





import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_lowerCAmelCase							:					List[str]		    =		datasets.utils.logging.get_logger(__name__)


@dataclass
class     __magic_name__     (							datasets.BuilderConfig							):





     """simple docstring"""



     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   '''utf-8'''
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   None
     __UpperCamelCase    			=   True  # deprecated
     __UpperCamelCase    			=   None  # deprecated
     __UpperCamelCase    			=   10 << 20  # 10MB
     __UpperCamelCase    			=   None




class     __magic_name__     (							datasets.ArrowBasedBuilder							):





     """simple docstring"""



     __UpperCamelCase    			=   JsonConfig



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





        '''simple docstring'''




        if self.config.block_size is not None:
           logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"     )
           A_    : str								=	self.config.block_size
        if self.config.use_threads is not True:
           logger.warning(
               "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."     )
        if self.config.newlines_in_values is not None:
           raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"     )
        return datasets.DatasetInfo(features=self.config.features     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:Optional[int]     ):





        '''simple docstring'''




        if not self.config.data_files:
           raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"     )
        A_    : Optional[Any]								=	dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(a_	,					(str, list, tuple)     ):
           A_    : List[Any]								=	data_files
           if isinstance(a_	,					a_     ):
              A_    : Any								=	[files]
           A_    : str								=	[dl_manager.iter_files(a_     ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,					gen_kwargs={"files": files}     )]
        A_    : Optional[Any]								=	[]
        for split_name, files in data_files.items():
           if isinstance(a_	,					a_     ):
              A_    : Union[str, Any]								=	[files]
           A_    : List[str]								=	[dl_manager.iter_files(a_     ) for file in files]
           splits.append(datasets.SplitGenerator(name=a_	,					gen_kwargs={"files": files}     )     )
        return splits



     def   SCREAMING_SNAKE_CASE						(							self				:Dict	,					snake_case				:Optional[Any]     ):





        '''simple docstring'''




        if self.config.features is not None:
           # adding missing columns
           for column_name in set(self.config.features     ) - set(pa_table.column_names     ):
              A_    : Tuple								=	self.config.features.arrow_schema.field(a_     ).type
              A_    : Dict								=	pa_table.append_column(a_	,					pa.array([None] * len(a_     )	,					type=a_     )     )
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           A_    : List[str]								=	table_cast(a_	,					self.config.features.arrow_schema     )
        return pa_table





     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:Tuple     ):





        '''simple docstring'''




        for file_idx, file in enumerate(itertools.chain.from_iterable(a_     )     ):
           # If the file is one json object and if we need to look at the list of items in one specific field
           if self.config.field is not None:
              with open(a_	,					encoding=self.config.encoding	,					errors=self.config.encoding_errors     ) as f:
                 A_    : Any								=	json.load(a_     )

              # We keep only the field we are interested in
              A_    : Any								=	dataset[self.config.field]

              # We accept two format: a list of dicts or a dict of lists
              if isinstance(a_	,					(list, tuple)     ):
                 A_    : Union[str, Any]								=	set().union(*[row.keys() for row in dataset]     )
                 A_    : Dict								=	{col: [row.get(a_     ) for row in dataset] for col in keys}
              else:
                 A_    : List[Any]								=	dataset
              A_    : Tuple								=	pa.Table.from_pydict(a_     )
              yield file_idx, self._cast_table(a_     )

