueError(F'The {self.__class__} is only available in PyTorch.'       )

														self.check_model_type(lowerCamelCase       )

							def 					__snake_case      (							self :						Dict					,			**lowerCamelCase :						Any       )			->					Optional[Any]:
														__snake_case	:    Optional[Any]											=     {}
														__snake_case	:    List[str]											=     {}
														__snake_case	:    Optional[Any]											=     {}
														# preprocess args
														if "points_per_batch" in kwargs:
																					__snake_case	:    Any											=     kwargs["points_per_batch"]
														if "points_per_crop" in kwargs:
																					__snake_case	:    Tuple											=     kwargs["points_per_crop"]
														if "crops_n_layers" in kwargs:
																					__snake_case	:    Tuple											=     kwargs["crops_n_layers"]
														if "crop_overlap_ratio" in kwargs:
																					__snake_case	:    List[str]											=     kwargs["crop_overlap_ratio"]
														if "crop_n_points_downscale_factor" in kwargs:
																					__snake_case	:    int											=     kwargs["crop_n_points_downscale_factor"]
														# postprocess args
														if "pred_iou_thresh" in kwargs:
																					__snake_case	:    Optional[int]											=     kwargs["pred_iou_thresh"]
														if "stability_score_offset" in kwargs:
																					__snake_case	:    Dict											=     kwargs["stability_score_offset"]
														if "mask_threshold" in kwargs:
																					__snake_case	:    str											=     kwargs["mask_threshold"]
														if "stability_score_thresh" in kwargs:
																					__snake_case	:    List[str]											=     kwargs["stability_score_thresh"]
														if "crops_nms_thresh" in kwargs:
																					__snake_case	:    Dict											=     kwargs["crops_nms_thresh"]
														if "output_rle_mask" in kwargs:
																					__snake_case	:    Tuple											=     kwargs["output_rle_mask"]
														if "output_bboxes_mask" in kwargs:
																					__snake_case	:    Optional[Any]											=     kwargs["output_bboxes_mask"]
														return preprocess_kwargs, forward_params, postprocess_kwargs

							def __call__(							self :						List[Any]					,			lowerCamelCase :						List[str]					,			*lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						int=None					,			lowerCamelCase :						Dict=None					,			**lowerCamelCase :						int       )			->					Optional[int]:
														return super().__call__(lowerCamelCase					,			*lowerCamelCase					,			num_workers=lowerCamelCase					,			batch_size=lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						int					,			lowerCamelCase :						Any=64					,			lowerCamelCase :						int = 0					,			lowerCamelCase :						float = 512 / 1500					,			lowerCamelCase :						Optional[int] = 32					,			lowerCamelCase :						Optional[int] = 1					,			)			->					int:
														__snake_case	:    Any											=     load_image(lowerCamelCase       )
														__snake_case	:    List[Any]											=     self.image_processor.size["longest_edge"]
														__snake_case				,    __snake_case				,    __snake_case				,    __snake_case	:    Optional[int]											=     self.image_processor.generate_crop_boxes(
														    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
														__snake_case	:    Tuple											=     self.image_processor(images=lowerCamelCase					,			return_tensors="pt"       )

														with self.device_placement():
																					if self.framework == "pt":
																												__snake_case	:    List[Any]											=     self.get_inference_context()
																												with inference_context():
																																			__snake_case	:    Any											=     self._ensure_tensor_on_device(lowerCamelCase					,			device=self.device       )
																																			__snake_case	:    str											=     self.model.get_image_embeddings(model_inputs.pop("pixel_values"       )       )
																																			__snake_case	:    Tuple											=     image_embeddings

