
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.getLogger(__name__)





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     42





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "train"
	snake_case_					    =     "dev"
	snake_case_					    =     "test"





class 							UpperCAmelCase      :




	'''simple docstring'''





	@staticmethod
	def 				UpperCamelCase_  (       A					: Optional[int]							,A					: Union[Split, str] ):
					raise NotImplementedError






	@staticmethod
	def 				UpperCamelCase_  (       A					: str ):
					raise NotImplementedError







	@staticmethod
	def 				UpperCamelCase_  (       A					: List[InputExample]							,A					: List[str]							,A					: int							,A					: PreTrainedTokenizer							,A					: Tuple=False							,A					: Optional[int]="[CLS]"							,A					: Union[str, Any]=1							,A					: Optional[int]="[SEP]"							,A					: List[Any]=False							,A					: str=False							,A					: List[str]=0							,A					: int=0							,A					: Tuple=-1_00							,A					: str=0							,A					: Any=True							,):
					__A   			= {label: i for i, label in enumerate(A )}

					__A   			= []
					for ex_index, example in enumerate(A ):
									if ex_index % 1_00_00 == 0:
													logger.info("Writing example %d of %d"							,A							,len(A ) )

									__A   			= []
									__A   			= []
									for word, label in zip(example.words							,example.labels ):
													__A   			= tokenizer.tokenize(A )

													# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
													if len(A ) > 0:
																	tokens.extend(A )
																	# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																	label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A ) - 1) )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
									__A   			= tokenizer.num_special_tokens_to_add()
									if len(A ) > max_seq_length - special_tokens_count:
													__A   			= tokens[: (max_seq_length - special_tokens_count)]
													__A   			= label_ids[: (max_seq_length - special_tokens_count)]

									# The convention in BERT is:
									# (a) For sequence pairs:
									#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
									#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
									# (b) For single sequences:
									#  tokens:   [CLS] the dog is hairy . [SEP]
									#  type_ids:   0   0   0   0  0     0   0
									#
									# Where "type_ids" are used to indicate whether this is the first
									# sequence or the second sequence. The embedding vectors for `type=0` and
									# `type=1` were learned during pre-training and are added to the wordpiece
									# embedding vector (and position vector). This is not *strictly* necessary
									# since the [SEP] token unambiguously separates the sequences, but it makes
									# it easier for the model to learn the concept of sequences.
									#
									# For classification tasks, the first vector (corresponding to [CLS]) is
									# used as the "sentence vector". Note that this only makes sense because
									# the entire model is fine-tuned.
									tokens += [sep_token]
									label_ids += [pad_token_label_id]
									if sep_token_extra:
													# roberta uses an extra separator b/w pairs of sentences
													tokens += [sep_token]
													label_ids += [pad_token_label_id]
									__A   			= [sequence_a_segment_id] * len(A )

									if cls_token_at_end:
													tokens += [cls_token]
													label_ids += [pad_token_label_id]
													segment_ids += [cls_token_segment_id]
									else:
													__A   			= [cls_token] + tokens
													__A   			= [pad_token_label_id] + label_ids
													__A   			= [cls_token_segment_id] + segment_ids

									__A   			= tokenizer.convert_tokens_to_ids(A )

									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									__A   			= [1 if mask_padding_with_zero else 0] * len(A )

									# Zero-pad up to the sequence length.
									__A   			= max_seq_length - len(A )
									if pad_on_left:
													__A   			= ([pad_token] * padding_length) + input_ids
													__A   			= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
													__A   			= ([pad_token_segment_id] * padding_length) + segment_ids
													__A   			= ([pad_token_label_id] * padding_length) + label_ids
									else:
													input_ids += [pad_token] * padding_length
													input_mask += [0 if mask_padding_with_zero else 1] * padding_length
													segment_ids += [pad_token_segment_id] * padding_length
													label_ids += [pad_token_label_id] * padding_length

									assert len(A ) == max_seq_length
									assert len(A ) == max_seq_length
									assert len(A ) == max_seq_length
									assert len(A ) == max_seq_length

									if ex_index < 5:
													logger.info("*** Example ***" )
													logger.info("guid: %s"							,example.guid )
													logger.info("tokens: %s"							," ".join([str(A ) for x in tokens] ) )
													logger.info("input_ids: %s"							," ".join([str(A ) for x in input_ids] ) )
													logger.info("input_mask: %s"							," ".join([str(A ) for x in input_mask] ) )
													logger.info("segment_ids: %s"							," ".join([str(A ) for x in segment_ids] ) )
													logger.info("label_ids: %s"							," ".join([str(A ) for x in label_ids] ) )

									if "token_type_ids" not in tokenizer.model_input_names:
													__A   			= None

									features.append(
									    InputFeatures(
									        input_ids=A							,attention_mask=A							,token_type_ids=A							,label_ids=A ) )
					return features


if is_torch_available():
					import torch
					from torch import nn
					from torch.utils.data import Dataset





					class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




						'''simple docstring'''





						snake_case_					    =     42
						snake_case_					    =     nn.CrossEntropyLoss().ignore_index






						def __init__(       self					: Optional[Any]							,A					: TokenClassificationTask							,A					: str							,A					: PreTrainedTokenizer							,A					: List[str]							,A					: str							,A					: Optional[int] = None							,A					: int=False							,A					: Split = Split.train							,):
										# Load data features from cache or dataset file
										__A   			= os.path.join(
										    A							,"cached_{}_{}_{}".format(mode.value							,tokenizer.__class__.__name__							,str(A ) )							,)

