

'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 							A__   (	_UpperCAmelCase					,       unittest.TestCase  ):
		A__					=    KandinskyImgaImgPipeline
		A__					=    ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
		A__					=    [
		    'prompt',
		    'negative_prompt',
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		]
		A__					=    [
		    'generator',
		    'height',
		    'width',
		    'strength',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A__					=    False





		@property
		def 						A  (		self				:     Tuple			)     ->   int:





							'''simple docstring'''






							return 32





		@property
		def 						A  (		self				:     Optional[Any]			)     ->   Optional[int]:





							'''simple docstring'''






							return 32





		@property
		def 						A  (		self				:     Tuple			)     ->   List[str]:





							'''simple docstring'''






							return self.time_input_dim





		@property
		def 						A  (		self				:     Any			)     ->   int:





							'''simple docstring'''






							return self.time_input_dim * 4





		@property
		def 						A  (		self				:     int			)     ->   Any:





							'''simple docstring'''






							return 100





		@property
		def 						A  (		self				:     Any			)     ->   Optional[int]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'			)
							return tokenizer





		@property
		def 						A  (		self				:     int			)     ->   Tuple:





							'''simple docstring'''






							torch.manual_seed(0			)
							_SCREAMING_SNAKE_CASE							   =MCLIPConfig(
							    numDims=self.cross_attention_dim							, transformerDimensions=self.text_embedder_hidden_size							, hidden_size=self.text_embedder_hidden_size							, intermediate_size=37							, num_attention_heads=4							, num_hidden_layers=5							, vocab_size=1005							, )

							_SCREAMING_SNAKE_CASE							   =MultilingualCLIP(lowercase_			)
							_SCREAMING_SNAKE_CASE							   =text_encoder.eval()

							return text_encoder





		@property
		def 						A  (		self				:     Optional[Any]			)     ->   Union[str, Any]:





							'''simple docstring'''






							torch.manual_seed(0			)

							_SCREAMING_SNAKE_CASE							   ={
							    """in_channels""": 4,
							    # Out channels is double in channels because predicts mean and variance
							    """out_channels""": 8,
							    """addition_embed_type""": """text_image""",
							    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
							    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
							    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
							    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
							    """layers_per_block""": 1,
							    """encoder_hid_dim""": self.text_embedder_hidden_size,
							    """encoder_hid_dim_type""": """text_image_proj""",
							    """cross_attention_dim""": self.cross_attention_dim,
							    """attention_head_dim""": 4,
							    """resnet_time_scale_shift""": """scale_shift""",
							    """class_embed_type""": None,
							}

							_SCREAMING_SNAKE_CASE							   =UNetaDConditionModel(**lowercase_			)
							return model





		@property
		def 						A  (		self				:     Union[str, Any]			)     ->   Any:





							'''simple docstring'''






							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}





		@property
		def 						A  (		self				:     str			)     ->   Dict:





							'''simple docstring'''






							torch.manual_seed(0			)
							_SCREAMING_SNAKE_CASE							   =VQModel(**self.dummy_movq_kwargs			)
							return model





		def 						A  (		self				:     Dict			)     ->   Tuple:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =self.dummy_text_encoder
							_SCREAMING_SNAKE_CASE							   =self.dummy_tokenizer
							_SCREAMING_SNAKE_CASE							   =self.dummy_unet
							_SCREAMING_SNAKE_CASE							   =self.dummy_movq

							_SCREAMING_SNAKE_CASE							   ={
							    """num_train_timesteps""": 1000,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.0_00_85,
							    """beta_end""": 0.0_12,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							_SCREAMING_SNAKE_CASE							   =DDIMScheduler(**lowercase_			)

							_SCREAMING_SNAKE_CASE							   ={
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components





		def 						A  (		self				:     Optional[Any]							, _a				:     Any							, _a				:     Union[str, Any]=0			)     ->   List[Any]:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =floats_tensor((1, self.cross_attention_dim)							, rng=random.Random(lowercase_			)			).to(lowercase_			)
							_SCREAMING_SNAKE_CASE							   =floats_tensor((1, self.cross_attention_dim)							, rng=random.Random(seed + 1			)			).to(lowercase_			)
							# create init_image
							_SCREAMING_SNAKE_CASE							   =floats_tensor((1, 3, 64, 64)							, rng=random.Random(lowercase_			)			).to(lowercase_			)
							_SCREAMING_SNAKE_CASE							   =image.cpu().permute(0							, 2							, 3							, 1			)[0]
							_SCREAMING_SNAKE_CASE							   =Image.fromarray(np.uinta(lowercase_			)			).convert('RGB'			).resize((256, 256)			)

							if str(lowercase_			).startswith('mps'			):
												_SCREAMING_SNAKE_CASE							   =torch.manual_seed(lowercase_			)
							else:
												_SCREAMING_SNAKE_CASE							   =torch.Generator(device=lowercase_			).manual_seed(lowercase_			)
							_SCREAMING_SNAKE_CASE							   ={
							    """prompt""": """horse""",
							    """image""": init_image,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 10,
							    """guidance_scale""": 7.0,
							    """strength""": 0.2,
							    """output_type""": """np""",
							}
							return inputs





		def 						A  (		self				:     Tuple			)     ->   Dict:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   ="""cpu"""

