

'''simple docstring'''
import torch
from torch import nn
class 					_snake_case		(nn.Module):






			def __init__(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case=1			,_snake_case=False     ):
				super().__init__()

				UpperCAmelCase_     :      List[Any]						=  n_token
				UpperCAmelCase_     :      int						=  d_embed
				UpperCAmelCase_     :      Optional[Any]						=  d_proj

				UpperCAmelCase_     :      Tuple						=  cutoffs + [n_token]
				UpperCAmelCase_     :      List[str]						=  [0] + self.cutoffs
				UpperCAmelCase_     :      str						=  div_val

				UpperCAmelCase_     :      List[str]						=  self.cutoffs[0]
				UpperCAmelCase_     :      Optional[int]						=  len(self.cutoffs     ) - 1
				UpperCAmelCase_     :      Tuple						=  self.shortlist_size + self.n_clusters

				if self.n_clusters > 0:
					UpperCAmelCase_     :      Optional[int]						=  nn.Parameter(torch.zeros(self.n_clusters			,self.d_embed     )     )
					UpperCAmelCase_     :      Union[str, Any]						=  nn.Parameter(torch.zeros(self.n_clusters     )     )

				UpperCAmelCase_     :      str						=  nn.ModuleList()
				UpperCAmelCase_     :      List[Any]						=  nn.ParameterList()

				if div_val == 1:
					for i in range(len(self.cutoffs     )     ):
						if d_proj != d_embed:
							self.out_projs.append(nn.Parameter(torch.FloatTensor(_snake_case			,_snake_case     )     )     )
						else:
							self.out_projs.append(_snake_case     )

					self.out_layers.append(nn.Linear(_snake_case			,_snake_case     )     )
				else:
					for i in range(len(self.cutoffs     )     ):
						UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
						UpperCAmelCase_     :      Union[str, Any]						=  d_embed // (div_val**i)

						self.out_projs.append(nn.Parameter(torch.FloatTensor(_snake_case			,_snake_case     )     )     )

						self.out_layers.append(nn.Linear(_snake_case			,r_idx - l_idx     )     )

				UpperCAmelCase_     :      Tuple						=  keep_order






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case     ):
				if proj is None:
					UpperCAmelCase_     :      int						=  nn.functional.linear(_snake_case			,_snake_case			,bias=_snake_case     )
				else:
					# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
					UpperCAmelCase_     :      Tuple						=  nn.functional.linear(_snake_case			,proj.t().contiguous()     )
					UpperCAmelCase_     :      Dict						=  nn.functional.linear(_snake_case			,_snake_case			,bias=_snake_case     )
					# else:
					#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
					#     if bias is not None:
					#         logit = logit + bias

				return logit






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None			,_snake_case=False     ):

				if labels is not None:
					# Shift so that tokens < n predict n
					UpperCAmelCase_     :      Optional[Any]						=  hidden[..., :-1, :].contiguous()
					UpperCAmelCase_     :      Any						=  labels[..., 1:].contiguous()
					UpperCAmelCase_     :      Dict						=  hidden.view(-1			,hidden.size(-1     )     )
					UpperCAmelCase_     :      Union[str, Any]						=  labels.view(-1     )
					if hidden.size(0     ) != labels.size(0     ):
						raise RuntimeError("Input and labels should have the same size in the batch dimension."     )
				else:
					UpperCAmelCase_     :      Optional[Any]						=  hidden.view(-1			,hidden.size(-1     )     )

