


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			)    ->						Optional[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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						int:


						'''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 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		[]
						token.append((F'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token")			)
						return token





def 						_lowerCAmelCase							(     )    ->						Dict:


						'''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 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Union[str, Any]:


						'''simple docstring'''






						__snake_case				=		"imagenet-1k-id2label.json"
						__snake_case				=		1000

						__snake_case				=		"huggingface/label-files"
						__snake_case				=		num_labels
						__snake_case				=		json.load(open(cached_download(hf_hub_url(_lowerCAmelCase   ,						_lowerCAmelCase   ,						repo_type="dataset"			)			)   ,						"r"			)			)
						__snake_case				=		{int(_lowerCAmelCase			): 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=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						# 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				=		[192, 768, 1024]

						__snake_case				=		CvtForImageClassification(_lowerCAmelCase			)
						__snake_case				=		AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
						__snake_case				=		image_size
						__snake_case				=		torch.load(_lowerCAmelCase   ,						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(_lowerCAmelCase			)
												__snake_case				=		list_of_state_dict + embeddings(_lowerCAmelCase			)
												for cnt in range(config.depth[idx]			):
																		__snake_case				=		list_of_state_dict + attention(_lowerCAmelCase   ,						_lowerCAmelCase			)

						__snake_case				=		list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(_lowerCAmelCase			)
						for i in range(len(_lowerCAmelCase			)			):
												__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				:       str      =  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.'
	)

	A				:       List[str]      =  parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)









import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


A				:       List[str]      =  pytest.mark.integration





@pytest.mark.parametrize("path"   ,						["paws", "csv"]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						inspect_dataset(_lowerCAmelCase   ,						_lowerCAmelCase			)
						__snake_case				=		path + ".py"
						assert script_name in os.listdir(_lowerCAmelCase			)
						assert "__pycache__" not in os.listdir(_lowerCAmelCase			)





@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"   ,						["accuracy"]			)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						inspect_metric(_lowerCAmelCase   ,						_lowerCAmelCase			)
						__snake_case				=		path + ".py"
						assert script_name in os.listdir(_lowerCAmelCase			)
						assert "__pycache__" not in os.listdir(_lowerCAmelCase			)





@pytest.mark.parametrize(
    "path, config_name, expected_splits"   ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		get_dataset_config_info(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)
						assert info.config_name == config_name
						assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"   ,						[
        ("paws", None, ValueError),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						with pytest.raises(_lowerCAmelCase			):
												get_dataset_config_info(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)





@pytest.mark.parametrize(
    "path, expected"   ,						[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		get_dataset_config_names(_lowerCAmelCase			)
						assert expected in config_names





@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"   ,						[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		get_dataset_infos(_lowerCAmelCase			)
						assert list(infos.keys()			) == expected_configs
						__snake_case				=		expected_configs[0]
						assert expected_config in infos
						__snake_case				=		infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()			) == expected_splits_in_first_config





@pytest.mark.parametrize(
    "path, expected_config, expected_splits"   ,						[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						__snake_case				=		get_dataset_infos(_lowerCAmelCase			)
						assert expected_config in infos
						__snake_case				=		infos[expected_config]
						assert info.config_name == expected_config
						assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"   ,						[
        ("paws", None, ValueError),
    ]   ,						)
def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)    ->						str:


						'''simple docstring'''






						with pytest.raises(_lowerCAmelCase			):
												get_dataset_split_names(_lowerCAmelCase   ,						config_name=_lowerCAmelCase			)





