



"""simple docstring"""


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_    					=	"""http://www.mocksite.com/file1.txt"""
snake_case_    					=	"""\"text\": [\"foo\", \"foo\"]"""
snake_case_    					=	"""6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"""







class 				A_      :



						"""simple docstring"""
						__UpperCamelCase		    =							2_00
						__UpperCamelCase		    =							{"Content-Length": "100"}
						__UpperCamelCase		    =							{}






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  **lowercase_			:Optional[Any] )    ->      int:
												return [bytes(_UpperCAmelCase       ,  'utf-8' )]


def 			_lowerCAmelCase				(       *lowercase_		,     **lowercase_						):
						return MockResponse()

@pytest.mark.parametrize('urls_type'		,     [str, list, dict]						)
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						import requests

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

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

@pytest.mark.parametrize('paths_type'		,     [str, list, dict]						)
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			str(__UpperCAmelCase						)
						if issubclass(__UpperCAmelCase		,     __UpperCAmelCase						):
												UpperCAmelCase					=			filename
						elif issubclass(__UpperCAmelCase		,     __UpperCAmelCase						):
												UpperCAmelCase					=			[filename]
						elif issubclass(__UpperCAmelCase		,     __UpperCAmelCase						):
												UpperCAmelCase					=			{'''train''': filename}
						UpperCAmelCase					=			'''dummy'''
						UpperCAmelCase					=			xz_file.parent
						UpperCAmelCase					=			'''extracted'''
						UpperCAmelCase					=			DownloadConfig(
						    cache_dir=__UpperCAmelCase		,     use_etag=__UpperCAmelCase		,     )
						UpperCAmelCase					=			DownloadManager(dataset_name=__UpperCAmelCase		,     download_config=__UpperCAmelCase						)
						UpperCAmelCase					=			dl_manager.extract(__UpperCAmelCase						)
						UpperCAmelCase					=			paths
						for extracted_paths in [extracted_paths]:
												if isinstance(__UpperCAmelCase		,     __UpperCAmelCase						):
																		UpperCAmelCase					=			[extracted_paths]
																		UpperCAmelCase					=			[paths]
												elif isinstance(__UpperCAmelCase		,     __UpperCAmelCase						):
																		assert "train" in extracted_paths.keys()
																		UpperCAmelCase					=			extracted_paths.values()
																		UpperCAmelCase					=			paths.values()
												assert extracted_paths
												for extracted_path, input_path in zip(__UpperCAmelCase		,     __UpperCAmelCase						):
																		assert extracted_path == dl_manager.extracted_paths[input_path]
																		UpperCAmelCase					=			Path(__UpperCAmelCase						)
																		UpperCAmelCase					=			extracted_path.parts
																		assert parts[-1] == hash_url_to_filename(__UpperCAmelCase		,     etag=__UpperCAmelCase						)
																		assert parts[-2] == extracted_subdir
																		assert extracted_path.exists()
																		UpperCAmelCase					=			extracted_path.read_text()
																		UpperCAmelCase					=			text_file.read_text()
																		assert extracted_file_content == expected_file_content

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						assert path.endswith('.jsonl'						)
						for num_items, line in enumerate(__UpperCAmelCase		,     start=1						):
												UpperCAmelCase					=			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 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			request.getfixturevalue(__UpperCAmelCase						)
						UpperCAmelCase					=			DownloadManager()
						for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__UpperCAmelCase						)		,     start=1						):
												_test_jsonl(__UpperCAmelCase		,     __UpperCAmelCase						)
						assert num_jsonl == 2

@pytest.mark.parametrize('archive_nested_jsonl'		,     ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']						)
def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			request.getfixturevalue(__UpperCAmelCase						)
						UpperCAmelCase					=			DownloadManager()
						for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__UpperCAmelCase						)		,     start=1						):
												for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__UpperCAmelCase						)		,     start=1						):
																		_test_jsonl(__UpperCAmelCase		,     __UpperCAmelCase						)
						assert num_tar == 1
						assert num_jsonl == 2






def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			DownloadManager()
						for num_file, file in enumerate(dl_manager.iter_files(__UpperCAmelCase						)		,     start=1						):
												assert os.path.basename(__UpperCAmelCase						) == ("test.txt" if num_file == 1 else "train.txt")
						assert num_file == 2










"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""
						__UpperCamelCase		    =							(UniPCMultistepScheduler,)
						__UpperCamelCase		    =							(("""num_inference_steps""", 25),)






						def      UpperCAmelCase__					( self			:Any       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												UpperCAmelCase					=			{
												    'num_train_timesteps': 10_00,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'solver_order': 2,
												    'solver_type': 'bh2',
												}

												config.update(**lowercase_ )
												return config






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Optional[int]=0       ,  **lowercase_			:Optional[Any] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )
																		# copy over dummy past residuals
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

