

from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowerCAmelCase_       	=	logging.get_logger(__name__)





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				List[str]							)      ->List[str]:

									if isinstance(lowerCamelCase_							,   lowerCamelCase_							):
															snake_case_   =	[label.strip() for label in labels.split(''','''							) if label.strip()]
									return labels







			def __call__(     self :				str							,   _UpperCamelCase :				Dict							,   _UpperCamelCase :				List[Any]							,   _UpperCamelCase :				List[Any]							)      ->Dict:

									if len(lowerCamelCase_							) == 0 or len(lowerCamelCase_							) == 0:
															raise ValueError('''You must include at least one label and at least one sequence.'''							)
									if hypothesis_template.format(labels[0]							) == hypothesis_template:
															raise ValueError(
															    (
															        '''The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. '''
															        '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
															    ).format(lowerCamelCase_							)							)

									if isinstance(lowerCamelCase_							,   lowerCamelCase_							):
															snake_case_   =	[sequences]

									snake_case_   =	[]
									for sequence in sequences:
															sequence_pairs.extend([[sequence, hypothesis_template.format(lowerCamelCase_							)] for label in labels]							)

									return sequence_pairs, sequences






@add_end_docstrings(__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				List[Any]							,   _UpperCamelCase :				Any=ZeroShotClassificationArgumentHandler()							,   *_UpperCamelCase :				Optional[int]							,   **_UpperCamelCase :				Any							)      ->Optional[int]:
									snake_case_   =	args_parser
									super().__init__(*lowerCamelCase_							,   **lowerCamelCase_							)
									if self.entailment_id == -1:
															logger.warning(
															    '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
															    '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'''							)







			@property
			def 				snake_case__(     self :				Dict							)      ->List[Any]:

									for label, ind in self.model.config.labelaid.items():
															if label.lower().startswith('''entail'''							):
																					return ind
									return -1







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				Optional[Any]=True							,   _UpperCamelCase :				int=True							,   _UpperCamelCase :				List[Any]=TruncationStrategy.ONLY_FIRST							,   **_UpperCamelCase :				List[Any]							)      ->int:
									snake_case_   =	self.framework
									if self.tokenizer.pad_token is None:
															# Override for tokenizers not supporting padding
															logger.error(
															    '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
															    ''' `pad_token=eos_token`'''							)
															snake_case_   =	self.tokenizer.eos_token
									try:
															snake_case_   =	self.tokenizer(
															    lowerCamelCase_							,   add_special_tokens=lowerCamelCase_							,   return_tensors=lowerCamelCase_							,   padding=lowerCamelCase_							,   truncation=lowerCamelCase_							,   )
									except Exception as e:
															if "too short" in str(lowerCamelCase_							):
																					# tokenizers might yell that we want to truncate
																					# to a value that is not even reached by the input.
																					# In that case we don't want to truncate.
																					# It seems there's not a really better way to catch that
																					# exception.

																					snake_case_   =	self.tokenizer(
																					    lowerCamelCase_							,   add_special_tokens=lowerCamelCase_							,   return_tensors=lowerCamelCase_							,   padding=lowerCamelCase_							,   truncation=TruncationStrategy.DO_NOT_TRUNCATE							,   )
															else:
																					raise e

									return inputs







			def 				snake_case__(     self :				List[str]							,   **_UpperCamelCase :				List[Any]							)      ->Union[str, Any]:

									if kwargs.get('''multi_class'''							,   lowerCamelCase_							) is not None:
															snake_case_   =	kwargs['''multi_class''']
															logger.warning(
															    '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
															    '''`multi_class` will be removed in a future version of Transformers.'''							)
									snake_case_   =	{}
									if "candidate_labels" in kwargs:
															snake_case_   =	self._args_parser._parse_labels(kwargs['''candidate_labels''']							)
									if "hypothesis_template" in kwargs:
															snake_case_   =	kwargs['''hypothesis_template''']