							_SCREAMING_SNAKE_CASE							   =self.get_dummy_components()

							_SCREAMING_SNAKE_CASE							   =self.pipeline_class(**lowercase_			)
							_SCREAMING_SNAKE_CASE							   =pipe.to(lowercase_			)

							pipe.set_progress_bar_config(disable=lowercase_			)

							_SCREAMING_SNAKE_CASE							   =pipe(**self.get_dummy_inputs(lowercase_			)			)
							_SCREAMING_SNAKE_CASE							   =output.images

							_SCREAMING_SNAKE_CASE							   =pipe(
							    **self.get_dummy_inputs(lowercase_			)							, return_dict=lowercase_							, )[0]

							_SCREAMING_SNAKE_CASE							   =image[0, -3:, -3:, -1]
							_SCREAMING_SNAKE_CASE							   =image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							_SCREAMING_SNAKE_CASE							   =np.array(
							    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]			)
							assert (
							    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
							), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
							), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







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





		def 						A  (		self				:     Dict			)     ->   Union[str, Any]:





							'''simple docstring'''






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def 						A  (		self				:     Optional[Any]			)     ->   Tuple:





							'''simple docstring'''






							_SCREAMING_SNAKE_CASE							   =load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinsky/kandinsky_img2img_frog.npy'			)

							_SCREAMING_SNAKE_CASE							   =load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'			)
							_SCREAMING_SNAKE_CASE							   ="""A red cartoon frog, 4k"""

							_SCREAMING_SNAKE_CASE							   =KandinskyPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1-prior'							, torch_dtype=torch.floataa			)
							pipe_prior.to(lowercase_			)

							_SCREAMING_SNAKE_CASE							   =KandinskyImgaImgPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-1'							, torch_dtype=torch.floataa			)
							_SCREAMING_SNAKE_CASE							   =pipeline.to(lowercase_			)

							pipeline.set_progress_bar_config(disable=lowercase_			)

							_SCREAMING_SNAKE_CASE							   =torch.Generator(device='cpu'			).manual_seed(0			)
							_SCREAMING_SNAKE_CASE							   =pipe_prior(
							    lowercase_							, generator=lowercase_							, num_inference_steps=5							, negative_prompt=''							, ).to_tuple()

							_SCREAMING_SNAKE_CASE							   =pipeline(
							    lowercase_							, image=lowercase_							, image_embeds=lowercase_							, negative_image_embeds=lowercase_							, generator=lowercase_							, num_inference_steps=100							, height=768							, width=768							, strength=0.2							, output_type='np'							, )

							_SCREAMING_SNAKE_CASE							   =output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(lowercase_							, lowercase_			)



'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowerCamelCase			:				List[Any] 			=					"\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

lowerCamelCase			:				List[str] 			=					"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

lowerCamelCase			:				Dict 			=					"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Dict    )				->      List[str]:
     """simple docstring"""




     return float((preds == labels).mean()    )


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Any    )				->      Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =simple_accuracy(_UpperCamelCase							,			_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =float(fa_score(y_true=_UpperCamelCase							,			y_pred=_UpperCamelCase    )    )
     return {
         "accuracy": acc,
         "f1": fa,
     }


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[int]    )				->      Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =float(pearsonr(_UpperCamelCase							,			_UpperCamelCase    )[0]    )
     _SCREAMING_SNAKE_CASE							   =float(spearmanr(_UpperCamelCase							,			_UpperCamelCase    )[0]    )
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
     }







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





  def 						A  (		self				:     Optional[int]			)     ->   Any:





       '''simple docstring'''






       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'			)
       return datasets.MetricInfo(
           description=_DESCRIPTION							, citation=_CITATION							, inputs_description=_KWARGS_DESCRIPTION							, features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'			),
                   'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'			),
               }			)							, codebase_urls=[]							, reference_urls=[]							, format='numpy'							, )





  def 						A  (		self				:     str							, _a				:     str							, _a				:     List[Any]			)     ->   str:





       '''simple docstring'''






       if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(_a							, _a			)}
       elif self.config_name == "stsb":
            return pearson_and_spearman(_a							, _a			)
       elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(_a							, _a			)
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(_a							, _a			)}
       else:
            raise KeyError(
                'You should supply a configuration name selected in '
                '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'			)
