



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


_lowercase: int      			=		logging.get_logger(__name__)



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




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

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




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

						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_		):
											a					     =   [sequences]

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

						return sequence_pairs, sequences




@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_=ZeroShotClassificationArgumentHandler()		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=TruncationStrategy.ONLY_FIRST		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   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`"		)
											a					     =   self.tokenizer.eos_token
						try:
											a					     =   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.

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

						return inputs




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						if kwargs.get("multi_class"		,       lowerCamelCase_		) is not None:
											a					     =   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."		)
						a					     =   {}
						if "candidate_labels" in kwargs:
											a					     =   self._args_parser._parse_labels(kwargs["candidate_labels"]		)
						if "hypothesis_template" in kwargs:
											a					     =   kwargs["hypothesis_template"]

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




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

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

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_="This example is {}."		):
						"""simple docstring"""

						a     ,				a					     =   self._args_parser(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

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

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




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

						a					     =   inputs["candidate_label"]
						a					     =   inputs["sequence"]
						a					     =   {k: inputs[k] for k in self.tokenizer.model_input_names}
						a					     =   self.model(**lowerCamelCase_		)

						a					     =   {
						    "candidate_label": candidate_label,
						    "sequence": sequence,
						    "is_last": inputs["is_last"],
						    **outputs,
						}
						return model_outputs




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		):
						"""simple docstring"""

						a					     =   [outputs["candidate_label"] for outputs in model_outputs]
						a					     =   [outputs["sequence"] for outputs in model_outputs]
						a					     =   np.concatenate([output["logits"].numpy() for output in model_outputs]		)
						a					     =   logits.shape[0]
						a					     =   len(lowerCamelCase_		)
						a					     =   N // n
						a					     =   logits.reshape((num_sequences, n, -1)		)

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

						a					     =   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"
