"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


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





def       a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Optional[Any]=False  ):





       '''simple docstring'''

       lowercase__					,   lowercase__    :	List[Any]     					=			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       a_  (   _lowerCAmelCase						:				List[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			{}

       lowercase__    :	Any     					=			R'.*sequential.(\d+).*'
       lowercase__    :	Tuple     					=			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:
                            lowercase__    :	Tuple     					=			key.replace(_lowercase     ,				_lowercase  )

              if re.match(_lowercase     ,				_lowercase  ):
                     # replace sequential layers with list
                     lowercase__    :	Tuple     					=			re.match(_lowercase     ,				_lowercase  ).group(1  )

                     lowercase__    :	int     					=			key.replace(f"""sequential.{sequential_layer}."""     ,				f"""layers.{int(_lowercase  )//3}.linear."""  )
              elif re.match(_lowercase     ,				_lowercase  ):
                     lowercase__    :	str     					=			int(re.match(_lowercase     ,				_lowercase  ).group(1  )  )

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

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

                     lowercase__    :	Optional[Any]     					=			mixed_qkv[:qkv_dim]
                     lowercase__    :	Dict     					=			mixed_qkv[qkv_dim : qkv_dim * 2]
                     lowercase__    :	List[Any]     					=			mixed_qkv[qkv_dim * 2 :]

                     lowercase__    :	Optional[Any]     					=			query_layer
                     lowercase__    :	Union[str, Any]     					=			key_layer
                     lowercase__    :	Optional[int]     					=			value_layer
              else:
                     lowercase__    :	str     					=			value

       return model_state_dict





def       a_  (   _lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Tuple=False  ):





       '''simple docstring'''

       lowercase__					,   lowercase__    :	Optional[Any]     					=			init_clap(_lowercase     ,				enable_fusion=_lowercase  )

       clap_model.eval()
       lowercase__    :	Tuple     					=			clap_model.state_dict()
       lowercase__    :	List[Any]     					=			rename_state_dict(_lowercase  )

       lowercase__    :	Optional[Any]     					=			ClapConfig()
       lowercase__    :	Dict     					=			enable_fusion
       lowercase__    :	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__":
       _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     :		str											=					parser.parse_args()

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


"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }





@require_torch
@require_vision
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'do_center_crop'							)							)
             self.assertTrue(hasattr(a	,    'center_crop'							)							)
             self.assertTrue(hasattr(a	,    'do_flip_channel_order'							)							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2	,    crop_size=8_4							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 4_2}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 8_4, 'width': 8_4}							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	List[Any]     					=			image_processing(a	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	List[Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Any     					=			image_processing(a	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )


      def 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			image_processing(image_inputs[0]	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

             # Test batched
             lowercase__    :	Tuple     					=			image_processing(a	,    return_tensors='pt'							).pixel_values
             self.assertEqual(
                 encoded_images.shape	,    (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )	,    )

