


"""simple docstring"""







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


a__       :							Dict        =  logging.get_logger(__name__)  # pylint: disable=invalid-name




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''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     ):
										__SCREAMING_SNAKE_CASE		     =       [image]

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

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




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					if isinstance(lowerCAmelCase_						,		torch.Tensor     ):
										return mask
					elif isinstance(lowerCAmelCase_						,		PIL.Image.Image     ):
										__SCREAMING_SNAKE_CASE		     =       [mask]

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




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						UNetaDModel
	snake_case__       :						RePaintScheduler




	def __init__(				self    :       List[str]    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			str:
						super().__init__()
						self.register_modules(unet=UpperCAmelCase__    ,    scheduler=UpperCAmelCase__     )




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

						__SCREAMING_SNAKE_CASE		     =       _preprocess_image(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       original_image.to(device=self.device    ,    dtype=self.unet.dtype     )
						__SCREAMING_SNAKE_CASE		     =       _preprocess_mask(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       mask_image.to(device=self.device    ,    dtype=self.unet.dtype     )

						__SCREAMING_SNAKE_CASE		     =       original_image.shape[0]

						# sample gaussian noise to begin the loop
						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."""     )

						__SCREAMING_SNAKE_CASE		     =       original_image.shape
						__SCREAMING_SNAKE_CASE		     =       randn_tensor(UpperCAmelCase__    ,    generator=UpperCAmelCase__    ,    device=self.device    ,    dtype=self.unet.dtype     )

						# set step values
						self.scheduler.set_timesteps(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__    ,    self.device     )
						__SCREAMING_SNAKE_CASE		     =       eta

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

											else:
																# compute the reverse: x_t-1 -> x_t
																__SCREAMING_SNAKE_CASE		     =       self.scheduler.undo_step(UpperCAmelCase__    ,    UpperCAmelCase__    ,    UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       t

						__SCREAMING_SNAKE_CASE		     =       (image / 2 + 0.5).clamp(0    ,    1     )
						__SCREAMING_SNAKE_CASE		     =       image.cpu().permute(0    ,    2    ,    3    ,    1     ).numpy()
						if output_type == "pil":
											__SCREAMING_SNAKE_CASE		     =       self.numpy_to_pil(UpperCAmelCase__     )

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=UpperCAmelCase__     )




"""simple docstring"""







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a__       :							Dict        =  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						str					      =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	snake_case__       :						Tuple					      =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		snake_case__       :						List[Any]					      =    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		snake_case__       :						Dict					      =    {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}




	@require_torch
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="pt"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier(["This is great !", "This is bad"]    ,    top_k=2     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						    ]    ,    )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    top_k=1     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )

						# Legacy behavior
						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier(["This is great !", "Something else"]    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
						    ]    ,    )

						__SCREAMING_SNAKE_CASE		     =       text_classifier(["This is great !", "Something else"]    ,    return_all_scores=UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [
						        {"label": "LABEL_0", "score": 0.504},
						        {"label": "LABEL_0", "score": 0.504},
						    ]    ,    )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						import torch

						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="pt"    ,    device=torch.device("cpu"     )    ,    )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )




	@require_tf
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="tf"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "LABEL_0", "score": 0.504}]     )




	@slow
	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is bad !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "NEGATIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("Birds are a type of animal"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 0.988}]     )




	@slow
	@require_tf
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"    ,    framework="tf"     )

						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is great !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("This is bad !"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "NEGATIVE", "score": 1.0}]     )
						__SCREAMING_SNAKE_CASE		     =       text_classifier("Birds are a type of animal"     )
						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": "POSITIVE", "score": 0.988}]     )




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Tuple     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       TextClassificationPipeline(model=UpperCAmelCase__    ,    tokenizer=UpperCAmelCase__     )
						return text_classifier, ["HuggingFace is in", "This is another test"]




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       text_classifier.model
						# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
						__SCREAMING_SNAKE_CASE		     =       "HuggingFace is in"
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )

						self.assertEqual(nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]     )
						self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )

						__SCREAMING_SNAKE_CASE		     =       ["HuggingFace is in ", "Paris is in France"]
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]    ,    )
						self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )
						self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()     )

						# Forcing to get all results with `top_k=None`
						# This is NOT the legacy format
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__    ,    top_k=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       len(model.config.idalabel.values()     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [[{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N, [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N]    ,    )

						__SCREAMING_SNAKE_CASE		     =       {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}    ,    )
						self.assertTrue(outputs["label"] in model.config.idalabel.values()     )

						# This might be used a text pair, but tokenizer + pipe interaction
						# makes it hard to understand that it's not using the pair properly
						# https://github.com/huggingface/transformers/issues/17305
						# We disabled this usage instead as it was outputting wrong outputs.
						__SCREAMING_SNAKE_CASE		     =       [["HuggingFace is in ", "Paris is in France"]]
						with self.assertRaises(UpperCAmelCase__     ):
											text_classifier(UpperCAmelCase__     )

						# This used to be valid for doing text pairs
						# We're keeping it working because of backward compatibility
						__SCREAMING_SNAKE_CASE		     =       text_classifier([[["HuggingFace is in ", "Paris is in France"]]]     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]    ,    )
						self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )
