
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowercase  :					List[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""",
}

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Optional[Any]:
       lowercase	,			lowercase							:     Any       	=      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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Optional[int]       	=      {}

       lowercase							:     Optional[int]       	=      R""".*sequential.(\d+).*"""
       lowercase							:     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:
                            lowercase							:     Dict       	=      key.replace(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

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

                     lowercase							:     Optional[Any]       	=      key.replace(f"sequential.{sequential_layer}."					,	f"layers.{int(SCREAMING_SNAKE_CASE__				)//3}.linear."				)
              elif re.match(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     lowercase							:     Dict       	=      int(re.match(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				).group(1				)				)

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

                     lowercase							:     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
                     lowercase							:     Dict       	=      value
                     lowercase							:     Union[str, Any]       	=      mixed_qkv.size(0				) // 3

                     lowercase							:     Any       	=      mixed_qkv[:qkv_dim]
                     lowercase							:     str       	=      mixed_qkv[qkv_dim : qkv_dim * 2]
                     lowercase							:     Optional[Any]       	=      mixed_qkv[qkv_dim * 2 :]

                     lowercase							:     Tuple       	=      query_layer
                     lowercase							:     Dict       	=      key_layer
                     lowercase							:     Union[str, Any]       	=      value_layer
              else:
                     lowercase							:     Tuple       	=      value

       return model_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Tuple:
       lowercase	,			lowercase							:     List[str]       	=      init_clap(SCREAMING_SNAKE_CASE__					,	enable_fusion=SCREAMING_SNAKE_CASE__				)

       clap_model.eval()
       lowercase							:     List[Any]       	=      clap_model.state_dict()
       lowercase							:     str       	=      rename_state_dict(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Union[str, Any]       	=      ClapConfig()
       lowercase							:     List[str]       	=      enable_fusion
       lowercase							:     Tuple       	=      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__":
  lowercase  :					int						    =							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""")
  lowercase  :					Union[str, Any]						    =							parser.parse_args()

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


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class __snake_case		(      unittest.TestCase     ):
    _a		:						Optional[int]=				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      hf_hub_download(
               repo_id="""nateraw/video-demo"""		,filename="""archery.mp4"""		,repo_type="""dataset"""      )
           lowercase							:     List[str]       	=      VideoClassificationPipeline(model=snake_case		,image_processor=snake_case		,top_k=2      )
           lowercase							:     Dict       	=      [
               example_video_filepath,
               """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
           ]
           return video_classifier, examples
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           for example in examples:
                  lowercase							:     int       	=      video_classifier(snake_case      )

                  self.assertEqual(
                      snake_case		,[
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                          {"""score""": ANY(snake_case      ), """label""": ANY(snake_case      )},
                      ]		,)
    @require_torch
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
           lowercase							:     str       	=      VideoMAEFeatureExtractor(
               size={"""shortest_edge""": 10}		,crop_size={"""height""": 10, """width""": 10}      )
           lowercase							:     List[Any]       	=      pipeline(
               """video-classification"""		,model=snake_case		,feature_extractor=snake_case		,frame_sampling_rate=4      )

           lowercase							:     Dict       	=      hf_hub_download(repo_id="""nateraw/video-demo"""		,filename="""archery.mp4"""		,repo_type="""dataset"""      )
           lowercase							:     Any       	=      video_classifier(snake_case		,top_k=2      )
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}]		,)

           lowercase							:     str       	=      video_classifier(
               [
                   video_file_path,
                   video_file_path,
               ]		,top_k=2		,)
           self.assertEqual(
               nested_simplify(snake_case		,decimals=4      )		,[
                   [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
                   [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
               ]		,)
    @require_tf
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           pass
