






'''simple docstring'''







import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A	:			Dict         =logging.get_logger(__name__)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Union[str, Any]         =       MobileNetVaConfig(layer_norm_eps=0.001      )

							if "_quant" in model_name:
														raise ValueError("""Quantized models are not supported."""      )

							lowerCamelCase__					:	Optional[int]         =       re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""				,			UpperCamelCase      )
							if matches:
														lowerCamelCase__					:	Optional[int]         =       float(matches[1]      )
														lowerCamelCase__					:	Optional[int]         =       int(matches[2]      )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							lowerCamelCase__					:	List[Any]         =       1001
							lowerCamelCase__					:	Any         =       """imagenet-1k-id2label.json"""
							lowerCamelCase__					:	Union[str, Any]         =       """huggingface/label-files"""
							lowerCamelCase__					:	List[Any]         =       json.load(open(hf_hub_download(UpperCamelCase				,			UpperCamelCase				,			repo_type="""dataset"""      )				,			"""r"""      )      )
							lowerCamelCase__					:	str         =       {int(UpperCamelCase      ) + 1: v for k, v in idalabel.items()}
							lowerCamelCase__					:	Dict         =       """background"""
							lowerCamelCase__					:	Tuple         =       idalabel
							lowerCamelCase__					:	Dict         =       {v: k for k, v in idalabel.items()}

							return config





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	int         =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase__					:	List[str]         =       Image.open(requests.get(UpperCamelCase				,			stream=UpperCamelCase      ).raw      )
							return im





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase=False      )      ->       Tuple:
							lowerCamelCase__					:	Union[str, Any]         =       get_mobilenet_va_config(UpperCamelCase      )

							# Load 🤗 model
							lowerCamelCase__					:	Optional[Any]         =       MobileNetVaForImageClassification(UpperCamelCase      ).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							lowerCamelCase__					:	Optional[Any]         =       MobileNetVaImageProcessor(
							    crop_size={"""width""": config.image_size, """height""": config.image_size}				,			size={"""shortest_edge""": config.image_size + 32}				,			)
							lowerCamelCase__					:	Any         =       image_processor(images=prepare_img()				,			return_tensors="""pt"""      )
							lowerCamelCase__					:	str         =       model(**UpperCamelCase      )
							lowerCamelCase__					:	Dict         =       outputs.logits

							assert logits.shape == (1, 1001)

							if model_name == "mobilenet_v1_1.0_224":
														lowerCamelCase__					:	Optional[Any]         =       torch.tensor([-4.1739, -1.1233, 3.1205]      )
							elif model_name == "mobilenet_v1_0.75_192":
														lowerCamelCase__					:	Union[str, Any]         =       torch.tensor([-3.9440, -2.3141, -0.3333]      )
							else:
														lowerCamelCase__					:	Tuple         =       None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]				,			UpperCamelCase				,			atol=1E-4      )

							Path(UpperCamelCase      ).mkdir(exist_ok=UpperCamelCase      )
							print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
							model.save_pretrained(UpperCamelCase      )
							print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
							image_processor.save_pretrained(UpperCamelCase      )

							if push_to_hub:
														print("""Pushing to the hub..."""      )
														lowerCamelCase__					:	Optional[Any]         =       """google/""" + model_name
														image_processor.push_to_hub(UpperCamelCase      )
														model.push_to_hub(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Any         =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''mobilenet_v1_1.0_224''',
			    type=str,
			    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
			)
			parser.add_argument(
			    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			_A	:			Optional[Any]         =parser.parse_args()
			convert_movilevit_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)





'''simple docstring'''


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCamelCase__					=   {
    '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__					=   AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')






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






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Dict	    =					{}

		_UpperCAmelCase						:				str	    =					R".*sequential.(\d+).*"
		_UpperCAmelCase						:				Any	    =					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						:				Union[str, Any]	    =					key.replace(__lowerCAmelCase					,       __lowerCAmelCase      )

				if re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						# replace sequential layers with list
						_UpperCAmelCase						:				List[Any]	    =					re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )

						_UpperCAmelCase						:				Optional[int]	    =					key.replace(F"""sequential.{sequential_layer}."""					,       F"""layers.{int(__lowerCAmelCase      )//3}.linear."""      )
				elif re.match(__lowerCAmelCase					,       __lowerCAmelCase      ):
						_UpperCAmelCase						:				List[str]	    =					int(re.match(__lowerCAmelCase					,       __lowerCAmelCase      ).group(1      )      )

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

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

						_UpperCAmelCase						:				Union[str, Any]	    =					mixed_qkv[:qkv_dim]
						_UpperCAmelCase						:				int	    =					mixed_qkv[qkv_dim : qkv_dim * 2]
						_UpperCAmelCase						:				Optional[int]	    =					mixed_qkv[qkv_dim * 2 :]

						_UpperCAmelCase						:				List[Any]	    =					query_layer
						_UpperCAmelCase						:				int	    =					key_layer
						_UpperCAmelCase						:				Any	    =					value_layer
				else:
						_UpperCAmelCase						:				Dict	    =					value

		return model_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[str]	    =					init_clap(__lowerCAmelCase					,       enable_fusion=__lowerCAmelCase      )

		clap_model.eval()
		_UpperCAmelCase						:				List[str]	    =					clap_model.state_dict()
		_UpperCAmelCase						:				str	    =					rename_state_dict(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					ClapConfig()
		_UpperCAmelCase						:				str	    =					enable_fusion
		_UpperCAmelCase						:				Union[str, Any]	    =					ClapModel(__lowerCAmelCase      )

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

		model.save_pretrained(__lowerCAmelCase      )
		transformers_config.save_pretrained(__lowerCAmelCase      )


if __name__ == "__main__":
						lowerCamelCase__					=   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__					=   parser.parse_args()

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



