

'''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       (				__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       List[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       (				__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  []
		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       (				__lowerCAmelCase		:      List[Any]     ):

		"""simple docstring"""

		snake_case__						:       int       						=  []
		token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''')     )
		return token




def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Dict       						=  []
		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       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      str     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  '''imagenet-1k-id2label.json'''
		snake_case__						:       Optional[Any]       						=  10_00

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

		snake_case__						:       List[str]       						=  idalabel
		snake_case__						:       Tuple       						=  {v: k for k, v in idalabel.items()}

		snake_case__						:       str       						=  CvtConfig(num_labels=__UpperCamelCase   ,							idalabel=__UpperCamelCase   ,							labelaid=__UpperCamelCase     )

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

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

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

		snake_case__						:       Optional[Any]       						=  CvtForImageClassification(__UpperCamelCase     )
		snake_case__						:       Any       						=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''     )
		snake_case__						:       List[str]       						=  image_size
		snake_case__						:       Tuple       						=  torch.load(__UpperCamelCase   ,							map_location=torch.device('''cpu'''     )     )

		snake_case__						:       int       						=  OrderedDict()
		snake_case__						:       Tuple       						=  []

		for idx in range(len(config.depth     )     ):
				if config.cls_token[idx]:
						snake_case__						:       List[str]       						=  list_of_state_dict + cls_token(__UpperCamelCase     )
				snake_case__						:       Any       						=  list_of_state_dict + embeddings(__UpperCamelCase     )
				for cnt in range(config.depth[idx]     ):
						snake_case__						:       Dict       						=  list_of_state_dict + attention(__UpperCamelCase   ,							__UpperCamelCase     )

		snake_case__						:       Any       						=  list_of_state_dict + final()
		for gg in list_of_state_dict:
				print(__UpperCamelCase     )
		for i in range(len(__UpperCamelCase     )     ):
				snake_case__						:       int       						=  original_weights[list_of_state_dict[i][1]]

		model.load_state_dict(__UpperCamelCase     )
		model.save_pretrained(__UpperCamelCase     )
		image_processor.save_pretrained(__UpperCamelCase     )


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

if __name__ == "__main__":
					lowerCAmelCase__						:       Dict											=						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."""
					)

					lowerCAmelCase__						:       Optional[Any]											=						parser.parse_args()
					convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)






'''simple docstring'''


from bisect import bisect
from itertools import accumulate




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  sorted(zip(__lowerCAmelCase   ,							__lowerCAmelCase     )   ,							key=lambda __lowerCAmelCase     : x[0] / x[1]   ,							reverse=__lowerCAmelCase     )
		snake_case__						,       snake_case__						:       Union[str, Any]       						=  [i[0] for i in r], [i[1] for i in r]
		snake_case__						:       Dict       						=  list(accumulate(__lowerCAmelCase     )     )
		snake_case__						:       Optional[int]       						=  bisect(__lowerCAmelCase   ,							__lowerCAmelCase     )
		return (
		    0
		    if k == 0
		    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
		    if k != n
		    else sum(vl[:k]     )
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



