

"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class    _lowerCAmelCase    (      lowercase	,unittest.TestCase  ):







 """simple docstring"""







 __UpperCAmelCase      :   Optional[int]  						=      PaintByExamplePipeline
 __UpperCAmelCase      :   List[str]  						=      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __UpperCAmelCase      :   int  						=      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __UpperCAmelCase      :   Optional[int]  						=      frozenset([]  )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


 def    _lowercase					(     self				:     Any      ):
    torch.manual_seed(0      )
    __lowercase								=		UNetaDConditionModel(
        block_out_channels=(3_2, 6_4),   layers_per_block=2,   sample_size=3_2,   in_channels=9,   out_channels=4,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),   cross_attention_dim=3_2,   )
    __lowercase								=		PNDMScheduler(skip_prk_steps=UpperCAmelCase__      )
    torch.manual_seed(0      )
    __lowercase								=		AutoencoderKL(
        block_out_channels=[3_2, 6_4],   in_channels=3,   out_channels=3,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],   latent_channels=4,   )
    torch.manual_seed(0      )
    __lowercase								=		CLIPVisionConfig(
        hidden_size=3_2,   projection_dim=3_2,   intermediate_size=3_7,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   image_size=3_2,   patch_size=4,   )
    __lowercase								=		PaintByExampleImageEncoder(UpperCAmelCase__,   proj_size=3_2      )
    __lowercase								=		CLIPImageProcessor(crop_size=3_2,   size=3_2      )

    __lowercase								=		{
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "image_encoder": image_encoder,
        "safety_checker": None,
        "feature_extractor": feature_extractor,
    }
    return components


 def    _lowercase					(     self				:     List[Any],   UpperCAmelCase__				:     Any      ):
    __lowercase								=		np.array(image.convert("RGB"      )      )
    __lowercase								=		image[None].transpose(0,   3,   1,   2      )
    __lowercase								=		torch.from_numpy(UpperCAmelCase__      ).to(dtype=torch.floataa      ) / 127.5 - 1.0
    return image


 def    _lowercase					(     self				:     Optional[int],   UpperCAmelCase__				:     Dict="cpu",   UpperCAmelCase__				:     Tuple=0      ):
    # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
    __lowercase								=		floats_tensor((1, 3, 3_2, 3_2),   rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
    __lowercase								=		image.cpu().permute(0,   2,   3,   1      )[0]
    __lowercase								=		Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
    __lowercase								=		Image.fromarray(np.uinta(image + 4      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
    __lowercase								=		Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert("RGB"      ).resize((3_2, 3_2)      )

    if str(UpperCAmelCase__      ).startswith("mps"      ):
       __lowercase								=		torch.manual_seed(UpperCAmelCase__      )
    else:
       __lowercase								=		torch.Generator(device=UpperCAmelCase__      ).manual_seed(UpperCAmelCase__      )
    __lowercase								=		{
        "example_image": example_image,
        "image": init_image,
        "mask_image": mask_image,
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }
    return inputs


 def    _lowercase					(     self				:     Dict      ):
    __lowercase								=		self.get_dummy_components()

    # make sure here that pndm scheduler skips prk
    __lowercase								=		PaintByExamplePipeline(**UpperCAmelCase__      )
    __lowercase								=		pipe.to("cpu"      )
    pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

    __lowercase								=		self.get_dummy_inputs()
    __lowercase								=		pipe(**UpperCAmelCase__      )
    __lowercase								=		output.images

    __lowercase								=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 6_4, 6_4, 3)
    __lowercase								=		np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]      )

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2


 def    _lowercase					(     self				:     List[Any]      ):
    __lowercase								=		"cpu"
    __lowercase								=		self.get_dummy_inputs()
    inputs.pop("mask_image"      )
    __lowercase								=		self.convert_to_pt(inputs.pop("image"      )      )
    __lowercase								=		image.clamp(0,   1      ) / 2

    # make sure here that pndm scheduler skips prk
    __lowercase								=		PaintByExamplePipeline(**self.get_dummy_components()      )
    __lowercase								=		pipe.to(UpperCAmelCase__      )
    pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

    __lowercase								=		pipe(image=UpperCAmelCase__,   mask_image=mask_image[:, 0],   **UpperCAmelCase__      )
    __lowercase								=		output.images

    __lowercase								=		image.cpu().permute(0,   2,   3,   1      )[0]
    __lowercase								=		mask_image.cpu().permute(0,   2,   3,   1      )[0]

    __lowercase								=		Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert("RGB"      )
    __lowercase								=		Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert("RGB"      )

