'''simple docstring'''
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


a=	'''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

a=	'''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


a=	'''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''






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







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         return datasets.MetricInfo(
             description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
                 {
                     'predictions': datasets.Value('string'     ),
                     'references': datasets.Value('string'     ),
                 }     )  ,      homepage='https://github.com/moussaKam/FrugalScore'  ,      )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         if self.config_name == "default":
             __UpperCamelCase						:						int		     =	'moussaKam/frugalscore_tiny_bert-base_bert-score'
         else:
             __UpperCamelCase						:						str		     =	self.config_name
         __UpperCamelCase						:						Dict		     =	AutoModelForSequenceClassification.from_pretrained(_A     )
         __UpperCamelCase						:						Optional[Any]		     =	AutoTokenizer.from_pretrained(_A     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=1_2_8  ,      _lowerCamelCase=None  ,      ):
         assert len(_A     ) == len(
             _A     ), "predictions and references should have the same number of sentences."
         if device is not None:
             assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
         else:
             __UpperCamelCase						:						List[Any]		     =	'gpu' if torch.cuda.is_available() else 'cpu'
         __UpperCamelCase						:						Any		     =	TrainingArguments(
             'trainer'  ,      fpaa=(device == 'gpu')  ,      per_device_eval_batch_size=_A  ,      report_to='all'  ,      no_cuda=(device == 'cpu')  ,      log_level='warning'  ,      )
         __UpperCamelCase						:						Dict		     =	{'sentence1': predictions, 'sentence2': references}
         __UpperCamelCase						:						str		     =	datasets.Dataset.from_dict(_A     )

         def tokenize_function(_lowerCamelCase     ):
             return self.tokenizer(
                 data['sentence1']  ,      data['sentence2']  ,      max_length=_A  ,      truncation=_A  ,      padding=_A     )

         __UpperCamelCase						:						str		     =	raw_datasets.map(_A  ,      batched=_A     )
         tokenized_datasets.remove_columns(['sentence1', 'sentence2']     )
         __UpperCamelCase						:						Optional[int]		     =	Trainer(self.model  ,      _A  ,      tokenizer=self.tokenizer     )
         __UpperCamelCase						:						Dict		     =	trainer.predict(_A     )
         return {"scores": list(predictions.predictions.squeeze(-1     )     )}






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class      snake_case_      (					_a      ,			_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =StableDiffusionParadigmsPipeline
							__UpperCAmelCase      =TEXT_TO_IMAGE_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_BATCH_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_IMAGE_PARAMS




							def 							A__	(			self				):
												torch.manual_seed(0				)
												__lowerCAmelCase       =				UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)       ,    layers_per_block=2       ,    sample_size=3_2       ,    in_channels=4       ,    out_channels=4       ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,    cross_attention_dim=3_2       ,    attention_head_dim=(2, 4)       ,    use_linear_projection=_A       ,    )
												__lowerCAmelCase       =				DDIMParallelScheduler(
												    beta_start=0.0_0085       ,    beta_end=0.012       ,    beta_schedule='scaled_linear'       ,    clip_sample=_A       ,    set_alpha_to_one=_A       ,    )
												torch.manual_seed(0				)
												__lowerCAmelCase       =				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       ,    sample_size=1_2_8       ,    )
												torch.manual_seed(0				)
												__lowerCAmelCase       =				CLIPTextConfig(
												    bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=3_2       ,    intermediate_size=3_7       ,    layer_norm_eps=1e-0_5       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1_0_0_0       ,    hidden_act='gelu'       ,    projection_dim=5_1_2       ,    )
												__lowerCAmelCase       =				CLIPTextModel(_A				)
												__lowerCAmelCase       =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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




							def 							A__	(			self       ,    _A       ,    _A=0				):
												if str(_A				).startswith('mps'				):
																	__lowerCAmelCase       =				torch.manual_seed(_A				)
												else:
																	__lowerCAmelCase       =				torch.Generator(device=_A				).manual_seed(_A				)
												__lowerCAmelCase       =				{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'generator': generator,
												    'num_inference_steps': 1_0,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'parallel': 3,
												    'debug': True,
												}
												return inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				sd_pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]
												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]				)

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												torch.manual_seed(0				)
												__lowerCAmelCase       =				DDPMParallelScheduler()
												torch.manual_seed(0				)
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				sd_pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]
												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]				)

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




							def 							A__	(			self				):
												super().test_inference_batch_consistent(batch_sizes=[1, 2]				)




							def 							A__	(			self				):
												super().test_inference_batch_single_identical(batch_size=2       ,    expected_max_diff=3e-3				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				'french fries'
												__lowerCAmelCase       =				sd_pipe(**_A       ,    negative_prompt=_A				)
												__lowerCAmelCase       =				output.images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]

												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]				)

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





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


							"""simple docstring"""





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




							def 							A__	(			self       ,    _A=0				):
												__lowerCAmelCase       =				torch.Generator(device=_A				).manual_seed(_A				)
												__lowerCAmelCase       =				{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'generator': generator,
												    'num_inference_steps': 1_0,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												    'parallel': 3,
												    'debug': True,
												}
												return inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				'stabilityai/stable-diffusion-2-base'
												__lowerCAmelCase       =				DDIMParallelScheduler.from_pretrained(_A       ,    subfolder='scheduler'				)
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline.from_pretrained(_A       ,    scheduler=_A       ,    safety_checker=_A				)
												pipe.to(_A				)
												pipe.set_progress_bar_config(disable=_A				)
												pipe.enable_attention_slicing()

												__lowerCAmelCase       =				self.get_inputs()
												__lowerCAmelCase       =				pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)

												__lowerCAmelCase       =				np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]				)

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






