

'''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


snake_case_       =       datasets.utils.logging.get_logger(__name__)
@dataclass
class 	SCREAMING_SNAKE_CASE__			(				datasets.BuilderConfig      ):
    _A			    =							None
    _A			    =							"utf-8"
    _A			    =							None
    _A			    =							10 << 20  # 10MB
    _A			    =							False
    _A			    =							"line"






class 	SCREAMING_SNAKE_CASE__			(				datasets.ArrowBasedBuilder      ):
    _A			    =							TextConfig




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     lowercase__			):






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




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






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







    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






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




'''simple docstring'''






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(EulerAncestralDiscreteScheduler,)
    _A			    =							10




    def   __lowerCamelCase      (					self				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {
              "num_train_timesteps": 1100,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowercase__			)
          return config




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for timesteps in [10, 50, 100, 1000]:
                self.check_over_configs(num_train_timesteps=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,     [0.0002, 0.002, 0.02]			):
                self.check_over_configs(beta_start=lowercase__				,     beta_end=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Any     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			int     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[str]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.get_scheduler_config(prediction_type="v_prediction"			)
          SCREAMING_SNAKE_CASE_      :			str     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			List[str]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[Any]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 108.4439			) < 1e-2
          assert abs(result_mean.item() - 0.1412			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps				,     device=lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[str]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3