														__snake_case	:    str											=     grid_points.shape[1]
														__snake_case	:    List[Any]											=     points_per_batch if points_per_batch is not None else n_points

														if points_per_batch <= 0:
																					raise ValueError(
																					    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
																					    "To return all points at once, set points_per_batch to None"       )

														for i in range(0					,			lowerCamelCase					,			lowerCamelCase       ):
																					__snake_case	:    int											=     grid_points[:, i : i + points_per_batch, :, :]
																					__snake_case	:    Any											=     input_labels[:, i : i + points_per_batch]
																					__snake_case	:    Dict											=     i == n_points - points_per_batch
																					yield {
																					    "input_points": batched_points,
																					    "input_labels": labels,
																					    "input_boxes": crop_boxes,
																					    "is_last": is_last,
																					    **model_inputs,
																					}

							def 					__snake_case      (							self :						int					,			lowerCamelCase :						Dict					,			lowerCamelCase :						List[str]=0.88					,			lowerCamelCase :						List[Any]=0.95					,			lowerCamelCase :						Union[str, Any]=0					,			lowerCamelCase :						str=1					,			)			->					Tuple:
														__snake_case	:    Any											=     model_inputs.pop("input_boxes"       )
														__snake_case	:    Tuple											=     model_inputs.pop("is_last"       )
														__snake_case	:    Union[str, Any]											=     model_inputs.pop("original_sizes"       ).tolist()
														__snake_case	:    List[Any]											=     model_inputs.pop("reshaped_input_sizes"       ).tolist()

														__snake_case	:    Any											=     self.model(**lowerCamelCase       )

														# post processing happens here in order to avoid CPU GPU copies of ALL the masks
														__snake_case	:    Optional[Any]											=     model_outputs["pred_masks"]
														__snake_case	:    List[str]											=     self.image_processor.post_process_masks(
														    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			binarize=lowerCamelCase       )
														__snake_case	:    Optional[int]											=     model_outputs["iou_scores"]
														__snake_case				,    __snake_case				,    __snake_case	:    Dict											=     self.image_processor.filter_masks(
														    masks[0]					,			iou_scores[0]					,			original_sizes[0]					,			input_boxes[0]					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			)
														return {
														    "masks": masks,
														    "is_last": is_last,
														    "boxes": boxes,
														    "iou_scores": iou_scores,
														}






							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						int=False					,			lowerCamelCase :						Union[str, Any]=False					,			lowerCamelCase :						Tuple=0.7					,			)			->					List[str]:
														__snake_case	:    Tuple											=     []
														__snake_case	:    Union[str, Any]											=     []
														__snake_case	:    int											=     []
														for model_output in model_outputs:
																					all_scores.append(model_output.pop("iou_scores"       )       )
																					all_masks.extend(model_output.pop("masks"       )       )
																					all_boxes.append(model_output.pop("boxes"       )       )

														__snake_case	:    List[str]											=     torch.cat(lowerCamelCase       )
														__snake_case	:    int											=     torch.cat(lowerCamelCase       )
														__snake_case				,    __snake_case				,    __snake_case				,    __snake_case	:    int											=     self.image_processor.post_process_for_mask_generation(
														    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    List[str]											=     defaultdict(lowerCamelCase       )
														for output in model_outputs:
																					for k, v in output.items():
																												extra[k].append(lowerCamelCase       )

														__snake_case	:    str											=     {}
														if output_rle_mask:
																					__snake_case	:    List[Any]											=     rle_mask

														if output_bboxes_mask:
																					__snake_case	:    Optional[Any]											=     bounding_boxes

														return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	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",
}

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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    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			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    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:
																												__snake_case	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

														if re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					# replace sequential layers with list
																					__snake_case	:    List[Any]											=     re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)

																					__snake_case	:    str											=     key.replace(F'sequential.{sequential_layer}.'							,			F'layers.{int(__lowerCamelCase			)//3}.linear.'			)
														elif re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Any											=     int(re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)			)

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

																					__snake_case	:    Tuple											=     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
																					__snake_case	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     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__":
			_snake_case  :	Union[str, Any]			      =       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")
			_snake_case  :	Tuple			      =       parser.parse_args()

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

