



"""simple docstring"""






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


_lowercase			:			str										=						logging.getLogger(__name__)

@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							42
					_a             =							42
					_a             =							42





@dataclass
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							42
					_a             =							42
					_a             =							None
					_a             =							None





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'train'
					_a             =							'dev'
					_a             =							'test'





class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					@staticmethod
					def 		snake_case	(	lowerCamelCase							:  List[str],  lowerCamelCase							:  Union[Split, str]					)-> List[InputExample]:
								raise NotImplementedError



					@staticmethod
					def 		snake_case	(	lowerCamelCase							:  str					)-> List[str]:
								raise NotImplementedError




					@staticmethod
					def 		snake_case	(	lowerCamelCase							:  List[InputExample],  lowerCamelCase							:  List[str],  lowerCamelCase							:  int,  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  Optional[int]=False,  lowerCamelCase							:  Tuple="[CLS]",  lowerCamelCase							:  str=1,  lowerCamelCase							:  str="[SEP]",  lowerCamelCase							:  List[Any]=False,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Optional[int]=0,  lowerCamelCase							:  Tuple=0,  lowerCamelCase							:  Any=-100,  lowerCamelCase							:  Any=0,  lowerCamelCase							:  Tuple=True,  )-> List[InputFeatures]:
								lowerCamelCase__		:		Tuple		     ={label: i for i, label in enumerate(lowerCamelCase					)}

								lowerCamelCase__		:		List[str]		     =[]
								for ex_index, example in enumerate(lowerCamelCase					):
											if ex_index % 1_0000 == 0:
														logger.info('''Writing example %d of %d''',  lowerCamelCase,  len(lowerCamelCase					)					)

											lowerCamelCase__		:		Union[str, Any]		     =[]
											lowerCamelCase__		:		Tuple		     =[]
											for word, label in zip(example.words,  example.labels					):
														lowerCamelCase__		:		Tuple		     =tokenizer.tokenize(lowerCamelCase					)

														# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
														if len(lowerCamelCase					) > 0:
																	tokens.extend(lowerCamelCase					)
																	# 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(lowerCamelCase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											lowerCamelCase__		:		Optional[int]		     =tokenizer.num_special_tokens_to_add()
											if len(lowerCamelCase					) > max_seq_length - special_tokens_count:
														lowerCamelCase__		:		Dict		     =tokens[: (max_seq_length - special_tokens_count)]
														lowerCamelCase__		:		Optional[Any]		     =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]
											lowerCamelCase__		:		Union[str, Any]		     =[sequence_a_segment_id] * len(lowerCamelCase					)

											if cls_token_at_end:
														tokens += [cls_token]
														label_ids += [pad_token_label_id]
														segment_ids += [cls_token_segment_id]
											else:
														lowerCamelCase__		:		Any		     =[cls_token] + tokens
														lowerCamelCase__		:		List[str]		     =[pad_token_label_id] + label_ids
														lowerCamelCase__		:		Any		     =[cls_token_segment_id] + segment_ids

											lowerCamelCase__		:		Any		     =tokenizer.convert_tokens_to_ids(lowerCamelCase					)

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											lowerCamelCase__		:		Union[str, Any]		     =[1 if mask_padding_with_zero else 0] * len(lowerCamelCase					)

											# Zero-pad up to the sequence length.
											lowerCamelCase__		:		Tuple		     =max_seq_length - len(lowerCamelCase					)
											if pad_on_left:
														lowerCamelCase__		:		Optional[Any]		     =([pad_token] * padding_length) + input_ids
														lowerCamelCase__		:		Tuple		     =([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
														lowerCamelCase__		:		str		     =([pad_token_segment_id] * padding_length) + segment_ids
														lowerCamelCase__		:		Optional[Any]		     =([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(lowerCamelCase					) == max_seq_length
											assert len(lowerCamelCase					) == max_seq_length
											assert len(lowerCamelCase					) == max_seq_length
											assert len(lowerCamelCase					) == max_seq_length

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

											if "token_type_ids" not in tokenizer.model_input_names:
														lowerCamelCase__		:		Tuple		     =None

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


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

		class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


							'''simple docstring'''





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



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

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										lowerCamelCase__		:		int		     =cached_features_file + '''.lock'''
										with FileLock(lowerCamelCase					):
													if os.path.exists(lowerCamelCase					) and not overwrite_cache:
																logger.info(F'''Loading features from cached file {cached_features_file}'''					)
																lowerCamelCase__		:		Dict		     =torch.load(lowerCamelCase					)
													else:
																logger.info(F'''Creating features from dataset file at {data_dir}'''					)
																lowerCamelCase__		:		Dict		     =token_classification_task.read_examples_from_file(lowerCamelCase,  lowerCamelCase					)
																# TODO clean up all this to leverage built-in features of tokenizers
																lowerCamelCase__		:		Optional[Any]		     =token_classification_task.convert_examples_to_features(
																    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  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=lowerCamelCase,  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,  lowerCamelCase					)



							def __len__(	self							:  Tuple					)-> Tuple:
										return len(self.features					)




							def __getitem__(	self							:  Optional[int],  lowerCamelCase							:  Optional[Any]					)-> InputFeatures:
										return self.features[i]


if is_tf_available():
		import tensorflow as tf

		class 	__SCREAMING_SNAKE_CASE     :


							'''simple docstring'''





							_a             =							42
							_a             =							-1_0_0



							def __init__(	self							:  List[Any],  lowerCamelCase							:  TokenClassificationTask,  lowerCamelCase							:  str,  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  List[Any]=False,  lowerCamelCase							:  Split = Split.train,  )-> Any:
										lowerCamelCase__		:		Union[str, Any]		     =token_classification_task.read_examples_from_file(lowerCamelCase,  lowerCamelCase					)
										# TODO clean up all this to leverage built-in features of tokenizers
										lowerCamelCase__		:		List[str]		     =token_classification_task.convert_examples_to_features(
										    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  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=lowerCamelCase,  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:
													lowerCamelCase__		:		Union[str, Any]		     =tf.data.Dataset.from_generator(
													    lowerCamelCase,  ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa),  (
													        {'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)},
													        tf.TensorShape([None]					),
													    ),  )
										else:
													lowerCamelCase__		:		int		     =tf.data.Dataset.from_generator(
													    lowerCamelCase,  ({'''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 		snake_case	(	self							:  int					)-> Dict:
										lowerCamelCase__		:		List[Any]		     =self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

										return self.dataset



							def __len__(	self							:  Dict					)-> Union[str, Any]:
										return len(self.features					)




							def __getitem__(	self							:  Optional[int],  lowerCamelCase							:  Optional[int]					)-> InputFeatures:
										return self.features[i]









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





@slow
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

								lowerCamelCase__		:		Dict		     =image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2




