






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


A__      :     Optional[int]       =					'http://www.mocksite.com/file1.txt'
A__      :     Any       =					'"text": ["foo", "foo"]'
A__      :     Optional[Any]       =					'6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'
class  _UpperCAmelCase   :

				"""simple docstring"""




				lowercase__											=			200
				lowercase__											=			{"""Content-Length""": """100"""}
				lowercase__											=			{}




				def 							lowercase__  (	self		:       Tuple,    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								return [bytes(lowerCamelCase,    '''utf-8'''    )]






def     a     (		*lowerCamelCase_						,  **lowerCamelCase_							):






				'''simple docstring'''
				return MockResponse()






@pytest.mark.parametrize('''urls_type'''						,  [str, list, dict]							)
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				import requests

				monkeypatch.setattr(lowerCamelCase_						,  '''request'''						,  lowerCamelCase_							)

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






@pytest.mark.parametrize('''paths_type'''						,  [str, list, dict]							)
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     str(lowerCamelCase_							)
				if issubclass(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     filename
				elif issubclass(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     [filename]
				elif issubclass(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     {'''train''': filename}
				lowercase__  					=     '''dummy'''
				lowercase__  					=     xz_file.parent
				lowercase__  					=     '''extracted'''
				lowercase__  					=     DownloadConfig(
				    cache_dir=lowerCamelCase_						,  use_etag=lowerCamelCase_						,  )
				lowercase__  					=     DownloadManager(dataset_name=lowerCamelCase_						,  download_config=lowerCamelCase_							)
				lowercase__  					=     dl_manager.extract(lowerCamelCase_							)
				lowercase__  					=     paths
				for extracted_paths in [extracted_paths]:
								if isinstance(lowerCamelCase_						,  lowerCamelCase_							):
												lowercase__  					=     [extracted_paths]
												lowercase__  					=     [paths]
								elif isinstance(lowerCamelCase_						,  lowerCamelCase_							):
												assert "train" in extracted_paths.keys()
												lowercase__  					=     extracted_paths.values()
												lowercase__  					=     paths.values()
								assert extracted_paths
								for extracted_path, input_path in zip(lowerCamelCase_						,  lowerCamelCase_							):
												assert extracted_path == dl_manager.extracted_paths[input_path]
												lowercase__  					=     Path(lowerCamelCase_							)
												lowercase__  					=     extracted_path.parts
												assert parts[-1] == hash_url_to_filename(lowerCamelCase_						,  etag=lowerCamelCase_							)
												assert parts[-2] == extracted_subdir
												assert extracted_path.exists()
												lowercase__  					=     extracted_path.read_text()
												lowercase__  					=     text_file.read_text()
												assert extracted_file_content == expected_file_content






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				assert path.endswith('''.jsonl'''							)
				for num_items, line in enumerate(lowerCamelCase_						,  start=1							):
								lowercase__  					=     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     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     request.getfixturevalue(lowerCamelCase_							)
				lowercase__  					=     DownloadManager()
				for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase_							)						,  start=1							):
								_test_jsonl(lowerCamelCase_						,  lowerCamelCase_							)
				assert num_jsonl == 2






@pytest.mark.parametrize('''archive_nested_jsonl'''						,  ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']							)
def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     request.getfixturevalue(lowerCamelCase_							)
				lowercase__  					=     DownloadManager()
				for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase_							)						,  start=1							):
								for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCamelCase_							)						,  start=1							):
												_test_jsonl(lowerCamelCase_						,  lowerCamelCase_							)
				assert num_tar == 1
				assert num_jsonl == 2






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     DownloadManager()
				for num_file, file in enumerate(dl_manager.iter_files(lowerCamelCase_							)						,  start=1							):
								assert os.path.basename(lowerCamelCase_							) == ("test.txt" if num_file == 1 else "train.txt")
				assert num_file == 2












import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																new_scheduler.set_timesteps(lowerCamelCase    )
																# copy over dummy past residuals
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase    )

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       int,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

												lowercase__  					=     self.dummy_sample
												lowercase__  					=     0.1 * sample

												if num_inference_steps is not None and hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																scheduler.set_timesteps(lowerCamelCase    )
												elif num_inference_steps is not None and not hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																lowercase__  					=     num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.timesteps[5]
												lowercase__  					=     scheduler.timesteps[6]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												self.assertEqual(output_a.shape,    sample.shape    )
												self.assertEqual(output_a.shape,    output_a.shape    )

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												self.assertEqual(output_a.shape,    sample.shape    )
												self.assertEqual(output_a.shape,    output_a.shape    )




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




