





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      import torch

      from transformers.modeling_outputs import BaseModelOutput

      from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__UpperCAmelCase       :   Optional[Any]				       =							logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase			)
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Dict							,				**A :    str		):
         super().__init__(**A		)

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

         requires_backends(self							,				"""vision"""		)
         self.check_model_type(A		)




       def __call__(  self :    List[Any]							,				A :    Union[str, "Image.Image", List[Dict[str, Any]]]							,				A :    Union[str, List[str]] = None							,				**A :    Tuple							,				):
         if "text_queries" in kwargs:
           __snake_case:						Optional[int]   =						kwargs.pop("""text_queries"""		)

         if isinstance(A							,				(str, Image.Image)		):
           __snake_case:						Dict   =						{"""image""": image, """candidate_labels""": candidate_labels}
         else:
           __snake_case:						Dict   =						image
         __snake_case:						List[str]   =						super().__call__(A							,				**A		)
         return results




       def 							UpperCAmelCase__ (  self :    str							,				**A :    Tuple		):
         __snake_case:						Dict   =						{}
         if "threshold" in kwargs:
           __snake_case:						Tuple   =						kwargs["""threshold"""]
         if "top_k" in kwargs:
           __snake_case:						Dict   =						kwargs["""top_k"""]
         return {}, {}, postprocess_params




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Tuple		):
         __snake_case:						int   =						load_image(inputs["""image"""]		)
         __snake_case:						Any   =						inputs["""candidate_labels"""]
         if isinstance(A							,				A		):
           __snake_case:						Optional[int]   =						candidate_labels.split(""","""		)

         __snake_case:						List[str]   =						torch.tensor([[image.height, image.width]]							,				dtype=torch.intaa		)
         for i, candidate_label in enumerate(A		):
           __snake_case:						Union[str, Any]   =						self.tokenizer(A							,				return_tensors=self.framework		)
           __snake_case:						List[Any]   =						self.image_processor(A							,				return_tensors=self.framework		)
           yield {
               "is_last": i == len(A		) - 1,
               "target_size": target_size,
               "candidate_label": candidate_label,
               **text_inputs,
               **image_features,
           }




       def 							UpperCAmelCase__ (  self :    str							,				A :    Optional[int]		):
         __snake_case:						Optional[int]   =						model_inputs.pop("""target_size"""		)
         __snake_case:						Optional[int]   =						model_inputs.pop("""candidate_label"""		)
         __snake_case:						int   =						model_inputs.pop("""is_last"""		)

         __snake_case:						Optional[Any]   =						self.model(**A		)

         __snake_case:						List[str]   =						{"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
         return model_outputs




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Any							,				A :    Union[str, Any]=0.1							,				A :    Optional[Any]=None		):
         __snake_case:						Optional[Any]   =						[]
         for model_output in model_outputs:
           __snake_case:						int   =						model_output["""candidate_label"""]
           __snake_case:						List[Any]   =						BaseModelOutput(A		)
           __snake_case:						List[str]   =						self.image_processor.post_process_object_detection(
               outputs=A							,				threshold=A							,				target_sizes=model_output["""target_size"""]		)[0]

           for index in outputs["scores"].nonzero():
             __snake_case:						List[str]   =						outputs["""scores"""][index].item()
             __snake_case:						Any   =						self._get_bounding_box(outputs["""boxes"""][index][0]		)

             __snake_case:						Any   =						{"""score""": score, """label""": label, """box""": box}
             results.append(A		)

         __snake_case:						Optional[int]   =						sorted(A							,				key=lambda A		: x["score"]							,				reverse=A		)
         if top_k:
           __snake_case:						List[str]   =						results[:top_k]

         return results






       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    "torch.Tensor"		):
         if self.framework != "pt":
           raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""		)
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Optional[int]   =						box.int().tolist()
         __snake_case:						Union[str, Any]   =						{
             """xmin""": xmin,
             """ymin""": ymin,
             """xmax""": xmax,
             """ymax""": ymax,
         }
         return bbox













import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase       :   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",
}

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







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Union[str, Any]:
  __snake_case  ,   __snake_case:						int   =						create_model(
      """HTSAT-tiny"""					,					"""roberta"""					,					SCREAMING_SNAKE_CASE__					,					precision="""fp32"""					,					device="""cuda:0""" if torch.cuda.is_available() else """cpu"""					,					enable_fusion=SCREAMING_SNAKE_CASE__					,					fusion_type="""aff_2d""" if enable_fusion else None					,					)
  return model, model_cfg







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Any:
  __snake_case:						Optional[Any]   =						{}

  __snake_case:						int   =						r""".*sequential.(\d+).*"""
  __snake_case:						List[str]   =						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:						Tuple   =						key.replace(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

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

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

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

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

      __snake_case:						Union[str, Any]   =						mixed_qkv[:qkv_dim]
      __snake_case:						Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
      __snake_case:						int   =						mixed_qkv[qkv_dim * 2 :]

      __snake_case:						Optional[Any]   =						query_layer
      __snake_case:						str   =						key_layer
      __snake_case:						int   =						value_layer
    else:
      __snake_case:						Dict   =						value

  return model_state_dict







def 			A__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False)   ->    Optional[Any]:
  __snake_case  ,   __snake_case:						List[str]   =						init_clap(SCREAMING_SNAKE_CASE__					,					enable_fusion=SCREAMING_SNAKE_CASE__)

  clap_model.eval()
  __snake_case:						List[str]   =						clap_model.state_dict()
  __snake_case:						Optional[int]   =						rename_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any   =						ClapConfig()
  __snake_case:						Dict   =						enable_fusion
  __snake_case:						List[str]   =						ClapModel(SCREAMING_SNAKE_CASE__)

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

  model.save_pretrained(SCREAMING_SNAKE_CASE__)
  transformers_config.save_pretrained(SCREAMING_SNAKE_CASE__)


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

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






