


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,
)


UpperCAmelCase   :    str     =		logging.getLogger(__name__)







@dataclass(frozen=a_  )
class        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					str
		UpperCamelCase     :					str
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					Optional[str]			       =		None
		UpperCamelCase     :					Optional[str]			       =		None







@dataclass(frozen=a_  )
class        __lowercase       :



		"""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        __lowercase       (							a_  ):



				"""simple docstring"""

				UpperCamelCase     :					List[InputFeatures]







				def __init__( self		,      A		,      A		,      A		,      A = None		,      A=False		,      A = False		,      )  -> List[Any]:







							'''simple docstring'''







							lowerCamelCase    		=  hans_processors[task]()

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

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							lowerCamelCase    		=  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}'	)
													lowerCamelCase    		=  torch.load(A	)
										else:
													logger.info(F'Creating features from dataset file at {data_dir}'	)

													lowerCamelCase    		=  (
													    processor.get_dev_examples(A	) if evaluate else processor.get_train_examples(A	)
													)

													logger.info("""Training examples: %s"""		,      len(A	)	)
													lowerCamelCase    		=  hans_convert_examples_to_features(A		,      A		,      A		,      A	)
													logger.info("""Saving features into cached file %s"""		,      A	)
													torch.save(self.features		,      A	)







				def __len__( self	)  -> List[Any]:







							'''simple docstring'''







							return len(self.features	)







				def __getitem__( self		,      A	)  -> InputFeatures:







							'''simple docstring'''







							return self.features[i]







				def 					__A   ( self	)  -> Tuple:







							'''simple docstring'''







							return self.label_list


if is_tf_available():
		import tensorflow as tf







		class        __lowercase       :



				"""simple docstring"""

				UpperCamelCase     :					List[InputFeatures]







				def __init__( self		,      A		,      A		,      A		,      A = 1_28		,      A=False		,      A = False		,      )  -> str:







							'''simple docstring'''







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

							lowerCamelCase    		=  processor.get_dev_examples(A	) if evaluate else processor.get_train_examples(A	)
							lowerCamelCase    		=  hans_convert_examples_to_features(A		,      A		,      A		,      A	)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features	)		,      desc="""convert examples to features"""	):
													if ex_index % 1_00_00 == 0:
																logger.info("""Writing example %d of %d""" % (ex_index, len(A	))	)

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

							lowerCamelCase    		=  tf.data.Dataset.from_generator(
							    A		,      (
							        {
							            """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	)  -> Tuple:







							'''simple docstring'''







							return self.dataset







				def __len__( self	)  -> List[Any]:







							'''simple docstring'''







							return len(self.features	)







				def __getitem__( self		,      A	)  -> InputFeatures:







							'''simple docstring'''







							return self.features[i]







				def 					__A   ( self	)  -> str:







							'''simple docstring'''







							return self.label_list







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def 					__A   ( self		,      A	)  -> Optional[int]:







					'''simple docstring'''







					return self._create_examples(self._read_tsv(os.path.join(A		,      """heuristics_train_set.txt"""	)	)		,      """train"""	)







		def 					__A   ( self		,      A	)  -> int:







					'''simple docstring'''







					return self._create_examples(self._read_tsv(os.path.join(A		,      """heuristics_evaluation_set.txt"""	)	)		,      """dev"""	)







		def 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A	)  -> Optional[int]:







					'''simple docstring'''







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






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[InputExample]  ,							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						PreTrainedTokenizer  ,							):





			'''simple docstring'''




			lowerCamelCase    		=  {label: i for i, label in enumerate(lowerCamelCase__      )}

			lowerCamelCase    		=  []
			for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase__      )  ,							desc="""convert examples to features"""      ):
						if ex_index % 10000 == 0:
									logger.info("""Writing example %d""" % (ex_index)      )

						lowerCamelCase    		=  tokenizer(
						    example.text_a  ,							example.text_b  ,							add_special_tokens=lowerCamelCase__  ,							max_length=lowerCamelCase__  ,							padding="""max_length"""  ,							truncation=lowerCamelCase__  ,							return_overflowing_tokens=lowerCamelCase__  ,							)

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

						lowerCamelCase    		=  int(example.pairID      )

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

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

			return features


UpperCAmelCase   :    Dict     =		{
    "hans": 3,
}



UpperCAmelCase   :    Tuple     =		{
    "hans": HansProcessor,
}





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

