



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   int						 =						(DPMSolverSinglestepScheduler,)
       UpperCamelCase__  :   Dict						 =						(("""num_inference_steps""", 25),)



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''solver_order''': 2,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
               '''sample_max_value''': 1.0,
               '''algorithm_type''': '''dpmsolver++''',
               '''solver_type''': '''midpoint''',
               '''lambda_min_clipped''': -float('''inf'''),
               '''variance_type''': None,
           }

           config.update(**_lowercase)
           return config



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0					,		**__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)
           __a							=    kwargs.pop('''num_inference_steps'''					,		_lowercase)
           __a							=    self.dummy_sample
           __a							=    0.1 * sample
           __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

               __a	,       __a							=    sample, sample
               for t in range(_lowercase					,		time_step + scheduler.config.solver_order + 1):
                   __a							=    scheduler.step(_lowercase					,		_lowercase					,		_lowercase					,		**_lowercase).prev_sample
                   __a							=    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 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int=0					,		**__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    dict(self.forward_default_kwargs)
           __a							=    kwargs.pop('''num_inference_steps'''					,		_lowercase)
           __a							=    self.dummy_sample
           __a							=    0.1 * sample
           __a							=    [residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
               __a							=    self.get_scheduler_config()
               __a							=    scheduler_class(**_lowercase)
               scheduler.set_timesteps(_lowercase)

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowercase)
                   __a							=    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)
                   __a							=    dummy_past_residuals[: new_scheduler.config.solver_order]

               __a							=    scheduler.step(_lowercase					,		_lowercase					,		_lowercase					,		**_lowercase).prev_sample
               __a							=    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 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=None					,		**__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           if scheduler is None:
               __a							=    self.scheduler_classes[0]
               __a							=    self.get_scheduler_config(**_lowercase)
               __a							=    scheduler_class(**_lowercase)

           __a							=    self.scheduler_classes[0]
           __a							=    self.get_scheduler_config(**_lowercase)
           __a							=    scheduler_class(**_lowercase)

           __a							=    10
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter
           scheduler.set_timesteps(_lowercase)

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

           return sample



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           __a							=    50
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter
           scheduler.set_timesteps(_lowercase)

           # make sure that the first t is uneven
           for i, t in enumerate(scheduler.timesteps[3:]):
               __a							=    model(_lowercase					,		_lowercase)
               __a							=    scheduler.step(_lowercase					,		_lowercase					,		_lowercase).prev_sample

           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.25_74) < 1E-3



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           for timesteps in [25, 50, 100, 999, 1_000]:
               self.check_over_configs(num_train_timesteps=_lowercase)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    DPMSolverSinglestepScheduler(**self.get_scheduler_config())
           __a							=    self.full_loop(scheduler=_lowercase)
           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.27_91) < 1E-3

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

           __a							=    self.full_loop(scheduler=_lowercase)
           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.27_91) < 1E-3



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self.check_over_configs(thresholding=_lowercase)
           for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                   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					,		algorithm_type='''dpmsolver++'''					,		solver_order=_lowercase					,		solver_type=_lowercase					,		)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=_lowercase)



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                   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					,		algorithm_type=_lowercase					,		)
                           __a							=    self.full_loop(
                               solver_order=_lowercase					,		solver_type=_lowercase					,		prediction_type=_lowercase					,		algorithm_type=_lowercase					,		)
                           assert not torch.isnan(_lowercase).any(), "Samples have nan numbers"



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self.check_over_configs(lower_order_final=_lowercase)
           self.check_over_configs(lower_order_final=_lowercase)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           self.check_over_configs(lambda_min_clipped=-float('''inf'''))
           self.check_over_configs(lambda_min_clipped=-5.1)



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           self.check_over_configs(variance_type=_lowercase)
           self.check_over_configs(variance_type='''learned_range''')



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
               self.check_over_forward(num_inference_steps=_lowercase					,		time_step=0)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.full_loop()
           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.27_91) < 1E-3



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.full_loop(use_karras_sigmas=_lowercase)
           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.22_48) < 1E-3



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    self.full_loop(prediction_type='''v_prediction''')
           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.14_53) < 1E-3