           # If the file has one json object per line
           else:
              with open(a_	,					"rb"     ) as f:
                 A_    : Optional[Any]								=	0
                 # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                 # Set a default minimum value of 16kB if the chunk size is really small
                 A_    : List[Any]								=	max(self.config.chunksize // 32	,					16 << 10     )
                 A_    : Dict								=	(
                     self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                 )
                 while True:
                    A_    : int								=	f.read(self.config.chunksize     )
                    if not batch:
                       break
                    # Finish current line
                    try:
                       batch += f.readline()
                    except (AttributeError, io.UnsupportedOperation):
                       batch += readline(a_     )
                    # PyArrow only accepts utf-8 encoded bytes
                    if self.config.encoding != "utf-8":
                       A_    : int								=	batch.decode(self.config.encoding	,					errors=a_     ).encode("utf-8"     )
                    try:
                       while True:
                          try:
                             A_    : Optional[int]								=	paj.read_json(
                                 io.BytesIO(a_     )	,					read_options=paj.ReadOptions(block_size=a_     )     )
                             break
                          except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                             if (
                                 isinstance(a_	,					pa.ArrowInvalid     )
                                 and "straddling" not in str(a_     )
                                 or block_size > len(a_     )
                             ):
                                raise
                             else:
                                # Increase the block size in case it was too small.
                                # The block size will be reset for the next file.
                                logger.debug(
                                    f"Batch of {len(a_     )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."     )
                                block_size *= 2
                    except pa.ArrowInvalid as e:
                       try:
                          with open(
                              a_	,					encoding=self.config.encoding	,					errors=self.config.encoding_errors     ) as f:
                             A_    : List[Any]								=	json.load(a_     )
                       except json.JSONDecodeError:
                          logger.error(f"Failed to read file \'{file}\' with error {type(a_     )}: {e}"     )
                          raise e
                       # If possible, parse the file as a list of json objects and exit the loop
                       if isinstance(a_	,					a_     ):  # list is the only sequence type supported in JSON
                          try:
                             A_    : Any								=	set().union(*[row.keys() for row in dataset]     )
                             A_    : int								=	{col: [row.get(a_     ) for row in dataset] for col in keys}
                             A_    : Any								=	pa.Table.from_pydict(a_     )
                          except (pa.ArrowInvalid, AttributeError) as e:
                             logger.error(f"Failed to read file \'{file}\' with error {type(a_     )}: {e}"     )
                             raise ValueError(f"Not able to read records in the JSON file at {file}."     ) from None
                          yield file_idx, self._cast_table(a_     )
                          break
                       else:
                          logger.error(f"Failed to read file \'{file}\' with error {type(a_     )}: {e}"     )
                          raise ValueError(
                              f"Not able to read records in the JSON file at {file}. "
                              f"You should probably indicate the field of the JSON file containing your records. "
                              f"This JSON file contain the following fields: {str(list(dataset.keys()     )     )}. "
                              f"Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. "     ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), self._cast_table(a_     )
                    batch_idx += 1





'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


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







 def _snake_case (							self  ,**a_							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = {"""num_train_timesteps""": 1_000}
        config.update(**a_							)
        return config







 def _snake_case (							self  ,a_=0  ,**a_							)		->   List[str]:
        _UpperCAmelCase  :						Any			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Any			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.dummy_sample
        _UpperCAmelCase  :						Union[str, Any]			     = 0.1 * sample
        _UpperCAmelCase  :						List[str]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						str			     = self.get_scheduler_config(**a_							)
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)
               # copy over dummy past residuals
               _UpperCAmelCase  :						str			     = dummy_past_residuals[:]

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

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						List[str]			     = scheduler_class.from_pretrained(a_							)
                      new_scheduler.set_timesteps(a_							)
                      # copy over dummy past residuals
                      _UpperCAmelCase  :						Tuple			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self  ,a_=0  ,**a_							)		->   Dict:
        _UpperCAmelCase  :						str			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Union[str, Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
        _UpperCAmelCase  :						Tuple			     = 0.1 * sample
        _UpperCAmelCase  :						Optional[Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						Optional[Any]			     = self.get_scheduler_config()
               _UpperCAmelCase  :						Dict			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)

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

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

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

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

               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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







 def _snake_case (							self  ,**a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[str]			     = self.get_scheduler_config(**a_							)
        _UpperCAmelCase  :						List[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						List[Any]			     = 10
        _UpperCAmelCase  :						Dict			     = self.dummy_model()
        _UpperCAmelCase  :						Dict			     = self.dummy_sample_deter
        scheduler.set_timesteps(a_							)

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						Optional[Any]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						List[str]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						int			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        return sample







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						int			     = dict(self.forward_default_kwargs							)

        _UpperCAmelCase  :						Optional[Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						int			     = self.get_scheduler_config()
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)

               _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
               _UpperCAmelCase  :						Tuple			     = 0.1 * sample

               if num_inference_steps is not None and hasattr(a_  ,"""set_timesteps"""							):
                      scheduler.set_timesteps(a_							)
               elif num_inference_steps is not None and not hasattr(a_  ,"""set_timesteps"""							):
                      _UpperCAmelCase  :						Optional[Any]			     = num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               _UpperCAmelCase  :						Union[str, Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               _UpperCAmelCase  :						Optional[int]			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.timesteps[5]
               _UpperCAmelCase  :						Optional[Any]			     = scheduler.timesteps[6]

               _UpperCAmelCase  :						Optional[Any]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Optional[int]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

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







 def _snake_case (							self							)		->   List[Any]:
        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   int:
        for t, num_inference_steps in zip([1, 5, 10]  ,[10, 50, 100]							):
               self.check_over_forward(num_inference_steps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = self.full_loop()
        _UpperCAmelCase  :						Any			     = torch.mean(torch.abs(a_							)							)

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



