






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


UpperCamelCase								=		{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

UpperCamelCase								=		{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

UpperCamelCase								=		{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

UpperCamelCase								=		{
    "num_train_timesteps": 40,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCamelCase								=		{
    "num_train_timesteps": 201,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}

UpperCamelCase								=		{
    "num_train_timesteps": 151,
    "sigma_min": 0.0_02,
    "sigma_max": 80.0,
}






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    Dict:
							if isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError('boolean value expected'				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=False				)     ->    Tuple:
							_lowercase							:   List[str] 							=   checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
							_lowercase							:   int 							=   checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
							_lowercase							:   Optional[int] 							=   checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
							_lowercase							:   Optional[Any] 							=   checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
							_lowercase							:   Optional[Any] 							=   checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
							_lowercase							:   Optional[int] 							=   checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
							_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
							_lowercase							:   List[str] 							=   checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
							_lowercase							:   Optional[int] 							=   checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
							_lowercase							:   Optional[Any] 							=   checkpoint[F"""{old_prefix}.out_layers.3.bias"""]

							if has_skip:
														_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.skip_connection.weight"""]
														_lowercase							:   int 							=   checkpoint[F"""{old_prefix}.skip_connection.bias"""]

							return new_checkpoint






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE=None				)     ->    List[str]:
							_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3     ,     dim=0				)
							_lowercase							,							_lowercase							,							_lowercase							:   Union[str, Any] 							=   checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3     ,     dim=0				)

							_lowercase							:   str 							=   checkpoint[F"""{old_prefix}.norm.weight"""]
							_lowercase							:   Tuple 							=   checkpoint[F"""{old_prefix}.norm.bias"""]

							_lowercase							:   int 							=   weight_q.squeeze(-1				).squeeze(-1				)
							_lowercase							:   List[Any] 							=   bias_q.squeeze(-1				).squeeze(-1				)
							_lowercase							:   int 							=   weight_k.squeeze(-1				).squeeze(-1				)
							_lowercase							:   Any 							=   bias_k.squeeze(-1				).squeeze(-1				)
							_lowercase							:   List[str] 							=   weight_v.squeeze(-1				).squeeze(-1				)
							_lowercase							:   List[str] 							=   bias_v.squeeze(-1				).squeeze(-1				)

							_lowercase							:   Tuple 							=   (
							    checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1				).squeeze(-1				)
							)
							_lowercase							:   Dict 							=   checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1				).squeeze(-1				)

							return new_checkpoint






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    str:
							_lowercase							:   str 							=   torch.load(SCREAMING_SNAKE_CASE     ,     map_location='cpu'				)
							_lowercase							:   str 							=   {}

							_lowercase							:   str 							=   checkpoint['time_embed.0.weight']
							_lowercase							:   Tuple 							=   checkpoint['time_embed.0.bias']
							_lowercase							:   Optional[Any] 							=   checkpoint['time_embed.2.weight']
							_lowercase							:   int 							=   checkpoint['time_embed.2.bias']

							if unet_config["num_class_embeds"] is not None:
														_lowercase							:   Dict 							=   checkpoint['label_emb.weight']

							_lowercase							:   Tuple 							=   checkpoint['input_blocks.0.0.weight']
							_lowercase							:   Optional[Any] 							=   checkpoint['input_blocks.0.0.bias']

							_lowercase							:   Union[str, Any] 							=   unet_config['down_block_types']
							_lowercase							:   Tuple 							=   unet_config['layers_per_block']
							_lowercase							:   Any 							=   unet_config['attention_head_dim']
							_lowercase							:   List[Any] 							=   unet_config['block_out_channels']
							_lowercase							:   Optional[Any] 							=   1
							_lowercase							:   List[str] 							=   channels_list[0]

