
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			UpperCamelCase_				(  lowerCamelCase_					,       lowerCamelCase_					,       lowerCamelCase_					,       unittest.TestCase     ):




      '''simple docstring'''







      a		:Tuple						 =      StableDiffusionImageVariationPipeline
      a		:Any						 =      IMAGE_VARIATION_PARAMS
      a		:Tuple						 =      IMAGE_VARIATION_BATCH_PARAMS
      a		:List[Any]						 =      frozenset([]     )
      # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      a		:Optional[Any]						 =      frozenset([]     )







      def lowercase__		( self):
            torch.manual_seed(0)
            lowerCAmelCase_						=      UNetaDConditionModel(
                block_out_channels=(32, 64)					,					layers_per_block=2					,					sample_size=32					,					in_channels=4					,					out_channels=4					,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,					cross_attention_dim=32					,					)
            lowerCAmelCase_						=      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
            torch.manual_seed(0)
            lowerCAmelCase_						=      AutoencoderKL(
                block_out_channels=[32, 64]					,					in_channels=3					,					out_channels=3					,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,					latent_channels=4					,					)
            torch.manual_seed(0)
            lowerCAmelCase_						=      CLIPVisionConfig(
                hidden_size=32					,					projection_dim=32					,					intermediate_size=37					,					layer_norm_eps=1E-05					,					num_attention_heads=4					,					num_hidden_layers=5					,					image_size=32					,					patch_size=4					,					)
            lowerCAmelCase_						=      CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      CLIPImageProcessor(crop_size=32					,					size=32)

            lowerCAmelCase_						=      {
                '''unet''': unet,
                '''scheduler''': scheduler,
                '''vae''': vae,
                '''image_encoder''': image_encoder,
                '''feature_extractor''': feature_extractor,
                '''safety_checker''': None,
            }
            return components







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase=0):
            lowerCAmelCase_						=      floats_tensor((1, 3, 32, 32)					,					rng=random.Random(__SCREAMING_SNAKE_CASE))
            lowerCAmelCase_						=      image.cpu().permute(0					,					2					,					3					,					1)[0]
            lowerCAmelCase_						=      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''').resize((32, 32))
            if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
                  lowerCAmelCase_						=      torch.manual_seed(__SCREAMING_SNAKE_CASE)
            else:
                  lowerCAmelCase_						=      torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      {
                '''image''': image,
                '''generator''': generator,
                '''num_inference_steps''': 2,
                '''guidance_scale''': 6.0,
                '''output_type''': '''numpy''',
            }
            return inputs







      def lowercase__		( self):
            lowerCAmelCase_						=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            lowerCAmelCase_						=      self.get_dummy_components()
            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      sd_pipe.to(__SCREAMING_SNAKE_CASE)
            sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      sd_pipe(**__SCREAMING_SNAKE_CASE).images
            lowerCAmelCase_						=      image[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            lowerCAmelCase_						=      np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921])

            assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
            lowerCAmelCase_						=      self.get_dummy_components()
            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline(**__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      sd_pipe.to(__SCREAMING_SNAKE_CASE)
            sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      2 * [inputs['''image''']]
            lowerCAmelCase_						=      sd_pipe(**__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      output.images

            lowerCAmelCase_						=      image[-1, -3:, -3:, -1]

            assert image.shape == (2, 64, 64, 3)
            lowerCAmelCase_						=      np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289])

            assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3







      def lowercase__		( self):
            super().test_inference_batch_single_identical(expected_max_diff=3E-3)


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




      '''simple docstring'''







      def lowercase__		( self):
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase="cpu"					,					_UpperCAmelCase=torch.floataa					,					_UpperCAmelCase=0):
            lowerCAmelCase_						=      torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      load_image(
                '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
                '''/stable_diffusion_imgvar/input_image_vermeer.png''')
            lowerCAmelCase_						=      np.random.RandomState(__SCREAMING_SNAKE_CASE).standard_normal((1, 4, 64, 64))
            lowerCAmelCase_						=      torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE					,					dtype=__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      {
                '''image''': init_image,
                '''latents''': latents,
                '''generator''': generator,
                '''num_inference_steps''': 3,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs







      def lowercase__		( self):
            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline.from_pretrained(
                '''lambdalabs/sd-image-variations-diffusers'''					,					safety_checker=__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      sd_pipe.to(__SCREAMING_SNAKE_CASE)
            sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      self.get_inputs(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      sd_pipe(**__SCREAMING_SNAKE_CASE).images
            lowerCAmelCase_						=      image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 512, 512, 3)
            lowerCAmelCase_						=      np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379])
            assert np.abs(image_slice - expected_slice).max() < 6E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      0

            def callback_fn(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase) -> None:
                  lowerCAmelCase_						=      True
                  nonlocal number_of_steps
                  number_of_steps += 1
                  if step == 1:
                        lowerCAmelCase_						=      latents.detach().cpu().numpy()
                        assert latents.shape == (1, 4, 64, 64)
                        lowerCAmelCase_						=      latents[0, -3:, -3:, -1]
                        lowerCAmelCase_						=      np.array(
                            [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309])

                        assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2
                  elif step == 2:
                        lowerCAmelCase_						=      latents.detach().cpu().numpy()
                        assert latents.shape == (1, 4, 64, 64)
                        lowerCAmelCase_						=      latents[0, -3:, -3:, -1]
                        lowerCAmelCase_						=      np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273])

