

import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__snake_case				:      int   		=			"""http://www.mocksite.com/file1.txt"""
__snake_case				:      Dict   		=			"""\"text\": [\"foo\", \"foo\"]"""
__snake_case				:      Optional[Any]   		=			"""6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"""



class        A__:
						"""simple docstring"""






						_A    :				Dict       					=			200
						_A    :				Union[str, Any]       					=			{'''Content-Length''': '''100'''}
						_A    :				Optional[int]       					=			{}





						def 			UpperCamelCase__					(    self  ,      **_lowercase    )     -> Union[str, Any]:
									return [bytes(_lowercase  ,      """utf-8"""    )]







def 					_UpperCAmelCase				(						*a__  ,	**a__):

			'''simple docstring'''



			return MockResponse()


@pytest.mark.parametrize("""urls_type"""  ,	[str, list, dict])
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			import requests

			monkeypatch.setattr(_snake_case  ,	"""request"""  ,	_snake_case)

			a_						:						List[Any] 							= URL
			if issubclass(_snake_case  ,	_snake_case):
						a_						:						Any 							= url
			elif issubclass(_snake_case  ,	_snake_case):
						a_						:						Optional[Any] 							= [url]
			elif issubclass(_snake_case  ,	_snake_case):
						a_						:						str 							= {"""train""": url}
			a_						:						Optional[Any] 							= """dummy"""
			a_						:						Optional[Any] 							= """downloads"""
			a_						:						Dict 							= tmp_path
			a_						:						int 							= DownloadConfig(
			    cache_dir=os.path.join(_snake_case  ,	_snake_case)  ,	use_etag=_snake_case  ,	)
			a_						:						Tuple 							= DownloadManager(dataset_name=_snake_case  ,	download_config=_snake_case)
			a_						:						Any 							= dl_manager.download(_snake_case)
			a_						:						int 							= urls
			for downloaded_paths in [downloaded_paths]:
						if isinstance(_snake_case  ,	_snake_case):
									a_						:						Union[str, Any] 							= [downloaded_paths]
									a_						:						int 							= [urls]
						elif isinstance(_snake_case  ,	_snake_case):
									assert "train" in downloaded_paths.keys()
									a_						:						List[str] 							= downloaded_paths.values()
									a_						:						Any 							= urls.values()
						assert downloaded_paths
						for downloaded_path, input_url in zip(_snake_case  ,	_snake_case):
									assert downloaded_path == dl_manager.downloaded_paths[input_url]
									a_						:						Tuple 							= Path(_snake_case)
									a_						:						Union[str, Any] 							= downloaded_path.parts
									assert parts[-1] == HASH
									assert parts[-2] == cache_subdir
									assert downloaded_path.exists()
									a_						:						int 							= downloaded_path.read_text()
									assert content == CONTENT
									a_						:						Union[str, Any] 							= downloaded_path.with_suffix(""".json""")
									assert metadata_downloaded_path.exists()
									a_						:						Optional[Any] 							= json.loads(metadata_downloaded_path.read_text())
									assert metadata_content == {"url": URL, "etag": None}


@pytest.mark.parametrize("""paths_type"""  ,	[str, list, dict])
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= str(_snake_case)
			if issubclass(_snake_case  ,	_snake_case):
						a_						:						Dict 							= filename
			elif issubclass(_snake_case  ,	_snake_case):
						a_						:						Optional[int] 							= [filename]
			elif issubclass(_snake_case  ,	_snake_case):
						a_						:						Union[str, Any] 							= {"""train""": filename}
			a_						:						Union[str, Any] 							= """dummy"""
			a_						:						List[str] 							= xz_file.parent
			a_						:						Any 							= """extracted"""
			a_						:						Optional[int] 							= DownloadConfig(
			    cache_dir=_snake_case  ,	use_etag=_snake_case  ,	)
			a_						:						str 							= DownloadManager(dataset_name=_snake_case  ,	download_config=_snake_case)
			a_						:						Tuple 							= dl_manager.extract(_snake_case)
			a_						:						List[Any] 							= paths
			for extracted_paths in [extracted_paths]:
						if isinstance(_snake_case  ,	_snake_case):
									a_						:						Union[str, Any] 							= [extracted_paths]
									a_						:						Any 							= [paths]
						elif isinstance(_snake_case  ,	_snake_case):
									assert "train" in extracted_paths.keys()
									a_						:						List[str] 							= extracted_paths.values()
									a_						:						str 							= paths.values()
						assert extracted_paths
						for extracted_path, input_path in zip(_snake_case  ,	_snake_case):
									assert extracted_path == dl_manager.extracted_paths[input_path]
									a_						:						Optional[Any] 							= Path(_snake_case)
									a_						:						Tuple 							= extracted_path.parts
									assert parts[-1] == hash_url_to_filename(_snake_case  ,	etag=_snake_case)
									assert parts[-2] == extracted_subdir
									assert extracted_path.exists()
									a_						:						Optional[Any] 							= extracted_path.read_text()
									a_						:						Optional[Any] 							= text_file.read_text()
									assert extracted_file_content == expected_file_content


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			assert path.endswith(""".jsonl""")
			for num_items, line in enumerate(_snake_case  ,	start=1):
						a_						:						Any 							= json.loads(line.decode("""utf-8"""))
						assert item.keys() == {"col_1", "col_2", "col_3"}
			assert num_items == 4


@pytest.mark.parametrize("""archive_jsonl"""  ,	["""tar_jsonl_path""", """zip_jsonl_path"""])
def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= request.getfixturevalue(_snake_case)
			a_						:						str 							= DownloadManager()
			for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_snake_case)  ,	start=1):
						_test_jsonl(_snake_case  ,	_snake_case)
			assert num_jsonl == 2


@pytest.mark.parametrize("""archive_nested_jsonl"""  ,	["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""])
def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= request.getfixturevalue(_snake_case)
			a_						:						str 							= DownloadManager()
			for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_snake_case)  ,	start=1):
						for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_snake_case)  ,	start=1):
									_test_jsonl(_snake_case  ,	_snake_case)
			assert num_tar == 1
			assert num_jsonl == 2



def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= DownloadManager()
			for num_file, file in enumerate(dl_manager.iter_files(_snake_case)  ,	start=1):
						assert os.path.basename(_snake_case) == ("test.txt" if num_file == 1 else "train.txt")
			assert num_file == 2










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =UNetaDModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=3,      out_channels=3,      down_block_types=('DownBlock2D', 'AttnDownBlock2D'),      up_block_types=('AttnUpBlock2D', 'UpBlock2D'),      )
          return model





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

          _lowercase              =image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