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE				):
														_lowercase							:   Tuple 							=   channels_list[i]
														_lowercase							:   Tuple 							=   current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE				):
																												_lowercase							:   str 							=   F"""down_blocks.{i}.resnets.{j}"""
																												_lowercase							:   Dict 							=   F"""input_blocks.{current_layer}.0"""
																												_lowercase							:   int 							=   True if j == 0 and downsample_block_has_skip else False
																												_lowercase							:   Optional[int] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE				):
																												_lowercase							:   Tuple 							=   F"""down_blocks.{i}.resnets.{j}"""
																												_lowercase							:   Union[str, Any] 							=   F"""input_blocks.{current_layer}.0"""
																												_lowercase							:   List[Any] 							=   True if j == 0 and downsample_block_has_skip else False
																												_lowercase							:   Union[str, Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												_lowercase							:   Dict 							=   F"""down_blocks.{i}.attentions.{j}"""
																												_lowercase							:   int 							=   F"""input_blocks.{current_layer}.1"""
																												_lowercase							:   Any 							=   convert_attention(
																												    SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												current_layer += 1

														if i != len(SCREAMING_SNAKE_CASE				) - 1:
																					_lowercase							:   int 							=   F"""down_blocks.{i}.downsamplers.0"""
																					_lowercase							:   List[Any] 							=   F"""input_blocks.{current_layer}.0"""
																					_lowercase							:   Tuple 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																					current_layer += 1

														_lowercase							:   List[Any] 							=   current_channels

							# hardcoded the mid-block for now
							_lowercase							:   List[Any] 							=   'mid_block.resnets.0'
							_lowercase							:   Union[str, Any] 							=   'middle_block.0'
							_lowercase							:   Optional[int] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							_lowercase							:   Tuple 							=   'mid_block.attentions.0'
							_lowercase							:   Any 							=   'middle_block.1'
							_lowercase							:   str 							=   convert_attention(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							_lowercase							:   Optional[Any] 							=   'mid_block.resnets.1'
							_lowercase							:   Optional[Any] 							=   'middle_block.2'
							_lowercase							:   List[Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							_lowercase							:   Optional[int] 							=   0
							_lowercase							:   List[str] 							=   unet_config['up_block_types']

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												_lowercase							:   Tuple 							=   F"""up_blocks.{i}.resnets.{j}"""
																												_lowercase							:   int 							=   F"""output_blocks.{current_layer}.0"""
																												_lowercase							:   str 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE				) - 1:
																												_lowercase							:   Dict 							=   F"""up_blocks.{i}.upsamplers.0"""
																												_lowercase							:   Optional[Any] 							=   F"""output_blocks.{current_layer-1}.1"""
																												_lowercase							:   Dict 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												_lowercase							:   Tuple 							=   F"""up_blocks.{i}.resnets.{j}"""
																												_lowercase							:   List[str] 							=   F"""output_blocks.{current_layer}.0"""
																												_lowercase							:   Optional[Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     has_skip=SCREAMING_SNAKE_CASE				)
																												_lowercase							:   str 							=   F"""up_blocks.{i}.attentions.{j}"""
																												_lowercase							:   Dict 							=   F"""output_blocks.{current_layer}.1"""
																												_lowercase							:   Optional[Any] 							=   convert_attention(
																												    SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE				) - 1:
																												_lowercase							:   int 							=   F"""up_blocks.{i}.upsamplers.0"""
																												_lowercase							:   str 							=   F"""output_blocks.{current_layer-1}.2"""
																												_lowercase							:   List[Any] 							=   convert_resnet(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)

							_lowercase							:   List[Any] 							=   checkpoint['out.0.weight']
							_lowercase							:   Optional[Any] 							=   checkpoint['out.0.bias']
							_lowercase							:   Dict 							=   checkpoint['out.2.weight']
							_lowercase							:   Dict 							=   checkpoint['out.2.bias']

							return new_checkpoint


if __name__ == "__main__":
							UpperCamelCase								=		argparse.ArgumentParser()

							parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
							parser.add_argument(
							    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
							)
							parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

							UpperCamelCase								=		parser.parse_args()
							UpperCamelCase								=		strabool(args.class_cond)

							UpperCamelCase								=		os.path.basename(args.unet_path)
							print(f'''Checkpoint: {ckpt_name}''')

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														UpperCamelCase								=		IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														UpperCamelCase								=		LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														UpperCamelCase								=		TEST_UNET_CONFIG
							else:
														raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

							if not args.class_cond:
														UpperCamelCase								=		None

							UpperCamelCase								=		con_pt_to_diffuser(args.unet_path, unet_config)

							UpperCamelCase								=		UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														UpperCamelCase								=		CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														UpperCamelCase								=		CT_IMAGENET_64_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														UpperCamelCase								=		CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f'''Checkpoint type {ckpt_name} is not currently supported.''')

							UpperCamelCase								=		CMStochasticIterativeScheduler(**scheduler_config)

							UpperCamelCase								=		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)











import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCAmelCase							:       Dict					 =	"""\
    Text data.
    Second line of data."""

lowerCAmelCase							:       str					 =	"""file"""






@pytest.fixture(scope='''session'''					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   tmp_path_factory.mktemp('''data'''					) / (FILE_PATH + '''.zstd''')
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   bytes(UpperCamelCase__       ,			'''utf-8'''					)
						with zstd.open(UpperCamelCase__       ,			'''wb'''					) as f:
												f.write(UpperCamelCase__					)
						return path






@pytest.fixture
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    int:

						"""simple docstring"""





						with open(os.path.join(tmpfs.local_root_dir       ,			UpperCamelCase__					)       ,			'''w'''					) as f:
												f.write(UpperCamelCase__					)
						return FILE_PATH






@pytest.mark.parametrize('''compression_format'''       ,			['''gzip''', '''xz''', '''zstd''']					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   List[Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Dict           =   {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
						__SCREAMING_SNAKE_CASE:				List[str]           =   input_paths[compression_format]
						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path / '''cache'''
						__SCREAMING_SNAKE_CASE:				List[str]           =   DownloadConfig(cache_dir=UpperCamelCase__       ,			extract_compressed_file=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   cached_path(UpperCamelCase__       ,			download_config=UpperCamelCase__					)
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   f.read()
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Dict           =   f.read()
						assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''       ,			[True, False]					)
@pytest.mark.parametrize('''default_cache_dir'''       ,			[True, False]					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   str       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Optional[Any]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   '''custom_cache'''
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''custom_extracted_dir'''
						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path / '''custom_extracted_path'''
						if default_extracted:
												__SCREAMING_SNAKE_CASE:				int           =   ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
						else:
												monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''       ,			UpperCamelCase__					)
												monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''       ,			str(UpperCamelCase__					)					)
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

						__SCREAMING_SNAKE_CASE:				Dict           =   xz_file
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   (
						    DownloadConfig(extract_compressed_file=UpperCamelCase__					)
						    if default_cache_dir
						    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir       ,			extract_compressed_file=UpperCamelCase__					)
						)
						__SCREAMING_SNAKE_CASE:				int           =   cached_path(UpperCamelCase__       ,			download_config=UpperCamelCase__					)
						assert Path(UpperCamelCase__					).parent.parts[-2:] == expected






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   str(Path(UpperCamelCase__					).resolve()					)
						assert cached_path(UpperCamelCase__					) == text_file
						# relative path
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   str(Path(UpperCamelCase__					).resolve().relative_to(Path(os.getcwd()					)					)					)
						assert cached_path(UpperCamelCase__					) == text_file






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   str(tmp_path.resolve() / '''__missing_file__.txt'''					)
						with pytest.raises(UpperCamelCase__					):
												cached_path(UpperCamelCase__					)
						# relative path
						__SCREAMING_SNAKE_CASE:				int           =   '''./__missing_file__.txt'''
						with pytest.raises(UpperCamelCase__					):
												cached_path(UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   get_from_cache(F"""tmp://{tmpfs_file}"""					)
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Tuple           =   f.read()
						assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	)      ->    Any:

						"""simple docstring"""





						with pytest.raises(UpperCamelCase__					):
												cached_path('''https://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												http_get('''https://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												http_head('''https://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    List[str]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												ftp_get('''ftp://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												ftp_head('''ftp://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												fsspec_get('''s3://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												fsspec_head('''s3://huggingface.co'''					)



