"""simple docstring"""






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__A			   = datasets.utils.logging.get_logger(__name__)

__A			   = ['''names''', '''prefix''']
__A			   = ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__A			   = ['''encoding_errors''', '''on_bad_lines''']
__A			   = ['''date_format''']


@dataclass
class     UpperCAmelCase						(datasets.BuilderConfig  ):



    """simple docstring"""

    _UpperCAmelCase	:str						    = ","
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:Optional[Union[int, List[int], str]]						    = "infer"
    _UpperCAmelCase	:Optional[List[str]]						    = None
    _UpperCAmelCase	:Optional[List[str]]						    = None
    _UpperCAmelCase	:Optional[Union[int, str, List[int], List[str]]]						    = None
    _UpperCAmelCase	:Optional[Union[List[int], List[str]]]						    = None
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:Optional[Literal["c", "python", "pyarrow"]]						    = None
    _UpperCAmelCase	:Dict[Union[int, str], Callable[[Any], Any]]						    = None
    _UpperCAmelCase	:Optional[list]						    = None
    _UpperCAmelCase	:Optional[list]						    = None
    _UpperCAmelCase	:bool						    = False
    _UpperCAmelCase	:Optional[Union[int, List[int]]]						    = None
    _UpperCAmelCase	:Optional[int]						    = None
    _UpperCAmelCase	:Optional[Union[str, List[str]]]						    = None
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:bool						    = False
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:str						    = "."
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:str						    = '"'
    _UpperCAmelCase	:int						    = 0
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:int						    = 0
    _UpperCAmelCase	:bool						    = True
    _UpperCAmelCase	:bool						    = False
    _UpperCAmelCase	:Optional[str]						    = None
    _UpperCAmelCase	:int						    = 1_0000
    _UpperCAmelCase	:Optional[datasets.Features]						    = None
    _UpperCAmelCase	:Optional[str]						    = "strict"
    _UpperCAmelCase	:Literal["error", "warn", "skip"]						    = "error"
    _UpperCAmelCase	:Optional[str]						    = None






    def _snake_case			(   self							):
       if self.delimiter is not None:
          lowercase__:   Optional[int]					=      self.delimiter
       if self.column_names is not None:
          lowercase__:   int					=      self.column_names






    @property
    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      {
           '''sep''': self.sep,
           '''header''': self.header,
           '''names''': self.names,
           '''index_col''': self.index_col,
           '''usecols''': self.usecols,
           '''prefix''': self.prefix,
           '''mangle_dupe_cols''': self.mangle_dupe_cols,
           '''engine''': self.engine,
           '''converters''': self.converters,
           '''true_values''': self.true_values,
           '''false_values''': self.false_values,
           '''skipinitialspace''': self.skipinitialspace,
           '''skiprows''': self.skiprows,
           '''nrows''': self.nrows,
           '''na_values''': self.na_values,
           '''keep_default_na''': self.keep_default_na,
           '''na_filter''': self.na_filter,
           '''verbose''': self.verbose,
           '''skip_blank_lines''': self.skip_blank_lines,
           '''thousands''': self.thousands,
           '''decimal''': self.decimal,
           '''lineterminator''': self.lineterminator,
           '''quotechar''': self.quotechar,
           '''quoting''': self.quoting,
           '''escapechar''': self.escapechar,
           '''comment''': self.comment,
           '''encoding''': self.encoding,
           '''dialect''': self.dialect,
           '''error_bad_lines''': self.error_bad_lines,
           '''warn_bad_lines''': self.warn_bad_lines,
           '''skipfooter''': self.skipfooter,
           '''doublequote''': self.doublequote,
           '''memory_map''': self.memory_map,
           '''float_precision''': self.float_precision,
           '''chunksize''': self.chunksize,
           '''encoding_errors''': self.encoding_errors,
           '''on_bad_lines''': self.on_bad_lines,
           '''date_format''': self.date_format,
       }

       # some kwargs must not be passed if they don't have a default value
       # some others are deprecated and we can also not pass them if they are the default value
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
          if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()    ,			SCREAMING_SNAKE_CASE__							):
             del pd_read_csv_kwargs[pd_read_csv_parameter]

