


"""simple docstring"""




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_			     =  logging.get_logger(__name__)
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self							, _a      ):
				if isinstance(_a							, _a      ):
						__a           =			[label.strip() for label in labels.split(''','''      ) if label.strip()]
				return labels





		def __call__(  self							, _a							, _a							, _a      ):
				if len(_a      ) == 0 or len(_a      ) == 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(_a      )      )

				if isinstance(_a							, _a      ):
						__a           =			[sequences]

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

				return sequence_pairs, sequences







@add_end_docstrings(__SCREAMING_SNAKE_CASE       )
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a=ZeroShotClassificationArgumentHandler()							, *_a							, **_a      ):
				__a           =			args_parser
				super().__init__(*_a							, **_a      )
				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      ):
				for label, ind in self.model.config.labelaid.items():
						if label.lower().startswith('''entail'''      ):
								return ind
				return -1

		def 			__UpperCAmelCase       (  self							, _a							, _a=True							, _a=True							, _a=TruncationStrategy.ONLY_FIRST							, **_a      ):
				__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(
						    _a							, add_special_tokens=_a							, return_tensors=_a							, padding=_a							, truncation=_a							, )
				except Exception as e:
						if "too short" in str(_a      ):
								# 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(
								    _a							, add_special_tokens=_a							, return_tensors=_a							, padding=_a							, truncation=TruncationStrategy.DO_NOT_TRUNCATE							, )
						else:
								raise e

				return inputs

		def 			__UpperCAmelCase       (  self							, **_a      ):
				if kwargs.get('''multi_class'''							, _a      ) 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							, _a							, *_a							, **_a							, ):
				if len(_a      ) == 0:
						pass
				elif len(_a      ) == 1 and "candidate_labels" not in kwargs:
						__a           =			args[0]
				else:
						raise ValueError(f'''Unable to understand extra arguments {args}'''      )

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

		def 			__UpperCAmelCase       (  self							, _a							, _a=None							, _a="This example is {}."      ):
				__a      ,     __a           =			self._args_parser(_a							, _a							, _a      )

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

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

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			inputs['''candidate_label''']
				__a           =			inputs['''sequence''']
				__a           =			{k: inputs[k] for k in self.tokenizer.model_input_names}
				__a           =			self.model(**_a      )

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





		def 			__UpperCAmelCase       (  self							, _a							, _a=False      ):
				__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(_a      )
				__a           =			N // n
				__a           =			logits.reshape((num_sequences, n, -1)      )

				if multi_label or len(_a      ) == 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(_a      ) / np.exp(_a      ).sum(-1							, keepdims=_a      )
						__a           =			scores[..., 1]
				else:
						# softmax the "entailment" logits over all candidate labels
						__a           =			reshaped_outputs[..., self.entailment_id]
						__a           =			np.exp(_a      ) / np.exp(_a      ).sum(-1							, keepdims=_a      )

				__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(),
				}







"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()

        # make sure scheduler can always be converted to DDIM
        a__:     Dict										=	DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=lowercase    ,			scheduler=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

        if isinstance(lowercase    ,			lowercase) and len(lowercase) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).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__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase)


