
"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       []
							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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []
							token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token") )
							return token
def   __UpperCamelCase   (					)  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       []
							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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       "imagenet-1k-id2label.json"
							__snake_case     			=       10_00

							__snake_case     			=       "huggingface/label-files"
							__snake_case     			=       num_labels
							__snake_case     			=       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    repo_type="dataset" ) )							,    "r" ) )
							__snake_case     			=       {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}

							__snake_case     			=       idalabel
							__snake_case     			=       {v: k for k, v in idalabel.items()}

							__snake_case     			=       __snake_case     			=       CvtConfig(num_labels=SCREAMING_SNAKE_CASE							,    idalabel=SCREAMING_SNAKE_CASE							,    labelaid=SCREAMING_SNAKE_CASE )

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

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

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

							__snake_case     			=       CvtForImageClassification(SCREAMING_SNAKE_CASE )
							__snake_case     			=       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
							__snake_case     			=       image_size
							__snake_case     			=       torch.load(SCREAMING_SNAKE_CASE							,    map_location=torch.device("cpu" ) )

							__snake_case     			=       OrderedDict()
							__snake_case     			=       []

							for idx in range(len(config.depth ) ):
														if config.cls_token[idx]:
																					__snake_case     			=       list_of_state_dict + cls_token(SCREAMING_SNAKE_CASE )
														__snake_case     			=       list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE )
														for cnt in range(config.depth[idx] ):
																					__snake_case     			=       list_of_state_dict + attention(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							__snake_case     			=       list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(SCREAMING_SNAKE_CASE )
							for i in range(len(SCREAMING_SNAKE_CASE ) ):
														__snake_case     			=       original_weights[list_of_state_dict[i][1]]

							model.load_state_dict(SCREAMING_SNAKE_CASE )
							model.save_pretrained(SCREAMING_SNAKE_CASE )
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE )


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

if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      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."""
		)

		_SCREAMING_SNAKE_CASE	       =      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

from dataclasses import dataclass







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					float
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bool:







							"""simple docstring"""


							def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
														if node is None:
																					return True

														if not isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					return False

														try:
																					float(node.data )
														except (TypeError, ValueError):
																					return False

														return is_valid_tree(node.left ) and is_valid_tree(node.right )

							if not is_valid_tree(SCREAMING_SNAKE_CASE ):
														raise ValueError(
														    "Each node should be type of TreeNode and data should be float." )

							def is_binary_search_tree_recursive_check(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) -> bool:

														if node is None:
																					return True

														return (
														    left_bound < node.data < right_bound
														    and is_binary_search_tree_recursive_check(node.left							,    SCREAMING_SNAKE_CASE							,    node.data )
														    and is_binary_search_tree_recursive_check(
														        node.right							,    node.data							,    SCREAMING_SNAKE_CASE )
														)

							return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE							,    -float("inf" )							,    float("inf" ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
