
import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase						:      Any									= logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






			'''simple docstring'''





			warnings.warn(
			    "The preprocess method is deprecated and will be removed in a future version. Please"
			    " use VaeImageProcessor.preprocess instead"		,						_lowerCamelCase		,						)
			if isinstance(_lowerCamelCase		,						torch.Tensor):
						return image
			elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
						__UpperCamelCase			:     int         =      [image]

			if isinstance(image[0]		,						PIL.Image.Image):
						__UpperCamelCase			:     Union[str, Any]         =      image[0].size
						__UpperCamelCase			:     List[Any]         =      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						__UpperCamelCase			:     Any         =      [np.array(i.resize((w, h)		,						resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
						__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
						__UpperCamelCase			:     Any         =      np.array(_lowerCamelCase).astype(np.floataa) / 255.0
						__UpperCamelCase			:     Union[str, Any]         =      image.transpose(0		,						3		,						1		,						2)
						__UpperCamelCase			:     Optional[int]         =      2.0 * image - 1.0
						__UpperCamelCase			:     List[str]         =      torch.from_numpy(_lowerCamelCase)
			elif isinstance(image[0]		,						torch.Tensor):
						__UpperCamelCase			:     Dict         =      torch.cat(_lowerCamelCase		,						dim=0)
			return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






			'''simple docstring'''





			if isinstance(_lowerCamelCase		,						torch.Tensor):
						return mask
			elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
						__UpperCamelCase			:     Any         =      [mask]

			if isinstance(mask[0]		,						PIL.Image.Image):
						__UpperCamelCase			:     Optional[int]         =      mask[0].size
						__UpperCamelCase			:     List[Any]         =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__UpperCamelCase			:     List[str]         =      [np.array(m.convert("L").resize((w, h)		,						resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
						__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
						__UpperCamelCase			:     Any         =      mask.astype(np.floataa) / 255.0
						__UpperCamelCase			:     List[str]         =      0
						__UpperCamelCase			:     Dict         =      1
						__UpperCamelCase			:     List[Any]         =      torch.from_numpy(_lowerCamelCase)
			elif isinstance(mask[0]		,						torch.Tensor):
						__UpperCamelCase			:     Optional[Any]         =      torch.cat(_lowerCamelCase		,						dim=0)
			return mask




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  4_2
	_A			=  4_2







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
				super().__init__()
				self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Union[str, Any]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:int = 2_5_0  ,     a	:float = 0.0  ,     a	:int = 1_0  ,     a	:int = 1_0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[ImagePipelineOutput, Tuple]:
				__UpperCamelCase			:     Tuple         =      image

				__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
				__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
				__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
				__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

				__UpperCamelCase			:     str         =      original_image.shape[0]

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

				__UpperCamelCase			:     List[str]         =      original_image.shape
				__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

				# set step values
				self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
				__UpperCamelCase			:     Tuple         =      eta

				__UpperCamelCase			:     List[str]         =      self.scheduler.timesteps[0] + 1
				__UpperCamelCase			:     str         =      generator[0] if isinstance(a  ,     a		) else generator
				for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
							if t < t_last:
										# predict the noise residual
										__UpperCamelCase			:     Union[str, Any]         =      self.unet(a  ,     a		).sample
										# compute previous image: x_t -> x_t-1
										__UpperCamelCase			:     Tuple         =      self.scheduler.step(a  ,     a  ,     a  ,     a  ,     a  ,     a		).prev_sample

							else:
										# compute the reverse: x_t-1 -> x_t
										__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
							__UpperCamelCase			:     Dict         =      t

				__UpperCamelCase			:     Union[str, Any]         =      (image / 2 + 0.5).clamp(0  ,     1		)
				__UpperCamelCase			:     Tuple         =      image.cpu().permute(0  ,     2  ,     3  ,     1		).numpy()
				if output_type == "pil":
							__UpperCamelCase			:     str         =      self.numpy_to_pil(a		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=a		)

# 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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'facebook/bart-large-mnli'
	_A			=  (
	    '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.'
	)
	_A			=  'text_classifier'
	_A			=  AutoTokenizer
	_A			=  AutoModelForSequenceClassification

	_A			=  ['text', ['text']]
	_A			=  ['text']







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







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Dict  ,     a	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     str         =      labels
								return self.pre_processor(
								    [text] * len(a		)  ,     [f'This example is {label}' for label in labels]  ,     return_tensors="pt"  ,     padding="max_length"  ,     )







	def      _lowerCamelCase       (      self	:Any  ,     a	:Any		)	->				Optional[int]:
								__UpperCamelCase			:     Optional[int]         =      outputs.logits
								__UpperCamelCase			:     Dict         =      torch.argmax(logits[:, 2]		).item()
								return self._labels[label_id]