






"""simple docstring"""
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
			import timm


if is_torch_available():
			from torch import Tensor







class     UpperCamelCase_ (__A       ,		__A  ):
	__magic_name__			   =       '''pixel_values'''
	__magic_name__			   =       False
	__magic_name__			   =       TimmBackboneConfig





	def __init__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Any			,			**lowerCAmelCase_      :				Tuple		)		->    str:
				requires_backends(self			,			"timm"		)
				super().__init__(lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      config

				if config.backbone is None:
							raise ValueError("backbone is not set in the config. Please set it to a timm model name."		)

				if config.backbone not in timm.list_models():
							raise ValueError(f"""backbone {config.backbone} is not supported by timm."""		)

				if hasattr(lowerCAmelCase_			,			"out_features"		) and config.out_features is not None:
							raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."		)

				UpperCAmelCase_  :				Optional[int]      =      getattr(lowerCAmelCase_			,			"use_pretrained_backbone"			,			lowerCAmelCase_		)
				if pretrained is None:
							raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."		)

				# We just take the final layer by default. This matches the default for the transformers models.
				UpperCAmelCase_  :				Optional[Any]      =      config.out_indices if getattr(lowerCAmelCase_			,			"out_indices"			,			lowerCAmelCase_		) is not None else (-1,)

				UpperCAmelCase_  :				Dict      =      timm.create_model(
				    config.backbone			,			pretrained=lowerCAmelCase_			,			features_only=config.features_only			,			in_chans=config.num_channels			,			out_indices=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
				# These are used to control the output of the model when called. If output_hidden_states is True, then
				# return_layers is modified to include all layers.
				UpperCAmelCase_  :				str      =      self._backbone.return_layers
				UpperCAmelCase_  :				Optional[Any]      =      {layer["module"]: str(lowerCAmelCase_		) for i, layer in enumerate(self._backbone.feature_info.info		)}
				super()._init_backbone(lowerCAmelCase_		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Any			,			lowerCAmelCase_      :				Tuple			,			*lowerCAmelCase_      :				Any			,			**lowerCAmelCase_      :				Optional[int]		)		->    str:
				requires_backends(cls			,			["vision", "timm"]		)
				from ...models.timm_backbone import TimmBackboneConfig

				UpperCAmelCase_  :				List[Any]      =      kwargs.pop("config"			,			TimmBackboneConfig()		)

				UpperCAmelCase_  :				Optional[int]      =      kwargs.pop("use_timm_backbone"			,			lowerCAmelCase_		)
				if not use_timm:
							raise ValueError("use_timm_backbone must be True for timm backbones"		)

				UpperCAmelCase_  :				Dict      =      kwargs.pop("num_channels"			,			config.num_channels		)
				UpperCAmelCase_  :				str      =      kwargs.pop("features_only"			,			config.features_only		)
				UpperCAmelCase_  :				List[str]      =      kwargs.pop("use_pretrained_backbone"			,			config.use_pretrained_backbone		)
				UpperCAmelCase_  :				int      =      kwargs.pop("out_indices"			,			config.out_indices		)
				UpperCAmelCase_  :				Tuple      =      TimmBackboneConfig(
				    backbone=lowerCAmelCase_			,			num_channels=lowerCAmelCase_			,			features_only=lowerCAmelCase_			,			use_pretrained_backbone=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			)
				return super()._from_config(lowerCAmelCase_			,			**lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, Any]		)		->    Optional[int]:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				List[Any]=None			,			lowerCAmelCase_      :				Any=None			,			lowerCAmelCase_      :				Tuple=None			,			**lowerCAmelCase_      :				Optional[Any]		)		->    Union[BackboneOutput, Tuple[Tensor, ...]]:
				UpperCAmelCase_  :				Tuple      =      return_dict if return_dict is not None else self.config.use_return_dict
				UpperCAmelCase_  :				Any      =      (
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				UpperCAmelCase_  :				List[str]      =      output_attentions if output_attentions is not None else self.config.output_attentions

				if output_attentions:
							raise ValueError("Cannot output attentions for timm backbones at the moment"		)

				if output_hidden_states:
							# We modify the return layers to include all the stages of the backbone
							UpperCAmelCase_  :				Dict      =      self._all_layers
							UpperCAmelCase_  :				Any      =      self._backbone(lowerCAmelCase_			,			**lowerCAmelCase_		)
							UpperCAmelCase_  :				Optional[Any]      =      self._return_layers
							UpperCAmelCase_  :				str      =      tuple(hidden_states[i] for i in self.out_indices		)
				else:
							UpperCAmelCase_  :				Tuple      =      self._backbone(lowerCAmelCase_			,			**lowerCAmelCase_		)
							UpperCAmelCase_  :				Dict      =      None

				UpperCAmelCase_  :				List[str]      =      tuple(lowerCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      tuple(lowerCAmelCase_		) if hidden_states is not None else None

				if not return_dict:
							UpperCAmelCase_  :				List[str]      =      (feature_maps,)
							if output_hidden_states:
										UpperCAmelCase_  :				Optional[Any]      =      output + (hidden_states,)
							return output

				return BackboneOutput(feature_maps=lowerCAmelCase_			,			hidden_states=lowerCAmelCase_			,			attentions=lowerCAmelCase_		)











"""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        snake_case   (     A__		):
			UpperCAmelCase_  :				Tuple      =      []
			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        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				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        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      []
			token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")		)
			return token

def        snake_case   (     ):
			UpperCAmelCase_  :				str      =      []
			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        snake_case   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				int      =      "imagenet-1k-id2label.json"
			UpperCAmelCase_  :				Union[str, Any]      =      10_00

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

			UpperCAmelCase_  :				Optional[int]      =      idalabel
			UpperCAmelCase_  :				Optional[Any]      =      {v: k for k, v in idalabel.items()}

			UpperCAmelCase_  :				str      =      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":
						UpperCAmelCase_  :				Optional[int]      =      [1, 2, 10]

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

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

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

			UpperCAmelCase_  :				int      =      OrderedDict()
			UpperCAmelCase_  :				Optional[Any]      =      []

			for idx in range(len(config.depth		)		):
						if config.cls_token[idx]:
									UpperCAmelCase_  :				Optional[Any]      =      list_of_state_dict + cls_token(A__		)
						UpperCAmelCase_  :				Any      =      list_of_state_dict + embeddings(A__		)
						for cnt in range(config.depth[idx]		):
									UpperCAmelCase_  :				Dict      =      list_of_state_dict + attention(A__			,A__		)

			UpperCAmelCase_  :				Union[str, Any]      =      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(A__		)
			for i in range(len(A__		)		):
						UpperCAmelCase_  :				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__":
			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=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_       			=   parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



