

"""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


__A     :    Union[str, Any]    =		datasets.utils.logging.get_logger(__name__)





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





class 			__UpperCamelCase (   datasets.ArrowBasedBuilder						):
		SCREAMING_SNAKE_CASE								=    TextConfig


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							return datasets.DatasetInfo(features=self.config.features)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		List[str]):
							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			=					dl_manager.download_and_extract(self.config.data_files)
							if isinstance(__SCREAMING_SNAKE_CASE     ,     (str, list, tuple)):
												A			=					data_files
												if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	A			=					[files]
												A			=					[dl_manager.iter_files(__SCREAMING_SNAKE_CASE) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,     gen_kwargs={"files": files})]
							A			=					[]
							for split_name, files in data_files.items():
												if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	A			=					[files]
												A			=					[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 					SCREAMING_SNAKE_CASE__   (self :		Dict     ,     __SCREAMING_SNAKE_CASE :		pa.Table):
							if self.config.features is not None:
												A			=					self.config.features.arrow_schema
												if all(not require_storage_cast(__SCREAMING_SNAKE_CASE) for feature in self.config.features.values()):
																	# cheaper cast
																	A			=					pa_table.cast(__SCREAMING_SNAKE_CASE)
												else:
																	# more expensive cast; allows str <-> int/float or str to Audio for example
																	A			=					table_cast(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
												return pa_table
							else:
												return pa_table.cast(pa.schema({"text": pa.string()}))


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







'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       _a (_lowerCamelCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__					)			->	Union[str, Any]:
          super().__init__()
          self.register_modules(unet=A__				,							scheduler=A__					)






       @torch.no_grad()
       def __call__(							self				,							A__ = 1				,							A__ = None				,							A__ = 10_00				,							A__ = "pil"				,							A__ = True				,							)			->	Union[ImagePipelineOutput, Tuple]:
          # Sample gaussian noise to begin loop
          if isinstance(self.unet.config.sample_size				,							A__					):
             _SCREAMING_SNAKE_CASE          = (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size,
                 self.unet.config.sample_size,
             )
          else:
             _SCREAMING_SNAKE_CASE          = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__					)
             _SCREAMING_SNAKE_CASE          = image.to(self.device					)
          else:
             _SCREAMING_SNAKE_CASE          = randn_tensor(A__				,							generator=A__				,							device=self.device					)

          # set step values
          self.scheduler.set_timesteps(A__					)

          for t in self.progress_bar(self.scheduler.timesteps					):
             # 1. predict noise model_output
             _SCREAMING_SNAKE_CASE          = self.unet(A__				,							A__					).sample

             # 2. compute previous image: x_t -> x_t-1
             _SCREAMING_SNAKE_CASE          = self.scheduler.step(A__				,							A__				,							A__				,							generator=A__					).prev_sample

          _SCREAMING_SNAKE_CASE          = (image / 2 + 0.5).clamp(0				,							1					)
          _SCREAMING_SNAKE_CASE          = image.cpu().permute(0				,							2				,							3				,							1					).numpy()
          if output_type == "pil":
             _SCREAMING_SNAKE_CASE          = self.numpy_to_pil(A__					)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=A__					)