                        assert np.abs(latents_slice.flatten() - expected_slice).max() < 5E-2

            lowerCAmelCase_						=      False

            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline.from_pretrained(
                '''fusing/sd-image-variations-diffusers'''					,					safety_checker=__SCREAMING_SNAKE_CASE					,					torch_dtype=torch.floataa					,					)
            pipe.to(__SCREAMING_SNAKE_CASE)
            pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
            pipe.enable_attention_slicing()

            lowerCAmelCase_						=      self.get_inputs(__SCREAMING_SNAKE_CASE					,					dtype=torch.floataa)
            pipe(**__SCREAMING_SNAKE_CASE					,					callback=__SCREAMING_SNAKE_CASE					,					callback_steps=1)
            assert callback_fn.has_been_called
            assert number_of_steps == inputs["num_inference_steps"]







      def lowercase__		( self):
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()
            torch.cuda.reset_peak_memory_stats()

            lowerCAmelCase_						=      '''fusing/sd-image-variations-diffusers'''
            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline.from_pretrained(
                __SCREAMING_SNAKE_CASE					,					safety_checker=__SCREAMING_SNAKE_CASE					,					torch_dtype=torch.floataa)
            lowerCAmelCase_						=      pipe.to(__SCREAMING_SNAKE_CASE)
            pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)
            pipe.enable_attention_slicing(1)
            pipe.enable_sequential_cpu_offload()

            lowerCAmelCase_						=      self.get_inputs(__SCREAMING_SNAKE_CASE					,					dtype=torch.floataa)
            lowerCAmelCase_						=      pipe(**__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      torch.cuda.max_memory_allocated()
            # make sure that less than 2.6 GB is allocated
            assert mem_bytes < 2.6 * 10**9


@nightly
@require_torch_gpu
class 			UpperCamelCase_				(  unittest.TestCase     ):




      '''simple docstring'''







      def lowercase__		( self):
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase="cpu"					,					_UpperCAmelCase=torch.floataa					,					_UpperCAmelCase=0):
            lowerCAmelCase_						=      torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      load_image(
                '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
                '''/stable_diffusion_imgvar/input_image_vermeer.png''')
            lowerCAmelCase_						=      np.random.RandomState(__SCREAMING_SNAKE_CASE).standard_normal((1, 4, 64, 64))
            lowerCAmelCase_						=      torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE					,					dtype=__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      {
                '''image''': init_image,
                '''latents''': latents,
                '''generator''': generator,
                '''num_inference_steps''': 50,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs







      def lowercase__		( self):
            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''')
            sd_pipe.to(__SCREAMING_SNAKE_CASE)
            sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      self.get_inputs(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      sd_pipe(**__SCREAMING_SNAKE_CASE).images[0]

            lowerCAmelCase_						=      load_numpy(
                '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
                '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy''')
            lowerCAmelCase_						=      np.abs(expected_image - image).max()
            assert max_diff < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''')
            lowerCAmelCase_						=      DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
            sd_pipe.to(__SCREAMING_SNAKE_CASE)
            sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      self.get_inputs(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      25
            lowerCAmelCase_						=      sd_pipe(**__SCREAMING_SNAKE_CASE).images[0]

            lowerCAmelCase_						=      load_numpy(
                '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
                '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy''')
            lowerCAmelCase_						=      np.abs(expected_image - image).max()
            assert max_diff < 1E-3





'''simple docstring'''







import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_lowercase					:    Optional[int]       				=			"\\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"

_lowercase					:    List[Any]       				=			"\\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"

_lowercase					:    int       				=			"\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	):






					def       _snake_case       (  self						):


											"""simple docstring"""





											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       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = "warn"							,				):


											"""simple docstring"""





											if scheme is not None:
																	try:
																							lowercase_      :	List[str]						=			importlib.import_module('''seqeval.scheme'''						)
																							lowercase_      :	int						=			getattr(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
																	except AttributeError:
																							raise ValueError(F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'''						)
											lowercase_      :	List[Any]						=			classification_report(
											    y_true=__SCREAMING_SNAKE_CASE							,				y_pred=__SCREAMING_SNAKE_CASE							,				suffix=__SCREAMING_SNAKE_CASE							,				output_dict=__SCREAMING_SNAKE_CASE							,				scheme=__SCREAMING_SNAKE_CASE							,				mode=__SCREAMING_SNAKE_CASE							,				sample_weight=__SCREAMING_SNAKE_CASE							,				zero_division=__SCREAMING_SNAKE_CASE							,				)
											report.pop('''macro avg'''						)
											report.pop('''weighted avg'''						)
											lowercase_      :	int						=			report.pop('''micro avg'''						)

											lowercase_      :	List[Any]						=			{
											    type_name: {
											        '''precision''': score['''precision'''],
											        '''recall''': score['''recall'''],
											        '''f1''': score['''f1-score'''],
											        '''number''': score['''support'''],
											    }
											    for type_name, score in report.items()
											}
											lowercase_      :	Optional[Any]						=			overall_score['''precision''']
											lowercase_      :	Optional[Any]						=			overall_score['''recall''']
											lowercase_      :	Optional[int]						=			overall_score['''f1-score''']
											lowercase_      :	Optional[int]						=			accuracy_score(y_true=__SCREAMING_SNAKE_CASE							,				y_pred=__SCREAMING_SNAKE_CASE						)

											return scores



