
"""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.
lowerCAmelCase__							=				{'LayoutLMv2Config', 'LayoutLMv3Config'}





@is_pipeline_test
class  snake_case     (							unittest.TestCase   ):
							UpperCAmelCase__						     =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							UpperCAmelCase__						     =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														UpperCAmelCase__						     =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														UpperCAmelCase__						     =      {
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''LABEL_0''', '''score''': 0.5_04}]    )

									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is great !'''       ,       top_k=2    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}]    )

									SCREAMING_SNAKE_CASE_						 = text_classifier(['''This is great !''', '''This is bad''']       ,       top_k=2    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [
									        [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
									        [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
									    ]       ,       )

									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is great !'''       ,       top_k=1    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''LABEL_0''', '''score''': 0.5_04}]    )

									# Legacy behavior
									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is great !'''       ,       return_all_scores=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''LABEL_0''', '''score''': 0.5_04}]    )

									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is great !'''       ,       return_all_scores=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [[{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}]]    )

									SCREAMING_SNAKE_CASE_						 = text_classifier(['''This is great !''', '''Something else''']       ,       return_all_scores=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [
									        [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
									        [{'''label''': '''LABEL_0''', '''score''': 0.5_04}, {'''label''': '''LABEL_1''', '''score''': 0.4_96}],
									    ]       ,       )

									SCREAMING_SNAKE_CASE_						 = text_classifier(['''This is great !''', '''Something else''']       ,       return_all_scores=SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [
									        {'''label''': '''LABEL_0''', '''score''': 0.5_04},
									        {'''label''': '''LABEL_0''', '''score''': 0.5_04},
									    ]       ,       )

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''LABEL_0''', '''score''': 0.5_04}]    )

							@require_tf
							def _lowercase					(self    ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''LABEL_0''', '''score''': 0.5_04}]    )

							@slow
							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = pipeline('''text-classification'''    )

									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is great !'''    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''POSITIVE''', '''score''': 1.0}]    )
									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is bad !'''    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''NEGATIVE''', '''score''': 1.0}]    )
									SCREAMING_SNAKE_CASE_						 = text_classifier('''Birds are a type of animal'''    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''POSITIVE''', '''score''': 0.9_88}]    )

							@slow
							@require_tf
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = pipeline('''text-classification'''       ,       framework='''tf'''    )

									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is great !'''    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''POSITIVE''', '''score''': 1.0}]    )
									SCREAMING_SNAKE_CASE_						 = text_classifier('''This is bad !'''    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''NEGATIVE''', '''score''': 1.0}]    )
									SCREAMING_SNAKE_CASE_						 = text_classifier('''Birds are a type of animal'''    )
									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': '''POSITIVE''', '''score''': 0.9_88}]    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = TextClassificationPipeline(model=SCREAMING_SNAKE_CASE_       ,       tokenizer=SCREAMING_SNAKE_CASE_    )
									return text_classifier, ["HuggingFace is in", "This is another test"]







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_    )

									self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}]    )
									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(SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}, {'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}]       ,       )
									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(SCREAMING_SNAKE_CASE_       ,       top_k=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = len(model.config.idalabel.values()    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       [[{'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}] * N, [{'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}] * N]       ,       )

									SCREAMING_SNAKE_CASE_						 = {'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
									SCREAMING_SNAKE_CASE_						 = text_classifier(SCREAMING_SNAKE_CASE_    )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_    )       ,       {'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}       ,       )
									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(SCREAMING_SNAKE_CASE_    ):
											text_classifier(SCREAMING_SNAKE_CASE_    )

									# 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(SCREAMING_SNAKE_CASE_    )       ,       [{'''label''': ANY(SCREAMING_SNAKE_CASE_    ), '''score''': ANY(SCREAMING_SNAKE_CASE_    )}]       ,       )
									self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()    )

"""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


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



def        _lowerCamelCase ( __a    ):
		warnings.warn(
		    '''The preprocess method is deprecated and will be removed in a future version. Please'''
		    ''' use VaeImageProcessor.preprocess instead''',							__a,							)
		if isinstance(__a,							torch.Tensor    ):
				return image
		elif isinstance(__a,							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(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = np.array(__a    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = image.transpose(0,							3,							1,							2    )
				SCREAMING_SNAKE_CASE_						 = 2.0 * image - 1.0
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(image[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return image



def        _lowerCamelCase ( __a    ):
		if isinstance(__a,							torch.Tensor    ):
				return mask
		elif isinstance(__a,							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(__a,							axis=0    )
				SCREAMING_SNAKE_CASE_						 = mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE_						 = 0
				SCREAMING_SNAKE_CASE_						 = 1
				SCREAMING_SNAKE_CASE_						 = torch.from_numpy(__a    )
		elif isinstance(mask[0],							torch.Tensor    ):
				SCREAMING_SNAKE_CASE_						 = torch.cat(__a,							dim=0    )
		return mask





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      42
							UpperCAmelCase__						     =      42

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__()
									self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,       scheduler=SCREAMING_SNAKE_CASE_    )







							@torch.no_grad()
							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = 2_50       ,       SCREAMING_SNAKE_CASE_ = 0.0       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = 10       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = "pil"       ,       SCREAMING_SNAKE_CASE_ = True       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = image

									SCREAMING_SNAKE_CASE_						 = _preprocess_image(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = original_image.to(device=self.device       ,       dtype=self.unet.dtype    )
									SCREAMING_SNAKE_CASE_						 = _preprocess_mask(SCREAMING_SNAKE_CASE_    )
									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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) and len(SCREAMING_SNAKE_CASE_    ) != batch_size:
											raise ValueError(
											    f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_    )}, 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(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       device=self.device       ,       dtype=self.unet.dtype    )

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

									SCREAMING_SNAKE_CASE_						 = self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE_						 = generator[0] if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) 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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE_						 = self.scheduler.step(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ).prev_sample

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )