


"""simple docstring"""






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


UpperCamelCase__				:List[Any]			   =	logging.getLogger(__name__)






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__=-1   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	label_idx
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[InputExample]:

					"""simple docstring"""



					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:List[str]					     =	mode.value
					_UpperCamelCase					:int					     =	os.path.join(SCREAMING_SNAKE_CASE__					,				f"{mode}.txt"   )
					_UpperCamelCase					:List[str]					     =	1
					_UpperCamelCase					:Union[str, Any]					     =	[]
					with open(SCREAMING_SNAKE_CASE__					,				encoding='''utf-8'''   ) as f:
								_UpperCamelCase					:int					     =	[]
								_UpperCamelCase					:List[Any]					     =	[]
								for line in f:
											if line.startswith('''-DOCSTART-'''   ) or line == "" or line == "\n":
														if words:
																	examples.append(InputExample(guid=f"{mode}-{guid_index}"					,				words=SCREAMING_SNAKE_CASE__					,				labels=SCREAMING_SNAKE_CASE__   )   )
																	guid_index += 1
																	_UpperCamelCase					:int					     =	[]
																	_UpperCamelCase					:str					     =	[]
											else:
														_UpperCamelCase					:Optional[int]					     =	line.split(''' '''   )
														words.append(splits[0]   )
														if len(SCREAMING_SNAKE_CASE__   ) > 1:
																	labels.append(splits[self.label_idx].replace('''\n'''					,				''''''   )   )
														else:
																	# Examples could have no label for mode = "test"
																	labels.append('''O'''   )
								if words:
											examples.append(InputExample(guid=f"{mode}-{guid_index}"					,				words=SCREAMING_SNAKE_CASE__					,				labels=SCREAMING_SNAKE_CASE__   )   )
					return examples
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	0
					for line in test_input_reader:
								if line.startswith('''-DOCSTART-'''   ) or line == "" or line == "\n":
											writer.write(SCREAMING_SNAKE_CASE__   )
											if not preds_list[example_id]:
														example_id += 1
								elif preds_list[example_id]:
											_UpperCamelCase					:Optional[Any]					     =	line.split()[0] + ''' ''' + preds_list[example_id].pop(0   ) + '''\n'''
											writer.write(SCREAMING_SNAKE_CASE__   )
								else:
											logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''					,				line.split()[0]   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					if path:
								with open(SCREAMING_SNAKE_CASE__					,				'''r'''   ) as f:
											_UpperCamelCase					:Any					     =	f.read().splitlines()
								if "O" not in labels:
											_UpperCamelCase					:Optional[Any]					     =	['''O'''] + labels
								return labels
					else:
								return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__(label_idx=-2   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					if path:
								with open(SCREAMING_SNAKE_CASE__					,				'''r'''   ) as f:
											_UpperCamelCase					:Optional[int]					     =	f.read().splitlines()
								if "O" not in labels:
											_UpperCamelCase					:Dict					     =	['''O'''] + labels
								return labels
					else:
								return [
								    "O",
								    "B-ADVP",
								    "B-INTJ",
								    "B-LST",
								    "B-PRT",
								    "B-NP",
								    "B-SBAR",
								    "B-VP",
								    "B-ADJP",
								    "B-CONJP",
								    "B-PP",
								    "I-ADVP",
								    "I-INTJ",
								    "I-LST",
								    "I-PRT",
								    "I-NP",
								    "I-SBAR",
								    "I-VP",
								    "I-ADJP",
								    "I-CONJP",
								    "I-PP",
								]






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	List[InputExample]:

					"""simple docstring"""



					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[Any]					     =	mode.value
					_UpperCamelCase					:List[str]					     =	os.path.join(SCREAMING_SNAKE_CASE__					,				f"{mode}.txt"   )
					_UpperCamelCase					:Optional[int]					     =	1
					_UpperCamelCase					:str					     =	[]

					with open(SCREAMING_SNAKE_CASE__					,				encoding='''utf-8'''   ) as f:
								for sentence in parse_incr(SCREAMING_SNAKE_CASE__   ):
											_UpperCamelCase					:int					     =	[]
											_UpperCamelCase					:Tuple					     =	[]
											for token in sentence:
														words.append(token['''form''']   )
														labels.append(token['''upos''']   )
											assert len(SCREAMING_SNAKE_CASE__   ) == len(SCREAMING_SNAKE_CASE__   )
											if words:
														examples.append(InputExample(guid=f"{mode}-{guid_index}"					,				words=SCREAMING_SNAKE_CASE__					,				labels=SCREAMING_SNAKE_CASE__   )   )
														guid_index += 1
					return examples
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	0
					for sentence in parse_incr(SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:List[Any]					     =	preds_list[example_id]
								_UpperCamelCase					:Optional[Any]					     =	''''''
								for token in sentence:
											out += f"{token['form']} ({token['upos']}|{s_p.pop(0   )}) "
								out += "\n"
								writer.write(SCREAMING_SNAKE_CASE__   )
								example_id += 1





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					if path:
								with open(SCREAMING_SNAKE_CASE__					,				'''r'''   ) as f:
											return f.read().splitlines()
					else:
								return [
								    "ADJ",
								    "ADP",
								    "ADV",
								    "AUX",
								    "CCONJ",
								    "DET",
								    "INTJ",
								    "NOUN",
								    "NUM",
								    "PART",
								    "PRON",
								    "PROPN",
								    "PUNCT",
								    "SCONJ",
								    "SYM",
								    "VERB",
								    "X",
								]





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_UpperCamelCase					:int					     =	DDIMScheduler.from_config(scheduler.config   )

					self.register_modules(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
								raise ValueError(
								    f"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch"
								    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=self.unet.dtype   )

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

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

					_UpperCamelCase					:str					     =	(image / 2 + 0.5).clamp(0					,				1   )
					_UpperCamelCase					:int					     =	image.cpu().permute(0					,				2					,				3					,				1   ).numpy()
					if output_type == "pil":
								_UpperCamelCase					:Union[str, Any]					     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

