



"""simple docstring"""






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

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


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




@dataclass
class    __snake_case(					datasets.BuilderConfig       ):
    _A       = None
    _A       = "utf-8"
    _A       = None
    _A       = 10 << 20  # 10MB
    _A       = False
    _A       = "line"




class    __snake_case(					datasets.ArrowBasedBuilder       ):
    _A       = TextConfig




    def   A   (   self			):





      '''simple docstring'''



      return datasets.DatasetInfo(features=self.config.features			)




    def   A   (   self    ,							A_			):





      '''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}'''			)
      _SCREAMING_SNAKE_CASE        =			dl_manager.download_and_extract(self.config.data_files			)
      if isinstance(A_    ,							(str, list, tuple)			):
        _SCREAMING_SNAKE_CASE        =			data_files
        if isinstance(A_    ,							A_			):
          _SCREAMING_SNAKE_CASE        =			[files]
        _SCREAMING_SNAKE_CASE        =			[dl_manager.iter_files(A_			) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,							gen_kwargs={'''files''': files}			)]
      _SCREAMING_SNAKE_CASE        =			[]
      for split_name, files in data_files.items():
        if isinstance(A_    ,							A_			):
          _SCREAMING_SNAKE_CASE        =			[files]
        _SCREAMING_SNAKE_CASE        =			[dl_manager.iter_files(A_			) for file in files]
        splits.append(datasets.SplitGenerator(name=A_    ,							gen_kwargs={'''files''': files}			)			)
      return splits




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      if self.config.features is not None:
        _SCREAMING_SNAKE_CASE        =			self.config.features.arrow_schema
        if all(not require_storage_cast(A_			) for feature in self.config.features.values()			):
          # cheaper cast
          _SCREAMING_SNAKE_CASE        =			pa_table.cast(A_			)
        else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          _SCREAMING_SNAKE_CASE        =			table_cast(A_    ,							A_			)
        return pa_table
      else:
        return pa_table.cast(pa.schema({'''text''': pa.string()}			)			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			list(self.config.features			) if self.config.features is not None else ['''text''']
      for file_idx, file in enumerate(itertools.chain.from_iterable(A_			)			):
        # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
        with open(A_    ,							encoding=self.config.encoding    ,							errors=self.config.errors			) as f:
          if self.config.sample_by == "line":
            _SCREAMING_SNAKE_CASE        =			0
            while True:
              _SCREAMING_SNAKE_CASE        =			f.read(self.config.chunksize			)
              if not batch:
                break
              batch += f.readline()  # finish current line
              # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
              _SCREAMING_SNAKE_CASE        =			StringIO(A_			).readlines()
              if not self.config.keep_linebreaks:
                _SCREAMING_SNAKE_CASE        =			[line.rstrip('''\n'''			) for line in batch]
              _SCREAMING_SNAKE_CASE        =			pa.Table.from_arrays([pa.array(A_			)]    ,							names=A_			)
              # 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
          elif self.config.sample_by == "paragraph":
            _SCREAMING_SNAKE_CASE        =			0
            _SCREAMING_SNAKE_CASE        =			''''''
            while True:
              _SCREAMING_SNAKE_CASE        =			f.read(self.config.chunksize			)
              if not new_batch:
                break
              batch += new_batch
              batch += f.readline()  # finish current line
              _SCREAMING_SNAKE_CASE        =			batch.split('''\n\n'''			)
              _SCREAMING_SNAKE_CASE        =			pa.Table.from_arrays(
                  [pa.array([example for example in batch[:-1] if example]			)]    ,							names=A_			)
              # 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
              _SCREAMING_SNAKE_CASE        =			batch[-1]
            if batch:
              _SCREAMING_SNAKE_CASE        =			pa.Table.from_arrays([pa.array([batch]			)]    ,							names=A_			)
              yield (file_idx, batch_idx), self._cast_table(A_			)
          elif self.config.sample_by == "document":
            _SCREAMING_SNAKE_CASE        =			f.read()
            _SCREAMING_SNAKE_CASE        =			pa.Table.from_arrays([pa.array([text]			)]    ,							names=A_			)
            yield file_idx, self._cast_table(A_			)





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

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

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

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

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
