

'''simple docstring'''


import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard







@pytest.mark.parametrize(
    'compression_format, is_archive'   ,[
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]   ,)
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,)    ->    Union[str, Any]:
	UpperCAmelCase_		:      List[Any]       	=					{
	    '7z': (seven_zip_file, SevenZipExtractor),
	    'bz2': (bza_file, BzipaExtractor),
	    'gzip': (gz_file, GzipExtractor),
	    'lz4': (lza_file, LzaExtractor),
	    'tar': (tar_file, TarExtractor),
	    'xz': (xz_file, XzExtractor),
	    'zip': (zip_file, ZipExtractor),
	    'zstd': (zstd_file, ZstdExtractor),
	}
	UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					input_paths_and_base_extractors[compression_format]
	if input_path is None:
		UpperCAmelCase_		:      List[Any]       	=					f"""for '{compression_format}' compression_format, """
		if compression_format == "7z":
			reason += require_pyazr.kwargs["reason"]
		elif compression_format == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_format == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(UpperCamelCase  )
	assert base_extractor.is_extractable(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					tmp_path / ('extracted' if is_archive else 'extracted.txt')
	base_extractor.extract(UpperCamelCase   ,UpperCamelCase  )
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			UpperCAmelCase_		:      Optional[Any]       	=					file_path.read_text(encoding='utf-8'  )
	else:
		UpperCAmelCase_		:      Optional[Any]       	=					output_path.read_text(encoding='utf-8'  )
	UpperCAmelCase_		:      Dict       	=					text_file.read_text(encoding='utf-8'  )
	assert extracted_file_content == expected_file_content







@pytest.mark.parametrize(
    'compression_format, is_archive'   ,[
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]   ,)
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,)    ->    Any:
	UpperCAmelCase_		:      Any       	=					{
	    '7z': seven_zip_file,
	    'bz2': bza_file,
	    'gzip': gz_file,
	    'lz4': lza_file,
	    'tar': tar_file,
	    'xz': xz_file,
	    'zip': zip_file,
	    'zstd': zstd_file,
	}
	UpperCAmelCase_		:      List[str]       	=					input_paths[compression_format]
	if input_path is None:
		UpperCAmelCase_		:      Any       	=					f"""for '{compression_format}' compression_format, """
		if compression_format == "7z":
			reason += require_pyazr.kwargs["reason"]
		elif compression_format == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_format == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(UpperCamelCase  )
	UpperCAmelCase_		:      List[Any]       	=					Extractor.infer_extractor_format(UpperCamelCase  )
	assert extractor_format is not None
	UpperCAmelCase_		:      List[Any]       	=					tmp_path / ('extracted' if is_archive else 'extracted.txt')
	Extractor.extract(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			UpperCAmelCase_		:      int       	=					file_path.read_text(encoding='utf-8'  )
	else:
		UpperCAmelCase_		:      Optional[Any]       	=					output_path.read_text(encoding='utf-8'  )
	UpperCAmelCase_		:      List[str]       	=					text_file.read_text(encoding='utf-8'  )
	assert extracted_file_content == expected_file_content







@pytest.fixture
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	import tarfile

	UpperCAmelCase_		:      int       	=					tmp_path / 'data_dot_dot'
	directory.mkdir()
	UpperCAmelCase_		:      List[str]       	=					directory / 'tar_file_with_dot_dot.tar'
	with tarfile.TarFile(UpperCamelCase   ,'w'  ) as f:
		f.add(UpperCamelCase   ,arcname=os.path.join('..'   ,text_file.name  )  )
	return path







@pytest.fixture
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	import tarfile

	UpperCAmelCase_		:      Union[str, Any]       	=					tmp_path / 'data_sym_link'
	directory.mkdir()
	UpperCAmelCase_		:      str       	=					directory / 'tar_file_with_sym_link.tar'
	os.symlink('..'   ,directory / 'subdir'   ,target_is_directory=UpperCamelCase  )
	with tarfile.TarFile(UpperCamelCase   ,'w'  ) as f:
		f.add(str(directory / 'subdir'  )   ,arcname='subdir'  )  # str required by os.readlink on Windows and Python < 3.8
	return path







@pytest.mark.parametrize(
    'insecure_tar_file, error_log'   ,[('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]   ,)
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      Optional[Any]       	=					{
	    'tar_file_with_dot_dot': tar_file_with_dot_dot,
	    'tar_file_with_sym_link': tar_file_with_sym_link,
	}
	UpperCAmelCase_		:      Optional[Any]       	=					insecure_tar_files[insecure_tar_file]
	UpperCAmelCase_		:      Optional[int]       	=					tmp_path / 'extracted'
	TarExtractor.extract(UpperCamelCase   ,UpperCamelCase  )
	assert caplog.text
	for record in caplog.records:
		assert record.levelname == "ERROR"
		assert error_log in record.msg







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	# We should have less false positives than zipfile.is_zipfile
	# We do that by checking only the magic number
	UpperCAmelCase_		:      List[str]       	=					tmpdir / 'not_a_zip_file'
	# From: https://github.com/python/cpython/pull/5053
	UpperCAmelCase_		:      Any       	=					(
	    B'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
	    B'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
	    B'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
	    B'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
	)
	with not_a_zip_file.open('wb'  ) as f:
		f.write(UpperCamelCase  )
	assert zipfile.is_zipfile(str(UpperCamelCase  )  )  # is a false positive for `zipfile`
	assert not ZipExtractor.is_extractable(UpperCamelCase  )  # but we're right









'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class 					lowercase						(  a_ ):
							_lowerCamelCase		:      torch.FloatTensor



class 					lowercase						(  a_,					a_ ):


							@register_to_config
							def __init__(			self    ,			_snake_case = 6_5536    ,			_snake_case = None    ,			_snake_case = 2    ,			_snake_case = 2    ,			_snake_case = 0    ,			_snake_case = "fourier"    ,			_snake_case = True    ,			_snake_case = False    ,			_snake_case = 0.0    ,			_snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,			_snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,			_snake_case = "UNetMidBlock1D"    ,			_snake_case = None    ,			_snake_case = (32, 32, 64)    ,			_snake_case = None    ,			_snake_case = 8    ,			_snake_case = 1    ,			_snake_case = False    ,			)							->				List[str]:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					sample_size

								# time
								if time_embedding_type == "fourier":
									UpperCAmelCase_		:      Tuple       	=					GaussianFourierProjection(
									    embedding_size=8    ,			set_W_to_weight=_snake_case    ,			log=_snake_case    ,			flip_sin_to_cos=_snake_case)
									UpperCAmelCase_		:      int       	=					2 * block_out_channels[0]
								elif time_embedding_type == "positional":
									UpperCAmelCase_		:      Optional[Any]       	=					Timesteps(
									    block_out_channels[0]    ,			flip_sin_to_cos=_snake_case    ,			downscale_freq_shift=_snake_case)
									UpperCAmelCase_		:      List[Any]       	=					block_out_channels[0]

								if use_timestep_embedding:
									UpperCAmelCase_		:      Dict       	=					block_out_channels[0] * 4
									UpperCAmelCase_		:      List[Any]       	=					TimestepEmbedding(
									    in_channels=_snake_case    ,			time_embed_dim=_snake_case    ,			act_fn=_snake_case    ,			out_dim=block_out_channels[0]    ,			)

								UpperCAmelCase_		:      int       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Optional[int]       	=					None
								UpperCAmelCase_		:      Optional[int]       	=					nn.ModuleList([])
								UpperCAmelCase_		:      Any       	=					None

								# down
								UpperCAmelCase_		:      Dict       	=					in_channels
								for i, down_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      int       	=					output_channel
									UpperCAmelCase_		:      Optional[int]       	=					block_out_channels[i]

									if i == 0:
										input_channel += extra_in_channels

									UpperCAmelCase_		:      int       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Any       	=					get_down_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_downsample=not is_final_block or downsample_each_block    ,			)
									self.down_blocks.append(_snake_case)

