






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


__UpperCamelCase    :						Tuple		     =  logging.getLogger(__name__)


@dataclass
class      __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase       			=				42
							_UpperCAmelCase       			=				42
							_UpperCAmelCase       			=				42


@dataclass
class      __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase       			=				42
							_UpperCAmelCase       			=				42
							_UpperCAmelCase       			=				None
							_UpperCAmelCase       			=				None


class      __SCREAMING_SNAKE_CASE(		lowercase__		):
							_UpperCAmelCase       			=				'''train'''
							_UpperCAmelCase       			=				'''dev'''
							_UpperCAmelCase       			=				'''test'''


class      __SCREAMING_SNAKE_CASE:
							@staticmethod
							def lowerCAmelCase_       (   UpperCamelCase:					int				,		UpperCamelCase:					Union[Split, str]  )						->						str:
											raise NotImplementedError
							@staticmethod
							def lowerCAmelCase_       (   UpperCamelCase:					str  )						->						Any:
											raise NotImplementedError


							@staticmethod
							def lowerCAmelCase_       (   UpperCamelCase:					List[InputExample]				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					int				,		UpperCamelCase:					PreTrainedTokenizer				,		UpperCamelCase:					List[str]=False				,		UpperCamelCase:					Tuple="[CLS]"				,		UpperCamelCase:					Optional[int]=1				,		UpperCamelCase:					List[Any]="[SEP]"				,		UpperCamelCase:					List[str]=False				,		UpperCamelCase:					Optional[int]=False				,		UpperCamelCase:					Optional[Any]=0				,		UpperCamelCase:					List[str]=0				,		UpperCamelCase:					Tuple=-1_00				,		UpperCamelCase:					Optional[int]=0				,		UpperCamelCase:					int=True				,		)						->						Any:
											snake_case__													=   {label: i for i, label in enumerate(_a  )}

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

															snake_case__													=   []
															snake_case__													=   []
															for word, label in zip(example.words				,		example.labels  ):
																			snake_case__													=   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.
															snake_case__													=   tokenizer.num_special_tokens_to_add()
															if len(_a  ) > max_seq_length - special_tokens_count:
																			snake_case__													=   tokens[: (max_seq_length - special_tokens_count)]
																			snake_case__													=   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]
															snake_case__													=   [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:
																			snake_case__													=   [cls_token] + tokens
																			snake_case__													=   [pad_token_label_id] + label_ids
																			snake_case__													=   [cls_token_segment_id] + segment_ids

															snake_case__													=   tokenizer.convert_tokens_to_ids(_a  )

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

															# Zero-pad up to the sequence length.
															snake_case__													=   max_seq_length - len(_a  )
															if pad_on_left:
																			snake_case__													=   ([pad_token] * padding_length) + input_ids
																			snake_case__													=   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																			snake_case__													=   ([pad_token_segment_id] * padding_length) + segment_ids
																			snake_case__													=   ([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:
																			snake_case__													=   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      __SCREAMING_SNAKE_CASE(		lowercase__		):
														_UpperCAmelCase       			=				42
														_UpperCAmelCase       			=				nn.CrossEntropyLoss().ignore_index
														def __init__(   self:					int				,		UpperCamelCase:					TokenClassificationTask				,		UpperCamelCase:					str				,		UpperCamelCase:					PreTrainedTokenizer				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					str				,		UpperCamelCase:					Optional[int] = None				,		UpperCamelCase:					Any=False				,		UpperCamelCase:					Split = Split.train				,		)						->						Optional[Any]:
																		snake_case__													=   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.
																		snake_case__													=   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}'''  )
																										snake_case__													=   torch.load(_a  )
																						else:
																										logger.info(F'''Creating features from dataset file at {data_dir}'''  )
																										snake_case__													=   token_classification_task.read_examples_from_file(_a				,		_a  )
																										# TODO clean up all this to leverage built-in features of tokenizers
																										snake_case__													=   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:					List[Any]  )						->						str:
																		return len(self.features  )


														def __getitem__(   self:					Tuple				,		UpperCamelCase:					Any  )						->						Any:
																		return self.features[i]


if is_tf_available():
							import tensorflow as tf


							class      __SCREAMING_SNAKE_CASE:
														_UpperCAmelCase       			=				42
														_UpperCAmelCase       			=				-1_0_0
														def __init__(   self:					Tuple				,		UpperCamelCase:					TokenClassificationTask				,		UpperCamelCase:					str				,		UpperCamelCase:					PreTrainedTokenizer				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					str				,		UpperCamelCase:					Optional[int] = None				,		UpperCamelCase:					Union[str, Any]=False				,		UpperCamelCase:					Split = Split.train				,		)						->						int:
																		snake_case__													=   token_classification_task.read_examples_from_file(_a				,		_a  )
																		# TODO clean up all this to leverage built-in features of tokenizers
																		snake_case__													=   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:
																						snake_case__													=   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:
																						snake_case__													=   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 lowerCAmelCase_       (   self:					int  )						->						Optional[int]:
																		snake_case__													=   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

																		return self.dataset
														def __len__(   self:					Optional[int]  )						->						Any:
																		return len(self.features  )


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






'''simple docstring'''







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   VQModel(
         block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,latent_channels=3							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   CLIPTextConfig(
         bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=1000							,)
     return CLIPTextModel(_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.dummy_uncond_unet
     _a				:	List[Any]			=   DDIMScheduler()
     _a				:	List[Any]			=   self.dummy_vq_model

     _a				:	str			=   LDMPipeline(unet=_a							,vqvae=_a							,scheduler=_a       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	List[str]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Tuple			=   image[0, -3:, -3:, -1]
     _a				:	Optional[Any]			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _a				:	int			=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
     _a				:	Any			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Dict			=   ldm(generator=_a							,num_inference_steps=5							,output_type='numpy'       ).images

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

     assert image.shape == (1, 256, 256, 3)
     _a				:	Optional[int]			=   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
     _a				:	int			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance



