





"""simple docstring"""






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification

def      __A   (      a_    :Optional[int])		->		Tuple:
				__a			:    str      							=	[]
				embed.append(
				    (
				        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
				        F"""stage{idx}.patch_embed.proj.weight""",
				    ))
				embed.append(
				    (
				        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
				        F"""stage{idx}.patch_embed.proj.bias""",
				    ))
				embed.append(
				    (
				        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
				        F"""stage{idx}.patch_embed.norm.weight""",
				    ))
				embed.append(
				    (
				        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
				        F"""stage{idx}.patch_embed.norm.bias""",
				    ))
				return embed

def      __A   (      a_    :Dict ,							a_    :Optional[Any])		->		Optional[int]:
				__a			:    int      							=	[]
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
				        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
				    ))
				attention_weights.append(
				    (
				        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
				        F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
				    ))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight"""))
				attention_weights.append(
				    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias"""))
				return attention_weights

def      __A   (      a_    :Tuple)		->		str:
				__a			:    str      							=	[]
				token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token'''))
				return token

def      __A   (      )		->		Union[str, Any]:
				__a			:    Tuple      							=	[]
				head.append(('''layernorm.weight''', '''norm.weight'''))
				head.append(('''layernorm.bias''', '''norm.bias'''))
				head.append(('''classifier.weight''', '''head.weight'''))
				head.append(('''classifier.bias''', '''head.bias'''))
				return head

def      __A   (      a_    :Tuple ,							a_    :Tuple ,							a_    :int ,							a_    :List[str])		->		str:
				__a			:    Union[str, Any]      							=	'''imagenet-1k-id2label.json'''
				__a			:    Optional[int]      							=	10_00

				__a			:    str      							=	'''huggingface/label-files'''
				__a			:    str      							=	num_labels
				__a			:    List[Any]      							=	json.load(open(cached_download(hf_hub_url(a_ ,							a_ ,							repo_type='''dataset''')) ,							'''r'''))
				__a			:    Dict      							=	{int(a_): v for k, v in idalabel.items()}

				__a			:    Tuple      							=	idalabel
				__a			:    str      							=	{v: k for k, v in idalabel.items()}

				__a			:    Any      							=	CvtConfig(num_labels=a_ ,							idalabel=a_ ,							labelaid=a_)

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit('''/''' ,							1)[-1][4:6] == "13":
								__a			:    int      							=	[1, 2, 10]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit('''/''' ,							1)[-1][4:6] == "21":
								__a			:    Optional[Any]      							=	[1, 4, 16]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								__a			:    Tuple      							=	[2, 2, 20]
								__a			:    List[str]      							=	[3, 12, 16]
								__a			:    Optional[Any]      							=	[1_92, 7_68, 10_24]

				__a			:    Dict      							=	CvtForImageClassification(a_)
				__a			:    str      							=	AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''')
				__a			:    List[str]      							=	image_size
				__a			:    Optional[int]      							=	torch.load(a_ ,							map_location=torch.device('''cpu'''))

				__a			:    Any      							=	OrderedDict()
				__a			:    int      							=	[]

				for idx in range(len(config.depth)):
								if config.cls_token[idx]:
												__a			:    int      							=	list_of_state_dict + cls_token(a_)
								__a			:    Tuple      							=	list_of_state_dict + embeddings(a_)
								for cnt in range(config.depth[idx]):
												__a			:    List[str]      							=	list_of_state_dict + attention(a_ ,							a_)

				__a			:    List[Any]      							=	list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(a_)
				for i in range(len(a_)):
								__a			:    List[Any]      							=	original_weights[list_of_state_dict[i][1]]

				model.load_state_dict(a_)
				model.save_pretrained(a_)
				image_processor.save_pretrained(a_)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		A			      =	argparse.ArgumentParser()
		parser.add_argument(
		    '''--cvt_model''',
		    default='''cvt-w24''',
		    type=str,
		    help='''Name of the cvt model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--image_size''',
		    default=384,
		    type=int,
		    help='''Input Image Size''',
		)
		parser.add_argument(
		    '''--cvt_file_name''',
		    default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
		    type=str,
		    help='''Input Image Size''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		A			      =	parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase      					={
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      					=[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      UpperCamelCase      					=["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

      UpperCamelCase      					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






