
"""simple docstring"""





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_a														=		{
    """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														=		AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")



def 				lowerCamelCase__		(      __snake_case,					__snake_case=False     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase			,  _UpperCamelCase										=  create_model(
					    '''HTSAT-tiny''',					'''roberta''',					__snake_case,					precision='''fp32''',					device='''cuda:0''' if torch.cuda.is_available() else '''cpu''',					enable_fusion=__snake_case,					fusion_type='''aff_2d''' if enable_fusion else None,					)
					return model, model_cfg



def 				lowerCamelCase__		(      __snake_case     )							->		Any:


					"""simple docstring"""





					_UpperCamelCase										=  {}

					_UpperCamelCase										=  r'''.*sequential.(\d+).*'''
					_UpperCamelCase										=  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										=  key.replace(__snake_case,					__snake_case     )

										if re.match(__snake_case,					__snake_case     ):
															# replace sequential layers with list
															_UpperCamelCase										=  re.match(__snake_case,					__snake_case     ).group(1     )

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

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

															_UpperCamelCase										=  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										=  value
															_UpperCamelCase										=  mixed_qkv.size(0     ) // 3

															_UpperCamelCase										=  mixed_qkv[:qkv_dim]
															_UpperCamelCase										=  mixed_qkv[qkv_dim : qkv_dim * 2]
															_UpperCamelCase										=  mixed_qkv[qkv_dim * 2 :]

															_UpperCamelCase										=  query_layer
															_UpperCamelCase										=  key_layer
															_UpperCamelCase										=  value_layer
										else:
															_UpperCamelCase										=  value

					return model_state_dict



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case=False     )							->		int:


					"""simple docstring"""





					_UpperCamelCase			,  _UpperCamelCase										=  init_clap(__snake_case,					enable_fusion=__snake_case     )

					clap_model.eval()
					_UpperCamelCase										=  clap_model.state_dict()
					_UpperCamelCase										=  rename_state_dict(__snake_case     )

					_UpperCamelCase										=  ClapConfig()
					_UpperCamelCase										=  enable_fusion
					_UpperCamelCase										=  ClapModel(__snake_case     )

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

					model.save_pretrained(__snake_case     )
					transformers_config.save_pretrained(__snake_case     )


if __name__ == "__main__":
				_a														=		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														=		parser.parse_args()

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








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






