




'''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


_snake_case : Optional[int]  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							42



@dataclass
class      A       :
							lowercase_							     =							42
							lowercase_							     =							42
							lowercase_							     =							None
							lowercase_							     =							None



class      A       (						_a       ):
							lowercase_							     =							'train'
							lowercase_							     =							'dev'
							lowercase_							     =							'test'



class      A       :







							@staticmethod
							def   __lowerCAmelCase     (    lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Union[Split, str]   )     ->				List[InputExample]:


														"""simple docstring"""


														raise NotImplementedError







							@staticmethod
							def   __lowerCAmelCase     (    lowerCAmelCase_			:    str   )     ->				List[str]:


														"""simple docstring"""


														raise NotImplementedError







							@staticmethod
							def   __lowerCAmelCase     (    lowerCAmelCase_			:    List[InputExample]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    PreTrainedTokenizer				,     lowerCAmelCase_			:    str=False				,     lowerCAmelCase_			:    Dict="[CLS]"				,     lowerCAmelCase_			:    Optional[int]=1				,     lowerCAmelCase_			:    Union[str, Any]="[SEP]"				,     lowerCAmelCase_			:    Optional[int]=False				,     lowerCAmelCase_			:    str=False				,     lowerCAmelCase_			:    Optional[Any]=0				,     lowerCAmelCase_			:    Tuple=0				,     lowerCAmelCase_			:    Dict=-1_00				,     lowerCAmelCase_			:    List[str]=0				,     lowerCAmelCase_			:    str=True				,     )     ->				List[InputFeatures]:


														"""simple docstring"""


														_a      	=							{label: i for i, label in enumerate(lowerCAmelCase_   )}

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

																					_a      	=							[]
																					_a      	=							[]
																					for word, label in zip(example.words				,     example.labels   ):
																												_a      	=							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.
																					_a      	=							tokenizer.num_special_tokens_to_add()
																					if len(lowerCAmelCase_   ) > 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(lowerCAmelCase_   )

																					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(lowerCAmelCase_   )

																					# 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(lowerCAmelCase_   )

																					# Zero-pad up to the sequence length.
																					_a      	=							max_seq_length - len(lowerCAmelCase_   )
																					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(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:
																												_a      	=							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      A       (						_a       ):
											lowercase_							     =							42
											lowercase_							     =							nn.CrossEntropyLoss().ignore_index







											def __init__(    self			:    Dict				,     lowerCAmelCase_			:    TokenClassificationTask				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    PreTrainedTokenizer				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[int] = None				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Split = Split.train				,     )     ->				List[str]:


																		"""simple docstring"""


																		_a      	=							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.
																		_a      	=							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}'   )
																																_a      	=							torch.load(lowerCAmelCase_   )
																									else:
																																logger.info(F'Creating features from dataset file at {data_dir}'   )
																																_a      	=							token_classification_task.read_examples_from_file(lowerCAmelCase_				,     lowerCAmelCase_   )
																																# TODO clean up all this to leverage built-in features of tokenizers
																																_a      	=							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			:    Any   )     ->				int:


																		"""simple docstring"""


																		return len(self.features   )







											def __getitem__(    self			:    Dict				,     lowerCAmelCase_			:    Optional[int]   )     ->				InputFeatures:


																		"""simple docstring"""


																		return self.features[i]


if is_tf_available():
				import tensorflow as tf

				class      A       :
											lowercase_							     =							42
											lowercase_							     =							-100







											def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    TokenClassificationTask				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    PreTrainedTokenizer				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[int] = None				,     lowerCAmelCase_			:    List[str]=False				,     lowerCAmelCase_			:    Split = Split.train				,     )     ->				str:


																		"""simple docstring"""


																		_a      	=							token_classification_task.read_examples_from_file(lowerCAmelCase_				,     lowerCAmelCase_   )
																		# TODO clean up all this to leverage built-in features of tokenizers
																		_a      	=							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:
																									_a      	=							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:
																									_a      	=							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   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[Any]:


																		"""simple docstring"""


																		_a      	=							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

																		return self.dataset







											def __len__(    self			:    Tuple   )     ->				Tuple:


																		"""simple docstring"""


																		return len(self.features   )







											def __getitem__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Tuple   )     ->				InputFeatures:


																		"""simple docstring"""


																		return self.features[i]







'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

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







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]   )

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







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


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_dpmpp_2m'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=7.5				,     num_inference_steps=15				,     output_type='''np'''				,     use_karras_sigmas=lowerCAmelCase_				,     )

														_a      	=							output.images

														_a      	=							image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array(
														    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]   )

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