                 # Remove 2.0 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 2):
          for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
             del pd_read_csv_kwargs[pd_read_csv_parameter]

                 # Remove 1.3 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
          for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
             del pd_read_csv_kwargs[pd_read_csv_parameter]

       return pd_read_csv_kwargs



class     UpperCAmelCase						(datasets.ArrowBasedBuilder  ):



    """simple docstring"""

    _UpperCAmelCase	:Any						    = CsvConfig






    def _snake_case			(   self							):
       return datasets.DatasetInfo(features=self.config.features							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       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}"""							)
       lowercase__:   Union[str, Any]					=      dl_manager.download_and_extract(self.config.data_files							)
       if isinstance(SCREAMING_SNAKE_CASE__    ,			(str, list, tuple)							):
          lowercase__:   List[Any]					=      data_files
          if isinstance(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							):
             lowercase__:   Optional[int]					=      [files]
          lowercase__:   List[Any]					=      [dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,			gen_kwargs={'''files''': files}							)]
       lowercase__:   Dict					=      []
       for split_name, files in data_files.items():
          if isinstance(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							):
             lowercase__:   str					=      [files]
          lowercase__:   str					=      [dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
          splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__    ,			gen_kwargs={'''files''': files}							)							)
       return splits






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if self.config.features is not None:
          lowercase__:   Tuple					=      self.config.features.arrow_schema
          if all(not require_storage_cast(SCREAMING_SNAKE_CASE__							) for feature in self.config.features.values()							):
             # cheaper cast
             lowercase__:   Union[str, Any]					=      pa.Table.from_arrays([pa_table[field.name] for field in schema]    ,			schema=SCREAMING_SNAKE_CASE__							)
          else:
             # more expensive cast; allows str <-> int/float or str to Audio for example
             lowercase__:   List[Any]					=      table_cast(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__							)
       return pa_table






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      self.config.features.arrow_schema if self.config.features else None
       # dtype allows reading an int column as str
       lowercase__:   str					=      (
           {
               name: dtype.to_pandas_dtype() if not require_storage_cast(SCREAMING_SNAKE_CASE__							) else object
               for name, dtype, feature in zip(schema.names    ,			schema.types    ,			self.config.features.values()							)
           }
           if schema is not None
           else None
       )
       for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__							)							):
          lowercase__:   Any					=      pd.read_csv(SCREAMING_SNAKE_CASE__    ,			iterator=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__    ,			**self.config.pd_read_csv_kwargs							)
          try:
             for batch_idx, df in enumerate(SCREAMING_SNAKE_CASE__							):
                lowercase__:   Tuple					=      pa.Table.from_pandas(SCREAMING_SNAKE_CASE__							)
                # 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(SCREAMING_SNAKE_CASE__							)
          except ValueError as e:
             logger.error(F"""Failed to read file '{file}' with error {type(SCREAMING_SNAKE_CASE__							)}: {e}"""							)
             raise

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	Any							)  ->    int:
  torch.manual_seed(0							)
  __lowerCAmelCase											=						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 a					(      self   :	Dict							)  ->    int:
  __lowerCAmelCase											=						self.dummy_uncond_unet
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""							).images

  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       num_inference_steps=20      ,       output_type="""numpy"""      ,       return_dict=SCREAMING_SNAKE_CASE__							)[0]

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

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([1.0, 1.0, 0.0, 1.0, 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 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def a					(      self   :	Any							)  ->    Any:
  __lowerCAmelCase											=						"""google/ddpm-cifar10-32"""

  __lowerCAmelCase											=						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						PNDMScheduler()

  __lowerCAmelCase											=						PNDMPipeline(unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__							)
  pndm.to(SCREAMING_SNAKE_CASE__							)
  pndm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						torch.manual_seed(0							)
  __lowerCAmelCase											=						pndm(generator=SCREAMING_SNAKE_CASE__      ,       output_type="""numpy"""							).images

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

  assert image.shape == (1, 32, 32, 3)
  __lowerCAmelCase											=						np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]							)

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