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    self.full_loop(prediction_type='''v_prediction'''					,		use_karras_sigmas=_lowercase)
           __a							=    torch.mean(torch.abs(_lowercase))

           assert abs(result_mean.item() - 0.06_49) < 1E-3





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    self.scheduler_classes[0]
           __a							=    self.get_scheduler_config(thresholding=_lowercase					,		dynamic_thresholding_ratio=0)
           __a							=    scheduler_class(**_lowercase)

           __a							=    10
           __a							=    self.dummy_model()
           __a							=    self.dummy_sample_deter.half()
           scheduler.set_timesteps(_lowercase)

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

           assert sample.dtype == torch.floataa










import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					NestedDataStructureLike[PathLike]					,		__SCREAMING_SNAKE_CASE							:					Optional[NamedSplit] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[Features] = None					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					Any					,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE					,		split=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		cache_dir=__SCREAMING_SNAKE_CASE					,		keep_in_memory=__SCREAMING_SNAKE_CASE					,		streaming=__SCREAMING_SNAKE_CASE					,		num_proc=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    field
           __a							=    path_or_paths if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a							=    Json(
               cache_dir=__SCREAMING_SNAKE_CASE					,		data_files=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		field=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           if self.streaming:
               __a							=    self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a							=    None
               __a							=    None
               __a							=    None
               __a							=    None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE					,		download_mode=__SCREAMING_SNAKE_CASE					,		verification_mode=__SCREAMING_SNAKE_CASE					,		base_path=__SCREAMING_SNAKE_CASE					,		num_proc=self.num_proc					,		)
               __a							=    self.builder.as_dataset(
                   split=self.split					,		verification_mode=__SCREAMING_SNAKE_CASE					,		in_memory=self.keep_in_memory)
           return dataset


class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dataset					,		__SCREAMING_SNAKE_CASE							:					Union[PathLike, BinaryIO]					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           if num_proc is not None and num_proc <= 0:
               raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

           __a							=    dataset
           __a							=    path_or_buf
           __a							=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           __a							=    num_proc
           __a							=    '''utf-8'''
           __a							=    to_json_kwargs



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.to_json_kwargs.pop('''path_or_buf'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.to_json_kwargs.pop('''orient'''					,		'''records''')
           __a							=    self.to_json_kwargs.pop('''lines'''					,		True if orient == '''records''' else False)
           __a							=    self.to_json_kwargs.pop('''index'''					,		False if orient in ['''split''', '''table'''] else True)
           __a							=    self.to_json_kwargs.pop('''compression'''					,		__SCREAMING_SNAKE_CASE)

           if compression not in [None, "infer", "gzip", "bz2", "xz"]:
               raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

           if isinstance(self.path_or_buf					,		(str, bytes, os.PathLike)):
               with fsspec.open(self.path_or_buf					,		'''wb'''					,		compression=__SCREAMING_SNAKE_CASE) as buffer:
                   __a							=    self._write(file_obj=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           else:
               if compression:
                   raise NotImplementedError(
                       F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                       ''' was passed. Please provide a local path instead.''')
               __a							=    self._write(
                   file_obj=self.path_or_buf					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           return written



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a	,       __a	,       __a	,       __a	,       __a							=    args

           __a							=    query_table(
               table=self.dataset.data					,		key=slice(__SCREAMING_SNAKE_CASE					,		offset + self.batch_size)					,		indices=self.dataset._indices					,		)
           __a							=    batch.to_pandas().to_json(
               path_or_buf=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           if not json_str.endswith('''\n'''):
               json_str += "\n"
           return json_str.encode(self.encoding)





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					BinaryIO					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		):


           '''simple docstring'''

           __a							=    0

           if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0					,		len(self.dataset)					,		self.batch_size)					,		unit='''ba'''					,		disable=not logging.is_progress_bar_enabled()					,		desc='''Creating json from Arrow format'''					,		):
                   __a							=    self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(__SCREAMING_SNAKE_CASE)
           else:
               __a	,       __a							=    len(self.dataset), self.batch_size
               with multiprocessing.Pool(self.num_proc) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json					,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)]					,		)					,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,		unit='''ba'''					,		disable=not logging.is_progress_bar_enabled()					,		desc='''Creating json from Arrow format'''					,		):
                       written += file_obj.write(__SCREAMING_SNAKE_CASE)

           return written





