
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((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict       )  ->    Tuple:
							if isinstance(UpperCAmelCase__			,				torch.Tensor       ):
														return image
							elif isinstance(UpperCAmelCase__			,				PIL.Image.Image       ):
														lowerCamelCase_							  =					[image]

							lowerCamelCase_							  =					[trans(img.convert("""RGB"""       )       ) for img in image]
							lowerCamelCase_							  =					torch.stack(UpperCAmelCase__       )
							return image





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     str   ):
												super().__init__()

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

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

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict   ):
												if strength < 0 or strength > 1:
																			raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''   )

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ):
												# get the original timestep using init_timestep
												lowerCamelCase_							  =					min(int(num_inference_steps * strength   )   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					max(num_inference_steps - init_timestep   ,					0   )
												lowerCamelCase_							  =					self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]=None   ):
												if not isinstance(__UpperCamelCase   ,					(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(__UpperCamelCase   )}'''   )

												lowerCamelCase_							  =					image.to(device=__UpperCamelCase   ,					dtype=__UpperCamelCase   )

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

												lowerCamelCase_							  =					init_latents.shape
												lowerCamelCase_							  =					randn_tensor(__UpperCamelCase   ,					generator=__UpperCamelCase   ,					device=__UpperCamelCase   ,					dtype=__UpperCamelCase   )

												# get latents
												print("""add noise to latents at timestep"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.scheduler.add_noise(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					init_latents

												return latents



					@torch.no_grad()
					def __call__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[torch.FloatTensor, PIL.Image.Image] = None   ,					__UpperCamelCase					:     float = 0.8   ,					__UpperCamelCase					:     int = 1   ,					__UpperCamelCase					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,					__UpperCamelCase					:     float = 0.0   ,					__UpperCamelCase					:     int = 5_0   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     Optional[str] = "pil"   ,					__UpperCamelCase					:     bool = True   ,					):
												self.check_inputs(__UpperCamelCase   )

												# 2. Preprocess image
												lowerCamelCase_							  =					preprocess(__UpperCamelCase   )

												# 3. set timesteps
												self.scheduler.set_timesteps(__UpperCamelCase   ,					device=self.device   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.get_timesteps(__UpperCamelCase   ,					__UpperCamelCase   ,					self.device   )
												lowerCamelCase_							  =					timesteps[:1].repeat(__UpperCamelCase   )

												# 4. Prepare latent variables
												lowerCamelCase_							  =					self.prepare_latents(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					self.unet.dtype   ,					self.device   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					latents

												# 5. Denoising loop
												for t in self.progress_bar(__UpperCamelCase   ):
																			# 1. predict noise model_output
																			lowerCamelCase_							  =					self.unet(__UpperCamelCase   ,					__UpperCamelCase   ).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(
																			    __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					eta=__UpperCamelCase   ,					use_clipped_model_output=__UpperCamelCase   ,					generator=__UpperCamelCase   ,					).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(__UpperCamelCase   )

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

												return ImagePipelineOutput(images=__UpperCamelCase   )







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''facebook/bart-large-mnli'''
					SCREAMING_SNAKE_CASE			    =    (
					    '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
					    '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
					    '''It returns the most likely label in the list of provided `labels` for the input text.'''
					)
					SCREAMING_SNAKE_CASE			    =    '''text_classifier'''
					SCREAMING_SNAKE_CASE			    =    AutoTokenizer
					SCREAMING_SNAKE_CASE			    =    AutoModelForSequenceClassification

					SCREAMING_SNAKE_CASE			    =    ['''text''', ['''text''']]
					SCREAMING_SNAKE_CASE			    =    ['''text''']

					def    lowercase__      (	self					:     Union[str, Any]   ):
												super().setup()
												lowerCamelCase_							  =					self.model.config
												lowerCamelCase_							  =					-1
												for idx, label in config.idalabel.items():
																			if label.lower().startswith("""entail"""   ):
																										lowerCamelCase_							  =					int(__UpperCamelCase   )
												if self.entailment_id == -1:
																			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					labels
												return self.pre_processor(
												    [text] * len(__UpperCamelCase   )   ,					[F'''This example is {label}''' for label in labels]   ,					return_tensors="""pt"""   ,					padding="""max_length"""   ,					)



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					outputs.logits
												lowerCamelCase_							  =					torch.argmax(logits[:, 2]   ).item()
												return self._labels[label_id]





