



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=AltDiffusionPipeline
		snake_case_			=TEXT_TO_IMAGE_PARAMS
		snake_case_			=TEXT_TO_IMAGE_BATCH_PARAMS
		snake_case_			=TEXT_TO_IMAGE_IMAGE_PARAMS
		snake_case_			=TEXT_TO_IMAGE_IMAGE_PARAMS



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						torch.manual_seed(0				)
						lowerCAmelCase__  :       int				=  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__  :       List[str]				=  DDIMScheduler(
						    beta_start=0.0_0085	,beta_end=0.012	,beta_schedule='''scaled_linear'''	,clip_sample=__lowerCamelCase	,set_alpha_to_one=__lowerCamelCase	,)
						torch.manual_seed(0				)
						lowerCAmelCase__  :       Optional[Any]				=  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	,)

						# TODO: address the non-deterministic text encoder (fails for save-load tests)
						# torch.manual_seed(0)
						# text_encoder_config = RobertaSeriesConfig(
						#     hidden_size=32,
						#     project_dim=32,
						#     intermediate_size=37,
						#     layer_norm_eps=1e-05,
						#     num_attention_heads=4,
						#     num_hidden_layers=5,
						#     vocab_size=5002,
						# )
						# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

						torch.manual_seed(0				)
						lowerCAmelCase__  :       Dict				=  CLIPTextConfig(
						    bos_token_id=0	,eos_token_id=2	,hidden_size=32	,projection_dim=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=50_02	,)
						lowerCAmelCase__  :       int				=  CLIPTextModel(__lowerCamelCase				)

						lowerCAmelCase__  :       str				=  XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''				)
						lowerCAmelCase__  :       Union[str, Any]				=  77

						lowerCAmelCase__  :       str				=  {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''safety_checker''': None,
						    '''feature_extractor''': None,
						}
						return components



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=0				)  ->    Optional[Any]:

						"""simple docstring"""






						if str(__lowerCamelCase				).startswith('''mps'''				):
										lowerCAmelCase__  :       Tuple				=  torch.manual_seed(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       Any				=  torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  {
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''guidance_scale''': 6.0,
						    '''output_type''': '''numpy''',
						}
						return inputs



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						super().test_inference_batch_single_identical(expected_max_diff=3e-3				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

						lowerCAmelCase__  :       int				=  self.get_dummy_components()
						torch.manual_seed(0				)
						lowerCAmelCase__  :       str				=  RobertaSeriesConfig(
						    hidden_size=32	,project_dim=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,vocab_size=50_02	,)
						# TODO: remove after fixing the non-deterministic text encoder
						lowerCAmelCase__  :       Any				=  RobertaSeriesModelWithTransformation(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  text_encoder

						lowerCAmelCase__  :       List[Any]				=  AltDiffusionPipeline(**__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       List[str]				=  self.get_dummy_inputs(__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  '''A photo of an astronaut'''
						lowerCAmelCase__  :       Any				=  alt_pipe(**__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  output.images
						lowerCAmelCase__  :       List[Any]				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCAmelCase__  :       Optional[Any]				=  np.array(
						    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]				)

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



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

						lowerCAmelCase__  :       str				=  self.get_dummy_components()
						lowerCAmelCase__  :       Optional[Any]				=  PNDMScheduler(skip_prk_steps=__lowerCamelCase				)
						torch.manual_seed(0				)
						lowerCAmelCase__  :       int				=  RobertaSeriesConfig(
						    hidden_size=32	,project_dim=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,vocab_size=50_02	,)
						# TODO: remove after fixing the non-deterministic text encoder
						lowerCAmelCase__  :       Tuple				=  RobertaSeriesModelWithTransformation(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  text_encoder
						lowerCAmelCase__  :       List[Any]				=  AltDiffusionPipeline(**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  alt_pipe(**__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  output.images
						lowerCAmelCase__  :       str				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCAmelCase__  :       Union[str, Any]				=  np.array(
						    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]				)

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



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''	,safety_checker=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  '''A painting of a squirrel eating a burger'''
						lowerCAmelCase__  :       List[Any]				=  torch.manual_seed(0				)
						lowerCAmelCase__  :       str				=  alt_pipe([prompt]	,generator=__lowerCamelCase	,guidance_scale=6.0	,num_inference_steps=20	,output_type='''np'''				)

						lowerCAmelCase__  :       str				=  output.images

						lowerCAmelCase__  :       int				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Dict				=  np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]				)

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



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''	,subfolder='''scheduler'''				)

						lowerCAmelCase__  :       List[str]				=  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''	,scheduler=__lowerCamelCase	,safety_checker=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  '''A painting of a squirrel eating a burger'''
						lowerCAmelCase__  :       Optional[Any]				=  torch.manual_seed(0				)

						lowerCAmelCase__  :       List[str]				=  alt_pipe([prompt]	,generator=__lowerCamelCase	,num_inference_steps=2	,output_type='''numpy'''				)
						lowerCAmelCase__  :       List[str]				=  output.images

						lowerCAmelCase__  :       Optional[int]				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       List[Any]				=  np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]				)

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






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

import datasets


__snake_case       :	str            ='\\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'

__snake_case       :	Tuple            ='\\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'

__snake_case       :	int            ='\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__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  simple_accuracy(lowerCamelCase_ ,lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  float(fa_score(y_true=lowerCamelCase_ ,y_pred=lowerCamelCase_))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  float(pearsonr(lowerCamelCase_ ,lowerCamelCase_)[0])
				lowerCAmelCase__  :       int				=  float(spearmanr(lowerCamelCase_ ,lowerCamelCase_)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Union[str, 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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						if self.config_name == "cola":
										return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase	,__lowerCamelCase				)}
						elif self.config_name == "stsb":
										return pearson_and_spearman(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["mrpc", "qqp"]:
										return acc_and_fa(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
										return {"accuracy": simple_accuracy(__lowerCamelCase	,__lowerCamelCase				)}
						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"]'''				)