								# mid
								UpperCAmelCase_		:      Optional[int]       	=					get_mid_block(
								    _snake_case    ,			in_channels=block_out_channels[-1]    ,			mid_channels=block_out_channels[-1]    ,			out_channels=block_out_channels[-1]    ,			embed_dim=block_out_channels[0]    ,			num_layers=_snake_case    ,			add_downsample=_snake_case    ,			)

								# up
								UpperCAmelCase_		:      Union[str, Any]       	=					list(reversed(_snake_case))
								UpperCAmelCase_		:      Optional[int]       	=					reversed_block_out_channels[0]
								if out_block_type is None:
									UpperCAmelCase_		:      Tuple       	=					out_channels
								else:
									UpperCAmelCase_		:      int       	=					block_out_channels[0]

								for i, up_block_type in enumerate(_snake_case):
									UpperCAmelCase_		:      Dict       	=					output_channel
									UpperCAmelCase_		:      Optional[Any]       	=					(
									    reversed_block_out_channels[i + 1] if i < len(_snake_case) - 1 else final_upsample_channels
									)

									UpperCAmelCase_		:      str       	=					i == len(_snake_case) - 1

									UpperCAmelCase_		:      Union[str, Any]       	=					get_up_block(
									    _snake_case    ,			num_layers=_snake_case    ,			in_channels=_snake_case    ,			out_channels=_snake_case    ,			temb_channels=block_out_channels[0]    ,			add_upsample=not is_final_block    ,			)
									self.up_blocks.append(_snake_case)
									UpperCAmelCase_		:      Dict       	=					output_channel

								# out
								UpperCAmelCase_		:      Union[str, Any]       	=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,			32)
								UpperCAmelCase_		:      Any       	=					get_out_block(
								    out_block_type=_snake_case    ,			num_groups_out=_snake_case    ,			embed_dim=block_out_channels[0]    ,			out_channels=_snake_case    ,			act_fn=_snake_case    ,			fc_dim=block_out_channels[-1] // 4    ,			)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = True    ,			)							->				Union[UNetaDOutput, Tuple]:
								UpperCAmelCase_		:      Union[str, Any]       	=					timestep
								if not torch.is_tensor(_snake_case):
									UpperCAmelCase_		:      Union[str, Any]       	=					torch.tensor([timesteps]    ,			dtype=torch.long    ,			device=sample.device)
								elif torch.is_tensor(_snake_case) and len(timesteps.shape) == 0:
									UpperCAmelCase_		:      Tuple       	=					timesteps[None].to(sample.device)

								UpperCAmelCase_		:      Any       	=					self.time_proj(_snake_case)
								if self.config.use_timestep_embedding:
									UpperCAmelCase_		:      int       	=					self.time_mlp(_snake_case)
								else:
									UpperCAmelCase_		:      int       	=					timestep_embed[..., None]
									UpperCAmelCase_		:      List[Any]       	=					timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
									UpperCAmelCase_		:      int       	=					timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

								# 2. down
								UpperCAmelCase_		:      Optional[Any]       	=					()
								for downsample_block in self.down_blocks:
									UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					downsample_block(hidden_states=_snake_case    ,			temb=_snake_case)
									down_block_res_samples += res_samples

								# 3. mid
								if self.mid_block:
									UpperCAmelCase_		:      List[Any]       	=					self.mid_block(_snake_case    ,			_snake_case)

								# 4. up
								for i, upsample_block in enumerate(self.up_blocks):
									UpperCAmelCase_		:      int       	=					down_block_res_samples[-1:]
									UpperCAmelCase_		:      Tuple       	=					down_block_res_samples[:-1]
									UpperCAmelCase_		:      List[Any]       	=					upsample_block(_snake_case    ,			res_hidden_states_tuple=_snake_case    ,			temb=_snake_case)

								# 5. post-process
								if self.out_block:
									UpperCAmelCase_		:      Optional[Any]       	=					self.out_block(_snake_case    ,			_snake_case)

								if not return_dict:
									return (sample,)

								return UNetaDOutput(sample=_snake_case)