				if self.n_clusters == 0:
					UpperCAmelCase_     :      Any						=  self._compute_logit(_snake_case			,self.out_layers[0].weight			,self.out_layers[0].bias			,self.out_projs[0]     )
					if labels is not None:
						UpperCAmelCase_     :      Any						=  labels != -1_00
						UpperCAmelCase_     :      Any						=  torch.zeros_like(_snake_case			,dtype=hidden.dtype			,device=hidden.device     )
						UpperCAmelCase_     :      Union[str, Any]						=  (
						    -nn.functional.log_softmax(_snake_case			,dim=-1     )[mask].gather(1			,labels[mask].unsqueeze(1     )     ).squeeze(1     )
						)
					else:
						UpperCAmelCase_     :      Optional[int]						=  nn.functional.log_softmax(_snake_case			,dim=-1     )
				else:
					# construct weights and biases
					UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  [], []
					for i in range(len(self.cutoffs     )     ):
						if self.div_val == 1:
							UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
							UpperCAmelCase_     :      Any						=  self.out_layers[0].weight[l_idx:r_idx]
							UpperCAmelCase_     :      List[str]						=  self.out_layers[0].bias[l_idx:r_idx]
						else:
							UpperCAmelCase_     :      Optional[Any]						=  self.out_layers[i].weight
							UpperCAmelCase_     :      str						=  self.out_layers[i].bias

						if i == 0:
							UpperCAmelCase_     :      Union[str, Any]						=  torch.cat([weight_i, self.cluster_weight]			,dim=0     )
							UpperCAmelCase_     :      Union[str, Any]						=  torch.cat([bias_i, self.cluster_bias]			,dim=0     )

						weights.append(_snake_case     )
						biases.append(_snake_case     )

					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  weights[0], biases[0], self.out_projs[0]

					UpperCAmelCase_     :      Optional[Any]						=  self._compute_logit(_snake_case			,_snake_case			,_snake_case			,_snake_case     )
					UpperCAmelCase_     :      Dict						=  nn.functional.log_softmax(_snake_case			,dim=1     )

					if labels is None:
						UpperCAmelCase_     :      int						=  hidden.new_empty((head_logit.size(0     ), self.n_token)     )
					else:
						UpperCAmelCase_     :      Any						=  torch.zeros_like(_snake_case			,dtype=hidden.dtype			,device=hidden.device     )

					UpperCAmelCase_     :      int						=  0
					UpperCAmelCase_     :      Optional[Any]						=  [0] + self.cutoffs
					for i in range(len(_snake_case     ) - 1     ):
						UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  cutoff_values[i], cutoff_values[i + 1]

						if labels is not None:
							UpperCAmelCase_     :      int						=  (labels >= l_idx) & (labels < r_idx)
							UpperCAmelCase_     :      Any						=  mask_i.nonzero().squeeze()

							if indices_i.numel() == 0:
								continue

							UpperCAmelCase_     :      Dict						=  labels.index_select(0			,_snake_case     ) - l_idx
							UpperCAmelCase_     :      List[Any]						=  head_logprob.index_select(0			,_snake_case     )
							UpperCAmelCase_     :      Optional[int]						=  hidden.index_select(0			,_snake_case     )
						else:
							UpperCAmelCase_     :      Optional[Any]						=  hidden

						if i == 0:
							if labels is not None:
								UpperCAmelCase_     :      List[str]						=  head_logprob_i.gather(1			,target_i[:, None]     ).squeeze(1     )
							else:
								UpperCAmelCase_     :      Optional[int]						=  head_logprob[:, : self.cutoffs[0]]
						else:
							UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Any						=  weights[i], biases[i], self.out_projs[i]

							UpperCAmelCase_     :      Union[str, Any]						=  self._compute_logit(_snake_case			,_snake_case			,_snake_case			,_snake_case     )
							UpperCAmelCase_     :      int						=  nn.functional.log_softmax(_snake_case			,dim=1     )
							UpperCAmelCase_     :      int						=  self.cutoffs[0] + i - 1  # No probability for the head cluster
							if labels is not None:
								UpperCAmelCase_     :      str						=  head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
								    1			,target_i[:, None]     ).squeeze(1     )
							else:
								UpperCAmelCase_     :      Union[str, Any]						=  head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
								UpperCAmelCase_     :      Optional[int]						=  logprob_i

						if labels is not None:
							if (hasattr(self			,"keep_order"     ) and self.keep_order) or keep_order:
								out.index_copy_(0			,_snake_case			,-logprob_i     )
							else:
								out[offset : offset + logprob_i.size(0     )].copy_(-logprob_i     )
							offset += logprob_i.size(0     )

