
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    (				UpperCAmelCase__					:   Optional[int]							)	->     List[Any]:
							lowerCamelCase_					=       []
							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    (				UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   Tuple							)	->     str:
							lowerCamelCase_					=       []
							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    (				UpperCAmelCase__					:   Optional[Any]							)	->     List[str]:
							lowerCamelCase_					=       []
							token.append((F'''cvt.encoder.stages.{idx}.cls_token''', """stage2.cls_token""")							)
							return token



def   __lowerCAmelCase    (				)	->     Union[str, Any]:
							lowerCamelCase_					=       []
							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    (				UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   str							)	->     Tuple:
							lowerCamelCase_					=       """imagenet-1k-id2label.json"""
							lowerCamelCase_					=       1_0_0_0

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

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

							lowerCamelCase_					=       lowerCamelCase_					=       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":
														lowerCamelCase_					=       [1, 2, 1_0]

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

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

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

							lowerCamelCase_					=       OrderedDict()
							lowerCamelCase_					=       []

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

							lowerCamelCase_					=       list_of_state_dict + final()
							for gg in list_of_state_dict:
														print(UpperCAmelCase__							)
							for i in range(len(UpperCAmelCase__							)							):
														lowerCamelCase_					=       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__":
		lowercase   =	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.'''
		)

		lowercase   =	parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowercase   =	{}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase   =	['''MLukeTokenizer''']

if TYPE_CHECKING:
		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mluke import MLukeTokenizer


else:
		import sys

		lowercase   =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





