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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase       :				Dict       =			logging.getLogger(__name__)




@dataclass(frozen=UpperCAmelCase					)
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	_UpperCAmelCase       :		str
	_UpperCAmelCase       :		str
	_UpperCAmelCase       :		Optional[str]       =					None
	_UpperCAmelCase       :		Optional[str]       =					None
	_UpperCAmelCase       :		Optional[str]       =					None




@dataclass(frozen=UpperCAmelCase					)
class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	_UpperCAmelCase       :		List[int]
	_UpperCAmelCase       :		Optional[List[int]]       =					None
	_UpperCAmelCase       :		Optional[List[int]]       =					None
	_UpperCAmelCase       :		Optional[Union[int, float]]       =					None
	_UpperCAmelCase       :		Optional[int]       =					None


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




			class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





				'''simple docstring'''



				_UpperCAmelCase       :		List[InputFeatures]



				def __init__(  self	:    int			,  lowercase	:    str			,  lowercase	:    PreTrainedTokenizer			,  lowercase	:    str			,  lowercase	:    Optional[int] = None			,  lowercase	:    Any=False			,  lowercase	:    bool = False			,  ):


							'''simple docstring'''


							_snake_case		=			hans_processors[task]()

							_snake_case		=			os.path.join(
							    lowercase			,  'cached_{}_{}_{}_{}'.format(
							        'dev' if evaluate else 'train'			,  tokenizer.__class__.__name__			,  str(lowercase	)			,  lowercase			,  )			,  )
							_snake_case		=			processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										_snake_case	,					_snake_case		=			label_list[2], label_list[1]
							_snake_case		=			label_list

							# 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(lowercase	):
										if os.path.exists(lowercase	) and not overwrite_cache:
													logger.info(f'''Loading features from cached file {cached_features_file}'''	)
													_snake_case		=			torch.load(lowercase	)
										else:
													logger.info(f'''Creating features from dataset file at {data_dir}'''	)

													_snake_case		=			(
													    processor.get_dev_examples(lowercase	) if evaluate else processor.get_train_examples(lowercase	)
													)

													logger.info('Training examples: %s'			,  len(lowercase	)	)
													_snake_case		=			hans_convert_examples_to_features(lowercase			,  lowercase			,  lowercase			,  lowercase	)
													logger.info('Saving features into cached file %s'			,  lowercase	)
													torch.save(self.features			,  lowercase	)



				def __len__(  self	:    List[Any]	):


							'''simple docstring'''


							return len(self.features	)



				def __getitem__(  self	:    Optional[Any]			,  lowercase	:    Tuple	):


							'''simple docstring'''


							return self.features[i]





				def  A							(  self	:    Optional[int]	):


							'''simple docstring'''


							return self.label_list


if is_tf_available():
			import tensorflow as tf




			class    SCREAMING_SNAKE_CASE__     :





				'''simple docstring'''



				_UpperCAmelCase       :		List[InputFeatures]



				def __init__(  self	:    Optional[Any]			,  lowercase	:    str			,  lowercase	:    PreTrainedTokenizer			,  lowercase	:    str			,  lowercase	:    Optional[int] = 128			,  lowercase	:    Tuple=False			,  lowercase	:    bool = False			,  ):


							'''simple docstring'''


							_snake_case		=			hans_processors[task]()
							_snake_case		=			processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										_snake_case	,					_snake_case		=			label_list[2], label_list[1]
							_snake_case		=			label_list

							_snake_case		=			processor.get_dev_examples(lowercase	) if evaluate else processor.get_train_examples(lowercase	)
							_snake_case		=			hans_convert_examples_to_features(lowercase			,  lowercase			,  lowercase			,  lowercase	)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features	)			,  desc='convert examples to features'	):
													if ex_index % 10_000 == 0:
																logger.info('Writing example %d of %d' % (ex_index, len(lowercase	))	)

													yield (
													    {
													        "example_id": 0,
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label,
													)

							_snake_case		=			tf.data.Dataset.from_generator(
							    lowercase			,  (
							        {
							            'example_id': tf.intaa,
							            'input_ids': tf.intaa,
							            'attention_mask': tf.intaa,
							            'token_type_ids': tf.intaa,
							        },
							        tf.intaa,
							    )			,  (
							        {
							            'example_id': tf.TensorShape([]	),
							            'input_ids': tf.TensorShape([None, None]	),
							            'attention_mask': tf.TensorShape([None, None]	),
							            'token_type_ids': tf.TensorShape([None, None]	),
							        },
							        tf.TensorShape([]	),
							    )			,  )



				def  A							(  self	:    str	):


							'''simple docstring'''


							return self.dataset



				def __len__(  self	:    str	):


							'''simple docstring'''


							return len(self.features	)



				def __getitem__(  self	:    Dict			,  lowercase	:    Tuple	):


							'''simple docstring'''


							return self.features[i]





				def  A							(  self	:    List[str]	):


							'''simple docstring'''


							return self.label_list




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def  A							(  self	:    Tuple			,  lowercase	:    Tuple	):


				'''simple docstring'''


				return self._create_examples(self._read_tsv(os.path.join(lowercase			,  'heuristics_train_set.txt'	)	)			,  'train'	)



	def  A							(  self	:    Tuple			,  lowercase	:    List[str]	):


				'''simple docstring'''


				return self._create_examples(self._read_tsv(os.path.join(lowercase			,  'heuristics_evaluation_set.txt'	)	)			,  'dev'	)



	def  A							(  self	:    Tuple	):


				'''simple docstring'''


				return ["contradiction", "entailment", "neutral"]





	def  A							(  self	:    int			,  lowercase	:    str			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			[]
				for i, line in enumerate(lowercase	):
							if i == 0:
										continue
							_snake_case		=			'%s-%s' % (set_type, line[0])
							_snake_case		=			line[5]
							_snake_case		=			line[6]
							_snake_case		=			line[7][2:] if line[7].startswith('ex'	) else line[7]
							_snake_case		=			line[0]
							examples.append(InputExample(guid=lowercase			,  text_a=lowercase			,  text_b=lowercase			,  label=lowercase			,  pairID=lowercase	)	)
				return examples






def        a_		(       __lowercase   :	List[InputExample]       ,					__lowercase   :	List[str]       ,					__lowercase   :	int       ,					__lowercase   :	PreTrainedTokenizer       ,					) ->  int:
			_snake_case		=			{label: i for i, label in enumerate(__lowercase )}

			_snake_case		=			[]
			for ex_index, example in tqdm.tqdm(enumerate(__lowercase )       ,					desc='convert examples to features' ):
						if ex_index % 10_000 == 0:
									logger.info('Writing example %d' % (ex_index) )

						_snake_case		=			tokenizer(
						    example.text_a       ,					example.text_b       ,					add_special_tokens=__lowercase       ,					max_length=__lowercase       ,					padding='max_length'       ,					truncation=__lowercase       ,					return_overflowing_tokens=__lowercase       ,					)

						_snake_case		=			label_map[example.label] if example.label in label_map else 0

						_snake_case		=			int(example.pairID )

						features.append(InputFeatures(**__lowercase       ,					label=__lowercase       ,					pairID=__lowercase ) )

			for i, example in enumerate(examples[:5] ):
						logger.info('*** Example ***' )
						logger.info(f'''guid: {example}''' )
						logger.info(f'''features: {features[i]}''' )

			return features


_lowerCamelCase       :				Optional[int]       =			{
    '''hans''': 3,
}

_lowerCamelCase       :				List[str]       =			{
    '''hans''': HansProcessor,
}

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)