				return out






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				if self.n_clusters == 0:
					UpperCAmelCase_     :      Dict						=  self._compute_logit(_snake_case			,self.out_layers[0].weight			,self.out_layers[0].bias			,self.out_projs[0]     )
					return nn.functional.log_softmax(_snake_case			,dim=-1     )
				else:
					# construct weights and biases
					UpperCAmelCase_					,					UpperCAmelCase_     :      Union[str, Any]						=  [], []
					for i in range(len(self.cutoffs     )     ):
						if self.div_val == 1:
							UpperCAmelCase_					,					UpperCAmelCase_     :      Any						=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
							UpperCAmelCase_     :      Tuple						=  self.out_layers[0].weight[l_idx:r_idx]
							UpperCAmelCase_     :      Any						=  self.out_layers[0].bias[l_idx:r_idx]
						else:
							UpperCAmelCase_     :      str						=  self.out_layers[i].weight
							UpperCAmelCase_     :      str						=  self.out_layers[i].bias

						if i == 0:
							UpperCAmelCase_     :      List[Any]						=  torch.cat([weight_i, self.cluster_weight]			,dim=0     )
							UpperCAmelCase_     :      List[str]						=  torch.cat([bias_i, self.cluster_bias]			,dim=0     )

						weights.append(_snake_case     )
						biases.append(_snake_case     )

					UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  weights[0], biases[0], self.out_projs[0]
					UpperCAmelCase_     :      List[Any]						=  self._compute_logit(_snake_case			,_snake_case			,_snake_case			,_snake_case     )

					UpperCAmelCase_     :      str						=  hidden.new_empty((head_logit.size(0     ), self.n_token)     )
					UpperCAmelCase_     :      List[Any]						=  nn.functional.log_softmax(_snake_case			,dim=1     )

					UpperCAmelCase_     :      Optional[int]						=  [0] + self.cutoffs
					for i in range(len(_snake_case     ) - 1     ):
						UpperCAmelCase_					,					UpperCAmelCase_     :      int						=  cutoff_values[i], cutoff_values[i + 1]

						if i == 0:
							UpperCAmelCase_     :      Optional[Any]						=  head_logprob[:, : self.cutoffs[0]]
						else:
							UpperCAmelCase_					,					UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  weights[i], biases[i], self.out_projs[i]

							UpperCAmelCase_     :      int						=  self._compute_logit(_snake_case			,_snake_case			,_snake_case			,_snake_case     )
							UpperCAmelCase_     :      int						=  nn.functional.log_softmax(_snake_case			,dim=1     )

							UpperCAmelCase_     :      Tuple						=  head_logprob[:, -i] + tail_logprob_i
							UpperCAmelCase_     :      Union[str, Any]						=  logprob_i

					return out











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							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_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 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')	)
    return token


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__											=			1_0_0_0

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

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

    SCREAMING_SNAKE_CASE__											=			SCREAMING_SNAKE_CASE__											=			CvtConfig(num_labels=lowerCAmelCase_ ,							idalabel=lowerCAmelCase_ ,							labelaid=lowerCAmelCase_	)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "13":
        SCREAMING_SNAKE_CASE__											=			[1, 2, 1_0]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "21":
        SCREAMING_SNAKE_CASE__											=			[1, 4, 1_6]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        SCREAMING_SNAKE_CASE__											=			[2, 2, 2_0]
        SCREAMING_SNAKE_CASE__											=			[3, 1_2, 1_6]
        SCREAMING_SNAKE_CASE__											=			[1_9_2, 7_6_8, 1_0_2_4]

    SCREAMING_SNAKE_CASE__											=			CvtForImageClassification(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
    SCREAMING_SNAKE_CASE__											=			image_size
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location=torch.device('''cpu'''	)	)

    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    SCREAMING_SNAKE_CASE__											=			[]

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

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

    model.load_state_dict(lowerCAmelCase_	)
    model.save_pretrained(lowerCAmelCase_	)
    image_processor.save_pretrained(lowerCAmelCase_	)


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

if __name__ == "__main__":
  _A					:				Union[str, Any]				=			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_84,
      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					:				int				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
