



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,
)


__snake_case		:Dict   	=  pytest.mark.integration





@pytest.mark.parametrize('''path'''     ,				['''paws''', '''csv''']       )
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    inspect_dataset(_UpperCAmelCase     ,				_UpperCAmelCase       )
    __a		 =       path + '''.py'''
    assert script_name in os.listdir(_UpperCAmelCase       )
    assert "__pycache__" not in os.listdir(_UpperCAmelCase       )





@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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    inspect_metric(_UpperCAmelCase     ,				_UpperCAmelCase       )
    __a		 =       path + '''.py'''
    assert script_name in os.listdir(_UpperCAmelCase       )
    assert "__pycache__" not in os.listdir(_UpperCAmelCase       )





@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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       get_dataset_config_info(_UpperCAmelCase     ,				config_name=_UpperCAmelCase       )
    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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    with pytest.raises(_UpperCAmelCase       ):
        get_dataset_config_info(_UpperCAmelCase     ,				config_name=_UpperCAmelCase       )





@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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       get_dataset_config_names(_UpperCAmelCase       )
    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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       get_dataset_infos(_UpperCAmelCase       )
    assert list(infos.keys()       ) == expected_configs
    __a		 =       expected_configs[0]
    assert expected_config in infos
    __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       get_dataset_infos(_UpperCAmelCase       )
    assert expected_config in infos
    __a		 =       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   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    with pytest.raises(_UpperCAmelCase       ):
        get_dataset_split_names(_UpperCAmelCase     ,				config_name=_UpperCAmelCase       )










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			(    _UpperCAmelCase       ):
    __a		 =       []
    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			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       []
    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			(    _UpperCAmelCase       ):
    __a		 =       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', '''stage2.cls_token''')       )
    return token





def   __snake_case			(    ):
    __a		 =       []
    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			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       '''imagenet-1k-id2label.json'''
    __a		 =       1000

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

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

    __a		 =       __a		 =       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":
        __a		 =       [1, 2, 10]

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        __a		 =       [2, 2, 20]
        __a		 =       [3, 12, 16]
        __a		 =       [192, 768, 1024]

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

    __a		 =       OrderedDict()
    __a		 =       []

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

    __a		 =       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase       )
    for i in range(len(_UpperCAmelCase       )       ):
        __a		 =       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__":
 __snake_case		: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.'''
 )

 __snake_case		:Dict   	=  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