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										__A   			= cached_features_file + ".lock"
										with FileLock(A ):
														if os.path.exists(A ) and not overwrite_cache:
																		logger.info(f'''Loading features from cached file {cached_features_file}''' )
																		__A   			= torch.load(A )
														else:
																		logger.info(f'''Creating features from dataset file at {data_dir}''' )
																		__A   			= token_classification_task.read_examples_from_file(A							,A )
																		# TODO clean up all this to leverage built-in features of tokenizers
																		__A   			= token_classification_task.convert_examples_to_features(
																		    A							,A							,A							,A							,cls_token_at_end=bool(model_type in ["xlnet"] )							,cls_token=tokenizer.cls_token							,cls_token_segment_id=2 if model_type in ["xlnet"] else 0							,sep_token=tokenizer.sep_token							,sep_token_extra=A							,pad_on_left=bool(tokenizer.padding_side == "left" )							,pad_token=tokenizer.pad_token_id							,pad_token_segment_id=tokenizer.pad_token_type_id							,pad_token_label_id=self.pad_token_label_id							,)
																		logger.info(f'''Saving features into cached file {cached_features_file}''' )
																		torch.save(self.features							,A )






						def __len__(       self					: str ):
										return len(self.features )







						def __getitem__(       self					: Optional[Any]							,A					: Union[str, Any] ):
										return self.features[i]


if is_tf_available():
					import tensorflow as tf





					class 							UpperCAmelCase      :




						'''simple docstring'''





						snake_case_					    =     42
						snake_case_					    =     -100






						def __init__(       self					: Optional[Any]							,A					: TokenClassificationTask							,A					: str							,A					: PreTrainedTokenizer							,A					: List[str]							,A					: str							,A					: Optional[int] = None							,A					: List[Any]=False							,A					: Split = Split.train							,):
										__A   			= token_classification_task.read_examples_from_file(A							,A )
										# TODO clean up all this to leverage built-in features of tokenizers
										__A   			= token_classification_task.convert_examples_to_features(
										    A							,A							,A							,A							,cls_token_at_end=bool(model_type in ["xlnet"] )							,cls_token=tokenizer.cls_token							,cls_token_segment_id=2 if model_type in ["xlnet"] else 0							,sep_token=tokenizer.sep_token							,sep_token_extra=A							,pad_on_left=bool(tokenizer.padding_side == "left" )							,pad_token=tokenizer.pad_token_id							,pad_token_segment_id=tokenizer.pad_token_type_id							,pad_token_label_id=self.pad_token_label_id							,)

										def gen():
														for ex in self.features:
																		if ex.token_type_ids is None:
																						yield (
																						    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																						    ex.label_ids,
																						)
																		else:
																						yield (
																						    {
																						        "input_ids": ex.input_ids,
																						        "attention_mask": ex.attention_mask,
																						        "token_type_ids": ex.token_type_ids,
																						    },
																						    ex.label_ids,
																						)

										if "token_type_ids" not in tokenizer.model_input_names:
														__A   			= tf.data.Dataset.from_generator(
														    A							,({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)							,(
														        {"input_ids": tf.TensorShape([None] ), "attention_mask": tf.TensorShape([None] )},
														        tf.TensorShape([None] ),
														    )							,)
										else:
														__A   			= tf.data.Dataset.from_generator(
														    A							,({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)							,(
														        {
														            "input_ids": tf.TensorShape([None] ),
														            "attention_mask": tf.TensorShape([None] ),
														            "token_type_ids": tf.TensorShape([None] ),
														        },
														        tf.TensorShape([None] ),
														    )							,)






						def 				UpperCamelCase_  (       self					: int ):
										__A   			= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

										return self.dataset






						def __len__(       self					: Any ):
										return len(self.features )







						def __getitem__(       self					: List[Any]							,A					: Optional[Any] ):
										return self.features[i]


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
					SCREAMING_SNAKE_CASE							:Any							 =				{
					    'linear': PIL.Image.Resampling.BILINEAR,
					    'bilinear': PIL.Image.Resampling.BILINEAR,
					    'bicubic': PIL.Image.Resampling.BICUBIC,
					    'lanczos': PIL.Image.Resampling.LANCZOS,
					    'nearest': PIL.Image.Resampling.NEAREST,
					}
else:
					SCREAMING_SNAKE_CASE							:int							 =				{
					    'linear': PIL.Image.LINEAR,
					    'bilinear': PIL.Image.BILINEAR,
					    'bicubic': PIL.Image.BICUBIC,
					    'lanczos': PIL.Image.LANCZOS,
					    'nearest': PIL.Image.NEAREST,
					}


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= (images / 2 + 0.5).clamp(0		,   1						)
				__A   			= images.cpu().permute(0		,   2		,   3		,   1						).float().numpy()
				__A   			= numpy_to_pil(a_						)
				return images


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if images.ndim == 3:
								__A   			= images[None, ...]
				__A   			= (images * 2_5_5).round().astype("uint8"						)
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								__A   			= [Image.fromarray(image.squeeze()		,   mode="L"						) for image in images]
				else:
								__A   			= [Image.fromarray(a_						) for image in images]

				return pil_images
