




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =42
						lowerCAmelCase__			      =None


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=0.9_9_9    ,						lowerCAmelCase="cosine"    ,						)->	Tuple:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowerCAmelCase ):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowerCAmelCase ):
						return math.exp(t * -1_2.0 )

		else:
				raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

		SCREAMING_SNAKE_CASE_					      =							[]
		for i in range(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_					      =							(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(lowerCAmelCase ) / alpha_bar_fn(lowerCAmelCase )    ,						lowerCAmelCase ) )
		return torch.tensor(lowerCAmelCase    ,						dtype=torch.floataa )




class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =1



						@register_to_config
						def __init__(			self	:   Any					,							snake_case__	:   int = 10_00					,							snake_case__	:   float = 0.00_01					,							snake_case__	:   float = 0.02					,							snake_case__	:   str = "linear"					,							snake_case__	:   Optional[Union[np.ndarray, List[float]]] = None					,							snake_case__	:   bool = True					,							snake_case__	:   bool = True					,							snake_case__	:   int = 0					,							snake_case__	:   str = "epsilon"					,							snake_case__	:   float = 1.0					,							**snake_case__	:   Optional[int]					,							):

								"""simple docstring"""

								if kwargs.get('set_alpha_to_one'					,							snake_case__  ) is not None:
										SCREAMING_SNAKE_CASE_					      =							(
										    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
										)
										deprecate('set_alpha_to_one'					,							'1.0.0'					,							snake_case__					,							standard_warn=snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							kwargs['set_alpha_to_one']
								if trained_betas is not None:
										SCREAMING_SNAKE_CASE_					      =							torch.tensor(snake_case__					,							dtype=torch.floataa  )
								elif beta_schedule == "linear":
										SCREAMING_SNAKE_CASE_					      =							torch.linspace(snake_case__					,							snake_case__					,							snake_case__					,							dtype=torch.floataa  )
								elif beta_schedule == "scaled_linear":
										# this schedule is very specific to the latent diffusion model.
										SCREAMING_SNAKE_CASE_					      =							(
										    torch.linspace(beta_start**0.5					,							beta_end**0.5					,							snake_case__					,							dtype=torch.floataa  ) ** 2
										)
								elif beta_schedule == "squaredcos_cap_v2":
										# Glide cosine schedule
										SCREAMING_SNAKE_CASE_					      =							betas_for_alpha_bar(snake_case__  )
								else:
										raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

								SCREAMING_SNAKE_CASE_					      =							1.0 - self.betas
								SCREAMING_SNAKE_CASE_					      =							torch.cumprod(self.alphas					,							dim=0  )

								# At every step in inverted ddim, we are looking into the next alphas_cumprod
								# For the final step, there is no next alphas_cumprod, and the index is out of bounds
								# `set_alpha_to_zero` decides whether we set this parameter simply to zero
								# in this case, self.step() just output the predicted noise
								# or whether we use the final alpha of the "non-previous" one.
								SCREAMING_SNAKE_CASE_					      =							torch.tensor(0.0  ) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								SCREAMING_SNAKE_CASE_					      =							1.0

								# setable values
								SCREAMING_SNAKE_CASE_					      =							None
								SCREAMING_SNAKE_CASE_					      =							torch.from_numpy(np.arange(0					,							snake_case__  ).copy().astype(np.intaa  )  )



						def 	__a					(			self	:   List[str]					,							snake_case__	:   torch.FloatTensor					,							snake_case__	:   Optional[int] = None  ):

								"""simple docstring"""

								return sample



						def 	__a					(			self	:   Tuple					,							snake_case__	:   int					,							snake_case__	:   Union[str, torch.device] = None  ):

								"""simple docstring"""

								if num_inference_steps > self.config.num_train_timesteps:
										raise ValueError(
										    f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
										    f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
										    f''' maximal {self.config.num_train_timesteps} timesteps.'''  )

								SCREAMING_SNAKE_CASE_					      =							num_inference_steps
								SCREAMING_SNAKE_CASE_					      =							self.config.num_train_timesteps // self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								SCREAMING_SNAKE_CASE_					      =							(np.arange(0					,							snake_case__  ) * step_ratio).round().copy().astype(np.intaa  )
								SCREAMING_SNAKE_CASE_					      =							torch.from_numpy(snake_case__  ).to(snake_case__  )
								self.timesteps += self.config.steps_offset



						def 	__a					(			self	:   Dict					,							snake_case__	:   torch.FloatTensor					,							snake_case__	:   int					,							snake_case__	:   torch.FloatTensor					,							snake_case__	:   float = 0.0					,							snake_case__	:   bool = False					,							snake_case__	:   Optional[torch.FloatTensor] = None					,							snake_case__	:   bool = True					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							timestep + self.config.num_train_timesteps // self.num_inference_steps

								# 2. compute alphas, betas
								# change original implementation to exactly match noise levels for analogous forward process
								SCREAMING_SNAKE_CASE_					      =							self.alphas_cumprod[timestep]
								SCREAMING_SNAKE_CASE_					      =							(
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

								SCREAMING_SNAKE_CASE_					      =							1 - alpha_prod_t

								# 3. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								if self.config.prediction_type == "epsilon":
										SCREAMING_SNAKE_CASE_					      =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										SCREAMING_SNAKE_CASE_					      =							model_output
								elif self.config.prediction_type == "sample":
										SCREAMING_SNAKE_CASE_					      =							model_output
										SCREAMING_SNAKE_CASE_					      =							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
										SCREAMING_SNAKE_CASE_					      =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										SCREAMING_SNAKE_CASE_					      =							(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
								else:
										raise ValueError(
										    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
										    ' `v_prediction`'  )

								# 4. Clip or threshold "predicted x_0"
								if self.config.clip_sample:
										SCREAMING_SNAKE_CASE_					      =							pred_original_sample.clamp(
										    -self.config.clip_sample_range					,							self.config.clip_sample_range  )

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								SCREAMING_SNAKE_CASE_					      =							(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

								# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								SCREAMING_SNAKE_CASE_					      =							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

								if not return_dict:
										return (prev_sample, pred_original_sample)
								return DDIMSchedulerOutput(prev_sample=snake_case__					,							pred_original_sample=snake_case__  )






						def __len__(			self	:   Any  ):

								"""simple docstring"""

								return self.config.num_train_timesteps





from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE:     Optional[int]									= logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Any					,							**snake_case__	:   str  ):

								"""simple docstring"""

								super().__init__(**snake_case__  )

								requires_backends(self					,							'vision'  )
								self.check_model_type(
								    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == 'tf'
								    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )



						def __call__(			self	:   str					,							snake_case__	:   Union[str, List[str], "Image", List["Image"]]					,							**snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   str					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								if "candidate_labels" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['candidate_labels']
								if "hypothesis_template" in kwargs:
										SCREAMING_SNAKE_CASE_					      =							kwargs['hypothesis_template']

								return preprocess_params, {}, {}



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Optional[int]					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   List[str]="This is a photo of {}."  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							load_image(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							self.image_processor(images=[image]					,							return_tensors=self.framework  )
								SCREAMING_SNAKE_CASE_					      =							candidate_labels
								SCREAMING_SNAKE_CASE_					      =							[hypothesis_template.format(snake_case__  ) for x in candidate_labels]
								SCREAMING_SNAKE_CASE_					      =							self.tokenizer(snake_case__					,							return_tensors=self.framework					,							padding=snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[text_inputs]
								return inputs



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_inputs.pop('text_inputs'  )
								if isinstance(text_inputs[0]					,							snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0]
								else:
										# Batching case.
										SCREAMING_SNAKE_CASE_					      =							text_inputs[0][0]

								SCREAMING_SNAKE_CASE_					      =							self.model(**snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							{
								    'candidate_labels': candidate_labels,
								    'logits': outputs.logits_per_image,
								}
								return model_outputs






						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							model_outputs.pop('candidate_labels'  )
								SCREAMING_SNAKE_CASE_					      =							model_outputs['logits'][0]
								if self.framework == "pt":
										SCREAMING_SNAKE_CASE_					      =							logits.softmax(dim=-1  ).squeeze(-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.tolist()
										if not isinstance(snake_case__					,							snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							[scores]
								elif self.framework == "tf":
										SCREAMING_SNAKE_CASE_					      =							stable_softmax(snake_case__					,							axis=-1  )
										SCREAMING_SNAKE_CASE_					      =							probs.numpy().tolist()
								else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''  )

								SCREAMING_SNAKE_CASE_					      =							[
								    {'score': score, 'label': candidate_label}
								    for score, candidate_label in sorted(zip(snake_case__					,							snake_case__  )					,							key=lambda snake_case__  : -x[0]  )
								]
								return result