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

																		UpperCAmelCase				,   UpperCAmelCase					=			sample, sample
																		for t in range(lowercase_       ,  time_step + scheduler.config.solver_order + 1 ):
																								UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																								UpperCAmelCase					=			new_scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Dict=0       ,  **lowercase_			:Optional[int] )    ->      List[str]:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )
												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )
												UpperCAmelCase					=			self.dummy_sample
												UpperCAmelCase					=			0.1 * sample
												UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )
																		scheduler.set_timesteps(lowercase_ )

																		# copy over dummy past residuals (must be after setting timesteps)
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

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

																								# copy over dummy past residual (must be after setting timesteps)
																								UpperCAmelCase					=			dummy_past_residuals[: new_scheduler.config.solver_order]

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			new_scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Optional[int]=None       ,  **lowercase_			:str )    ->      Dict:
												if scheduler is None:
																		UpperCAmelCase					=			self.scheduler_classes[0]
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter
												scheduler.set_timesteps(lowercase_ )

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

												return sample






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			dict(self.forward_default_kwargs )

												UpperCAmelCase					=			kwargs.pop('num_inference_steps'       ,  lowercase_ )

												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config()
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		UpperCAmelCase					=			self.dummy_sample
																		UpperCAmelCase					=			0.1 * sample

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

																		# copy over dummy past residuals (must be done after set_timesteps)
																		UpperCAmelCase					=			[residual + 0.2, residual + 0.15, residual + 0.10]
																		UpperCAmelCase					=			dummy_past_residuals[: scheduler.config.solver_order]

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

																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample
																		UpperCAmelCase					=			scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  **lowercase_ ).prev_sample

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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												UpperCAmelCase					=			UniPCMultistepScheduler(**self.get_scheduler_config() )
												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3

												UpperCAmelCase					=			DPMSolverSinglestepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			DEISMultistepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			DPMSolverMultistepScheduler.from_config(scheduler.config )
												UpperCAmelCase					=			UniPCMultistepScheduler.from_config(scheduler.config )

												UpperCAmelCase					=			self.full_loop(scheduler=lowercase_ )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																		self.check_over_configs(num_train_timesteps=lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      str:
												self.check_over_configs(thresholding=lowercase_ )
												for order in [1, 2, 3]:
																		for solver_type in ["bh1", "bh2"]:
																								for threshold in [0.5, 1.0, 2.0]:
																														for prediction_type in ["epsilon", "sample"]:
																																				self.check_over_configs(
																																				    thresholding=lowercase_       ,  prediction_type=lowercase_       ,  sample_max_value=lowercase_       ,  solver_order=lowercase_       ,  solver_type=lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Any )    ->      str:
												for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Optional[Any]:
												for solver_type in ["bh1", "bh2"]:
																		for order in [1, 2, 3]:
																								for prediction_type in ["epsilon", "sample"]:
																														self.check_over_configs(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														UpperCAmelCase					=			self.full_loop(
																														    solver_order=lowercase_       ,  solver_type=lowercase_       ,  prediction_type=lowercase_       ,  )
																														assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"






						def      UpperCAmelCase__					( self			:Dict )    ->      List[Any]:
												self.check_over_configs(lower_order_final=lowercase_ )
												self.check_over_configs(lower_order_final=lowercase_ )






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																		self.check_over_forward(num_inference_steps=lowercase_       ,  time_step=0 )






						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			self.full_loop()
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.2464 ) < 1E-3






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      str:
												UpperCAmelCase					=			self.full_loop(prediction_type='v_prediction' )
												UpperCAmelCase					=			torch.mean(torch.abs(lowercase_ ) )

												assert abs(result_mean.item() - 0.1014 ) < 1E-3






						def      UpperCAmelCase__					( self			:Tuple )    ->      Tuple:
												UpperCAmelCase					=			self.scheduler_classes[0]
												UpperCAmelCase					=			self.get_scheduler_config(thresholding=lowercase_       ,  dynamic_thresholding_ratio=0 )
												UpperCAmelCase					=			scheduler_class(**lowercase_ )

												UpperCAmelCase					=			10
												UpperCAmelCase					=			self.dummy_model()
												UpperCAmelCase					=			self.dummy_sample_deter.half()
												scheduler.set_timesteps(lowercase_ )

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

												assert sample.dtype == torch.floataa






						def      UpperCAmelCase__					( self			:List[Any]       ,  **lowercase_			:Optional[Any] )    ->      Dict:
												for scheduler_class in self.scheduler_classes:
																		UpperCAmelCase					=			self.get_scheduler_config(**lowercase_ )
																		UpperCAmelCase					=			scheduler_class(**lowercase_ )

																		scheduler.set_timesteps(scheduler.config.num_train_timesteps )
																		assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps





