



"""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


__SCREAMING_SNAKE_CASE     :  str										=					datasets.utils.logging.get_logger(__name__)


@dataclass
class 							lowerCamelCase_(      datasets.BuilderConfig			):





					'''simple docstring'''



					lowercase__      :				Union[str, Any]        =       None
					lowercase__      :				Dict        =       'utf-8'
					lowercase__      :				Optional[int]        =       None
					lowercase__      :				Optional[Any]        =       10 << 20  # 10MB
					lowercase__      :				int        =       False
					lowercase__      :				List[Any]        =       'line'


class 							lowerCamelCase_(      datasets.ArrowBasedBuilder			):





					'''simple docstring'''



					lowercase__      :				int        =       TextConfig

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

					def 	snake_case__					(			self ,				lowerCamelCase__      ):






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

					def 	snake_case__					(			self ,				lowerCamelCase__      ):






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





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = 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":
																					_lowerCamelCase					     = 0
																					while True:
																									_lowerCamelCase					     = 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)
																									_lowerCamelCase					     = StringIO(lowercase__      ).readlines()
																									if not self.config.keep_linebreaks:
																													_lowerCamelCase					     = [line.rstrip('''\n'''      ) for line in batch]
																									_lowerCamelCase					     = 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":
																					_lowerCamelCase					     = 0
																					_lowerCamelCase					     = ""
																					while True:
																									_lowerCamelCase					     = f.read(self.config.chunksize      )
																									if not new_batch:
																													break
																									batch += new_batch
																									batch += f.readline()  # finish current line
																									_lowerCamelCase					     = batch.split('''\n\n'''      )
																									_lowerCamelCase					     = 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
																									_lowerCamelCase					     = batch[-1]
																					if batch:
																									_lowerCamelCase					     = pa.Table.from_arrays([pa.array([batch]      )] ,				names=lowercase__      )
																									yield (file_idx, batch_idx), self._cast_table(lowercase__      )
																	elif self.config.sample_by == "document":
																					_lowerCamelCase					     = f.read()
																					_lowerCamelCase					     = pa.Table.from_arrays([pa.array([text]      )] ,				names=lowercase__      )
																					yield file_idx, self._cast_table(lowercase__      )



'''simple docstring'''






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(HeunDiscreteScheduler,)
    _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", "exp"]:
                self.check_over_configs(beta_schedule=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for clip_sample_range in [1.0, 2.0, 3.0]:
                self.check_over_configs(clip_sample_range=lowercase__				,     clip_sample=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

          scheduler.set_timesteps(self.num_inference_steps			)

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

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

                SCREAMING_SNAKE_CASE_      :			Dict     =       model(lowercase__				,     lowercase__			)

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

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

          if torch_device in ["cpu", "mps"]:
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3
          else:
                # CUDA
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

          scheduler.set_timesteps(self.num_inference_steps			)

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

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

                SCREAMING_SNAKE_CASE_      :			int     =       model(lowercase__				,     lowercase__			)

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

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

          if torch_device in ["cpu", "mps"]:
                assert abs(result_sum.item() - 4.69_34e-07			) < 1e-2
                assert abs(result_mean.item() - 6.11_12e-10			) < 1e-3
          else:
                # CUDA
                assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

          scheduler.set_timesteps(self.num_inference_steps				,     device=lowercase__			)

          SCREAMING_SNAKE_CASE_      :			Any     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			str     =       self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       model(lowercase__				,     lowercase__			)

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

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

          if str(lowercase__			).startswith("cpu"			):
                # The following sum varies between 148 and 156 on mps. Why?
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3
          elif str(lowercase__			).startswith("mps"			):
                # Larger tolerance on mps
                assert abs(result_mean.item() - 0.0002			) < 1e-2
          else:
                # CUDA
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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

          scheduler.set_timesteps(self.num_inference_steps				,     device=lowercase__			)

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

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

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

                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       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() - 0.00015			) < 1e-2
          assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07			) < 1e-2