    __lowercase								=		pipe(**self.get_dummy_inputs()      )
    __lowercase								=		output.images

    assert out_a.shape == (1, 6_4, 6_4, 3)
    assert np.abs(out_a.flatten() - out_a.flatten()      ).max() < 5E-2



 def    _lowercase					(     self				:     List[Any]      ):
    super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







@slow
@require_torch_gpu
class    _lowerCAmelCase    (      unittest.TestCase  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Any      ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



 def    _lowercase					(     self				:     List[Any]      ):
    # make sure here that pndm scheduler skips prk
    __lowercase								=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/paint_by_example/dog_in_bucket.png"      )
    __lowercase								=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/paint_by_example/mask.png"      )
    __lowercase								=		load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/paint_by_example/panda.jpg"      )

    __lowercase								=		PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"      )
    __lowercase								=		pipe.to(UpperCAmelCase__      )
    pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

    __lowercase								=		torch.manual_seed(3_2_1      )
    __lowercase								=		pipe(
        image=UpperCAmelCase__,   mask_image=UpperCAmelCase__,   example_image=UpperCAmelCase__,   generator=UpperCAmelCase__,   guidance_scale=5.0,   num_inference_steps=5_0,   output_type="np",   )

    __lowercase								=		output.images

    __lowercase								=		image[0, -3:, -3:, -1]

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=		np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]      )

    assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2







"""simple docstring"""





import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_a					 =				'\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = "Text Chunking using Transformation-Based Learning",\n    author = "Ramshaw, Lance  and\n      Marcus, Mitch",\n    booktitle = "Third Workshop on Very Large Corpora",\n    year = "1995",\n    url = "https://www.aclweb.org/anthology/W95-0107",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n'

_a					 =				'\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n'

_a					 =				'\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode="strict". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        "warn". "warn" acts as 0, but the warning is raised.\n\nReturns:\n    \'scores\': dict. Summary of the scores for overall and per type\n        Overall:\n            \'accuracy\': accuracy,\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            \'precision\': precision,\n            \'recall\': recall,\n            \'f1\': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]\n    >>> seqeval = datasets.load_metric("seqeval")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']\n    >>> print(results["overall_f1"])\n    0.5\n    >>> print(results["PER"]["f1"])\n    1.0\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,_KWARGS_DESCRIPTION  )
class    _lowerCAmelCase    (      datasets.Metric  ):







 """simple docstring"""







 def    _lowercase					(     self				:     Optional[int]      ):
    return datasets.MetricInfo(
        description=_DESCRIPTION,   citation=_CITATION,   homepage="https://github.com/chakki-works/seqeval",   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
            {
                "predictions": datasets.Sequence(datasets.Value("string",   id="label"      ),   id="sequence"      ),
                "references": datasets.Sequence(datasets.Value("string",   id="label"      ),   id="sequence"      ),
            }      ),   codebase_urls=["https://github.com/chakki-works/seqeval"],   reference_urls=["https://github.com/chakki-works/seqeval"],   )



 def    _lowercase					(     self				:     Optional[Any],   UpperCAmelCase__				:     List[str],   UpperCAmelCase__				:     str,   UpperCAmelCase__				:     bool = False,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[str] = None,   UpperCAmelCase__				:     Optional[List[int]] = None,   UpperCAmelCase__				:     Union[str, int] = "warn",   ):
    if scheme is not None:
       try:
          __lowercase								=		importlib.import_module("seqeval.scheme"      )
          __lowercase								=		getattr(UpperCAmelCase__,   UpperCAmelCase__      )
       except AttributeError:
          raise ValueError(F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""      )
    __lowercase								=		classification_report(
        y_true=UpperCAmelCase__,   y_pred=UpperCAmelCase__,   suffix=UpperCAmelCase__,   output_dict=UpperCAmelCase__,   scheme=UpperCAmelCase__,   mode=UpperCAmelCase__,   sample_weight=UpperCAmelCase__,   zero_division=UpperCAmelCase__,   )
    report.pop("macro avg"      )
    report.pop("weighted avg"      )
    __lowercase								=		report.pop("micro avg"      )

    __lowercase								=		{
        type_name: {
            "precision": score["precision"],
            "recall": score["recall"],
            "f1": score["f1-score"],
            "number": score["support"],
        }
        for type_name, score in report.items()
    }
    __lowercase								=		overall_score["precision"]
    __lowercase								=		overall_score["recall"]
    __lowercase								=		overall_score["f1-score"]
    __lowercase								=		accuracy_score(y_true=UpperCAmelCase__,   y_pred=UpperCAmelCase__      )

    return scores




