



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    __lowerCamelCase   (  lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       Any						=	[]
				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    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       Optional[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    __lowerCamelCase   (  lowerCamelCase__		):

				"""simple docstring"""


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


def    __lowerCamelCase   (  ):

				"""simple docstring"""


				lowercase__	:       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    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       List[Any]						=	"imagenet-1k-id2label.json"
				lowercase__	:       Union[str, Any]						=	1_000

				lowercase__	:       Any						=	"huggingface/label-files"
				lowercase__	:       Union[str, Any]						=	num_labels
				lowercase__	:       Tuple						=	json.load(open(cached_download(hf_hub_url(UpperCAmelCase_	,					UpperCAmelCase_	,					repo_type="dataset"		)		)	,					"r"		)		)
				lowercase__	:       List[Any]						=	{int(UpperCAmelCase_		): v for k, v in idalabel.items()}

				lowercase__	:       Union[str, Any]						=	idalabel
				lowercase__	:       str						=	{v: k for k, v in idalabel.items()}

				lowercase__	:       Optional[Any]						=	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":
								lowercase__	:       Optional[int]						=	[1, 2, 10]

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

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								lowercase__	:       Any						=	[2, 2, 20]
								lowercase__	:       List[Any]						=	[3, 12, 16]
								lowercase__	:       Union[str, Any]						=	[192, 768, 1_024]

				lowercase__	:       Tuple						=	CvtForImageClassification(UpperCAmelCase_		)
				lowercase__	:       List[str]						=	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"		)
				lowercase__	:       int						=	image_size
				lowercase__	:       List[Any]						=	torch.load(UpperCAmelCase_	,					map_location=torch.device("cpu"		)		)

				lowercase__	:       Optional[int]						=	OrderedDict()
				lowercase__	:       int						=	[]

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

				lowercase__	:       Tuple						=	list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(UpperCAmelCase_		)
				for i in range(len(UpperCAmelCase_		)		):
								lowercase__	:       Dict						=	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__	    =    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=3_8_4,
				    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__	    =    parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









"""simple docstring"""







from __future__ import annotations

def       _lowerCamelCase				(							UpperCAmelCase_ :       float,	UpperCAmelCase_ :       float,	UpperCAmelCase_ :       float,	)	-> tuple:






	"""simple docstring"""





	if (electron_conc, hole_conc, intrinsic_conc).count(0     ) != 1:
		raise ValueError("You cannot supply more or less than 2 values"     )
	elif electron_conc < 0:
		raise ValueError("Electron concentration cannot be negative in a semiconductor"     )
	elif hole_conc < 0:
		raise ValueError("Hole concentration cannot be negative in a semiconductor"     )
	elif intrinsic_conc < 0:
		raise ValueError(
		    "Intrinsic concentration cannot be negative in a semiconductor"     )
	elif electron_conc == 0:
		return (
		    "electron_conc",
		    intrinsic_conc**2 / hole_conc,
		)
	elif hole_conc == 0:
		return (
		    "hole_conc",
		    intrinsic_conc**2 / electron_conc,
		)
	elif intrinsic_conc == 0:
		return (
		    "intrinsic_conc",
		    (electron_conc * hole_conc) ** 0.5,
		)
	else:
		return (-1, -1)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




