





import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Dict:
    assert "mock" in _fsspec_registry
    assert "bz2" in _fsspec_registry


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    assert "mock" not in _fsspec_registry
    assert "bz2" in _fsspec_registry


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      str					 =		'mock-s3-bucket'
    lowerCamelCase__    :      Any					 =		F"""s3://{mock_bucket}"""
    lowerCamelCase__    :      int					 =		extract_path_from_uri(_UpperCAmelCase )
    assert dataset_path.startswith('s3://' ) is False

    lowerCamelCase__    :      List[Any]					 =		'./local/path'
    lowerCamelCase__    :      Tuple					 =		extract_path_from_uri(_UpperCAmelCase )
    assert dataset_path == new_dataset_path


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Optional[Any]					 =		is_remote_filesystem(_UpperCAmelCase )
    assert is_remote is True

    lowerCamelCase__    :      List[str]					 =		fsspec.filesystem('file' )

    lowerCamelCase__    :      List[str]					 =		is_remote_filesystem(_UpperCAmelCase )
    assert is_remote is False


@pytest.mark.parametrize('compression_fs_class'						,	_UpperCAmelCase )
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Any					 =		{'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
    lowerCamelCase__    :      str					 =		input_paths[compression_fs_class.protocol]
    if input_path is None:
        lowerCamelCase__    :      List[str]					 =		F"""for '{compression_fs_class.protocol}' compression protocol, """
        if compression_fs_class.protocol == "lz4":
            reason += require_lza.kwargs["reason"]
        elif compression_fs_class.protocol == "zstd":
            reason += require_zstandard.kwargs["reason"]
        pytest.skip(_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		fsspec.filesystem(compression_fs_class.protocol						,	fo=_UpperCAmelCase )
    assert isinstance(_UpperCAmelCase						,	_UpperCAmelCase )
    lowerCamelCase__    :      Optional[int]					 =		os.path.basename(_UpperCAmelCase )
    lowerCamelCase__    :      List[Any]					 =		expected_filename[: expected_filename.rindex('.' )]
    assert fs.glob('*' ) == [expected_filename]
    with fs.open(_UpperCAmelCase						,	'r'						,	encoding='utf-8' ) as f, open(_UpperCAmelCase						,	encoding='utf-8' ) as expected_file:
        assert f.read() == expected_file.read()


@pytest.mark.parametrize('protocol'						,	['zip', 'gzip'] )
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     List[str]:
    lowerCamelCase__    :      Optional[Any]					 =		{'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
    lowerCamelCase__    :      List[str]					 =		compressed_file_paths[protocol]
    lowerCamelCase__    :      List[str]					 =		'dataset.jsonl'
    lowerCamelCase__    :      Optional[Any]					 =		F"""{protocol}://{member_file_path}::{compressed_file_path}"""
    lowerCamelCase__ ,							*lowerCamelCase__    :      Optional[int]					 =		fsspec.get_fs_token_paths(_UpperCAmelCase )
    assert fs.isfile(_UpperCAmelCase )
    assert not fs.isfile('non_existing_' + member_file_path )


@pytest.mark.integration
def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Optional[int]					 =		hf_api.dataset_info(_UpperCAmelCase						,	token=_UpperCAmelCase )
    lowerCamelCase__    :      Any					 =		HfFileSystem(repo_info=_UpperCAmelCase						,	token=_UpperCAmelCase )
    assert sorted(hffs.glob('*' ) ) == [".gitattributes", "data"]
    assert hffs.isdir('data' )
    assert hffs.isfile('.gitattributes' ) and hffs.isfile('data/text_data.txt' )
    with open(_UpperCAmelCase ) as f:
        assert hffs.open('data/text_data.txt'						,	'r' ).read() == f.read()


def        SCREAMING_SNAKE_CASE      (       )			->     List[Any]:
    lowerCamelCase__    :      List[Any]					 =		'bz2'

    # Import module
    import datasets.filesystems

    # Overwrite protocol and reload
    register_implementation(_UpperCAmelCase						,	_UpperCAmelCase						,	clobber=_UpperCAmelCase )
    with pytest.warns(_UpperCAmelCase ) as warning_info:
        importlib.reload(datasets.filesystems )

    assert len(_UpperCAmelCase ) == 1
    assert (
        str(warning_info[0].message )
        == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
    )












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


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    lowerCamelCase__    :      Tuple					 =		'imagenet-1k-id2label.json'
    lowerCamelCase__    :      Union[str, Any]					 =		1000

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

    lowerCamelCase__    :      Tuple					 =		idalabel
    lowerCamelCase__    :      List[Any]					 =		{v: k for k, v in idalabel.items()}

    lowerCamelCase__    :      List[str]					 =		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__    :      List[Any]					 =		[1, 2, 10]

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

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

    lowerCamelCase__    :      Any					 =		CvtForImageClassification(_UpperCAmelCase )
    lowerCamelCase__    :      Optional[Any]					 =		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
    lowerCamelCase__    :      Tuple					 =		image_size
    lowerCamelCase__    :      List[str]					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )

    lowerCamelCase__    :      Optional[int]					 =		OrderedDict()
    lowerCamelCase__    :      Tuple					 =		[]

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

    lowerCamelCase__    :      int					 =		list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase )
    for i in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      str					 =		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__":
    _UpperCAmelCase				:       List[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=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."""
    )

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





