
'''simple docstring'''




import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import RegNetForImageClassification, RegNetModel
      from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class  A				:





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=32						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=10						,			SCREAMING_SNAKE_CASE=[10, 20, 30, 40]						,			SCREAMING_SNAKE_CASE=[1, 1, 2, 1]						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE="relu"						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=None						,			) ->				Dict:






         """simple docstring"""






         A       :   List[str]					=						parent
         A       :   int					=						batch_size
         A       :   Optional[int]					=						image_size
         A       :   int					=						num_channels
         A       :   List[Any]					=						embeddings_size
         A       :   str					=						hidden_sizes
         A       :   Any					=						depths
         A       :   List[str]					=						is_training
         A       :   Any					=						use_labels
         A       :   Any					=						hidden_act
         A       :   Tuple					=						num_labels
         A       :   List[str]					=						scope
         A       :   Union[str, Any]					=						len(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Any					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         A       :   Optional[Any]					=						None
         if self.use_labels:
             A       :   str					=						ids_tensor([self.batch_size]						,			self.num_labels	)

         A       :   List[Any]					=						self.get_config()

         return config, pixel_values, labels





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         return RegNetConfig(
             num_channels=self.num_channels						,			embeddings_size=self.embeddings_size						,			hidden_sizes=self.hidden_sizes						,			depths=self.depths						,			hidden_act=self.hidden_act						,			num_labels=self.num_labels						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A       :   int					=						RegNetModel(config=SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   List[str]					=						model(SCREAMING_SNAKE_CASE	)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape						,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   str					=						self.num_labels
         A       :   List[Any]					=						RegNetForImageClassification(SCREAMING_SNAKE_CASE	)
         model.to(SCREAMING_SNAKE_CASE	)
         model.eval()
         A       :   Optional[Any]					=						model(SCREAMING_SNAKE_CASE						,			labels=SCREAMING_SNAKE_CASE	)
         self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Tuple					=						self.prepare_config_and_inputs()
         A,  A,  A       :   Optional[int]					=						config_and_inputs
         A       :   Union[str, Any]					=						{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class  A				(							__snake_case    ,			__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
     __magic_name__						=		(
         {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
         if is_torch_available()
         else {}
     )

     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False
     __magic_name__						=		False





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Any					=						RegNetModelTester(self	)
         A       :   List[str]					=						ConfigTester(self						,			config_class=SCREAMING_SNAKE_CASE						,			has_text_modality=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return





     @unittest.skip(reason='''RegNet does not use inputs_embeds'''	)
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         pass





     @unittest.skip(reason='''RegNet does not support input and output embeddings'''	)
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         pass





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A,  A       :   Dict					=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A       :   Optional[int]					=						model_class(SCREAMING_SNAKE_CASE	)
             A       :   Dict					=						inspect.signature(model.forward	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             A       :   Dict					=						[*signature.parameters.keys()]

             A       :   Optional[Any]					=						['''pixel_values''']
             self.assertListEqual(arg_names[:1]						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   Optional[int]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A,  A       :   Optional[Any]					=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A       :   Tuple					=						model_class(config=SCREAMING_SNAKE_CASE	)
             for name, module in model.named_modules():
                 if isinstance(SCREAMING_SNAKE_CASE						,			(nn.BatchNormad, nn.GroupNorm)	):
                     self.assertTrue(
                         torch.all(module.weight == 1	)						,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,			)
                     self.assertTrue(
                         torch.all(module.bias == 0	)						,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,			)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         def check_hidden_states_output(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   Dict					=						model_class(SCREAMING_SNAKE_CASE	)
             model.to(SCREAMING_SNAKE_CASE	)
             model.eval()

             with torch.no_grad():
                 A       :   Any					=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)	)

             A       :   Any					=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             A       :   Any					=						self.model_tester.num_stages
             self.assertEqual(len(SCREAMING_SNAKE_CASE	)						,			expected_num_stages + 1	)

             # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]	)						,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]						,			)

         A,  A       :   str					=						self.model_tester.prepare_config_and_inputs_for_common()
         A       :   Dict					=						['''basic''', '''bottleneck''']
         for model_class in self.all_model_classes:
             for layer_type in layers_type:
                 A       :   Dict					=						layer_type
                 A       :   Union[str, Any]					=						True
                 check_hidden_states_output(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 A       :   Dict					=						True

                 check_hidden_states_output(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A       :   Dict					=						RegNetModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE	)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   Union[str, Any]					=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image







@require_torch
@require_vision
class  A				(							unittest.TestCase						):





     @cached_property
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
             if is_vision_available()
             else None
         )





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						self.default_image_processor
         A       :   int					=						prepare_img()
         A       :   Optional[Any]					=						image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE	)

         # forward pass
         with torch.no_grad():
             A       :   List[str]					=						model(**SCREAMING_SNAKE_CASE	)

         # verify the logits
         A       :   Dict					=						torch.Size((1, 1000)	)
         self.assertEqual(outputs.logits.shape						,			SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						torch.tensor([-0.4_180, -1.5_051, -3.4_836]	).to(SCREAMING_SNAKE_CASE	)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]						,			SCREAMING_SNAKE_CASE						,			atol=1e-4	)	)







import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, 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""",
}

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: 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:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

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

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

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

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

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

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


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

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