									snake_case_   =	{}
									if "multi_label" in kwargs:
															snake_case_   =	kwargs['''multi_label''']
									return preprocess_params, {}, postprocess_params







			def __call__(     self :				Optional[int]							,   _UpperCamelCase :				List[str]							,   *_UpperCamelCase :				List[str]							,   **_UpperCamelCase :				Optional[Any]							,   )      ->Optional[int]:

									if len(lowerCamelCase_							) == 0:
															pass
									elif len(lowerCamelCase_							) == 1 and "candidate_labels" not in kwargs:
															snake_case_   =	args[0]
									else:
															raise ValueError(f'''Unable to understand extra arguments {args}'''							)

									return super().__call__(lowerCamelCase_							,   **lowerCamelCase_							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[Any]							,   _UpperCamelCase :				Union[str, Any]=None							,   _UpperCamelCase :				Any="This example is {}."							)      ->List[str]:
									snake_case_,    snake_case_   =	self._args_parser(lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_							)

									for i, (candidate_label, sequence_pair) in enumerate(zip(lowerCamelCase_							,   lowerCamelCase_							)							):
															snake_case_   =	self._parse_and_tokenize([sequence_pair]							)

															yield {
															    "candidate_label": candidate_label,
															    "sequence": sequences[0],
															    "is_last": i == len(lowerCamelCase_							) - 1,
															    **model_input,
															}







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				Any							)      ->str:
									snake_case_   =	inputs['''candidate_label''']
									snake_case_   =	inputs['''sequence''']
									snake_case_   =	{k: inputs[k] for k in self.tokenizer.model_input_names}
									snake_case_   =	self.model(**lowerCamelCase_							)

									snake_case_   =	{
									    '''candidate_label''': candidate_label,
									    '''sequence''': sequence,
									    '''is_last''': inputs['''is_last'''],
									    **outputs,
									}
									return model_outputs







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							,   _UpperCamelCase :				Optional[int]=False							)      ->str:
									snake_case_   =	[outputs['''candidate_label'''] for outputs in model_outputs]
									snake_case_   =	[outputs['''sequence'''] for outputs in model_outputs]
									snake_case_   =	np.concatenate([output['''logits'''].numpy() for output in model_outputs]							)
									snake_case_   =	logits.shape[0]
									snake_case_   =	len(lowerCamelCase_							)
									snake_case_   =	N // n
									snake_case_   =	logits.reshape((num_sequences, n, -1)							)

									if multi_label or len(lowerCamelCase_							) == 1:
															# softmax over the entailment vs. contradiction dim for each label independently
															snake_case_   =	self.entailment_id
															snake_case_   =	-1 if entailment_id == 0 else 0
															snake_case_   =	reshaped_outputs[..., [contradiction_id, entailment_id]]
															snake_case_   =	np.exp(lowerCamelCase_							) / np.exp(lowerCamelCase_							).sum(-1							,   keepdims=lowerCamelCase_							)
															snake_case_   =	scores[..., 1]
									else:
															# softmax the "entailment" logits over all candidate labels
															snake_case_   =	reshaped_outputs[..., self.entailment_id]
															snake_case_   =	np.exp(lowerCamelCase_							) / np.exp(lowerCamelCase_							).sum(-1							,   keepdims=lowerCamelCase_							)

									snake_case_   =	list(reversed(scores[0].argsort()							)							)
									return {
									    "sequence": sequences[0],
									    "labels": [candidate_labels[i] for i in top_inds],
									    "scores": scores[0, top_inds].tolist(),
									}




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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




	@torch.no_grad()
	def __call__(self		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 50		,       lowerCamelCase_ = "pil"		,       lowerCamelCase_ = True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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

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

						for t in self.progress_bar(self.scheduler.timesteps		):
											# 1. predict noise model_output
											a					     =   self.unet(lowerCamelCase_		,       lowerCamelCase_		).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
											a					     =   self.scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		).prev_sample

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

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

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