



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowerCamelCase__			= """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

lowerCamelCase__			= """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

lowerCamelCase__			= """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""






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





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											return datasets.MetricInfo(
											    description=_DESCRIPTION      ,				citation=_CITATION      ,				inputs_description=_KWARGS_DESCRIPTION      ,				features=datasets.Features(
											        {
											            """predictions""": datasets.Sequence(datasets.Value("""string"""      ,				id="""token"""	)      ,				id="""sequence"""	),
											            """references""": datasets.Sequence(
											                datasets.Sequence(datasets.Value("""string"""      ,				id="""token"""	)      ,				id="""sequence"""	)      ,				id="""references"""	),
											        }	)      ,				codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]      ,				reference_urls=[
											        """https://en.wikipedia.org/wiki/BLEU""",
											        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
											    ]      ,				)







							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Tuple      ,				__lowercase		:		str=4      ,				__lowercase		:		int=False	):

											'''simple docstring'''
											__a       =		compute_bleu(
											    reference_corpus=__lowercase      ,				translation_corpus=__lowercase      ,				max_order=__lowercase      ,				smooth=__lowercase	)
											((__a)     ,					(__a)     ,					(__a)     ,					(__a)     ,					(__a)     ,					(__a))       =		score
											return {
											    "bleu": bleu,
											    "precisions": precisions,
											    "brevity_penalty": bp,
											    "length_ratio": ratio,
											    "translation_length": translation_length,
											    "reference_length": reference_length,
											}





import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							@property
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		1
											__a       =		3
											__a       =		(32, 32)

											__a       =		floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0	)	).to(__lowercase	)
											return image





							@property
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		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      ,				)
											return model





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		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      ,				)
											return model





							@property
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											torch.manual_seed(0	)
											__a       =		CLIPTextConfig(
											    bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=32      ,				intermediate_size=37      ,				layer_norm_eps=1E-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1000      ,				)
											return CLIPTextModel(__lowercase	)





							@property
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											def extract(*__lowercase		:		Tuple      ,				**__lowercase		:		Dict	):






															class    SCREAMING_SNAKE_CASE			:





																						def __init__(   self		:		List[str]	):

																										'''simple docstring'''
																										__a       =		torch.ones([0]	)







																						def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		str	):

																										'''simple docstring'''
																										self.pixel_values.to(__lowercase	)
																										return self



															return Out()

											return extract





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__a       =		self.dummy_cond_unet
											__a       =		DDIMScheduler(
											    beta_start=0.00085      ,				beta_end=0.012      ,				beta_schedule="""scaled_linear"""      ,				clip_sample=__lowercase      ,				set_alpha_to_one=__lowercase      ,				)

											__a       =		self.dummy_vae
											__a       =		self.dummy_text_encoder
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

											# make sure here that pndm scheduler skips prk
											__a       =		StableDiffusionPipeline(
											    unet=__lowercase      ,				scheduler=__lowercase      ,				vae=__lowercase      ,				text_encoder=__lowercase      ,				tokenizer=__lowercase      ,				safety_checker=__lowercase      ,				feature_extractor=self.dummy_extractor      ,				)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""A painting of a squirrel eating a burger"""

											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe([prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""	)
											__a       =		output.images

											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""      ,				return_dict=__lowercase      ,				)[0]

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

											assert image.shape == (1, 64, 64, 3)
											__a       =		np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]	)

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





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											__a       =		self.dummy_cond_unet
											__a       =		PNDMScheduler(skip_prk_steps=__lowercase	)
											__a       =		self.dummy_vae
											__a       =		self.dummy_text_encoder
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

											# make sure here that pndm scheduler skips prk
											__a       =		StableDiffusionPipeline(
											    unet=__lowercase      ,				scheduler=__lowercase      ,				vae=__lowercase      ,				text_encoder=__lowercase      ,				tokenizer=__lowercase      ,				safety_checker=__lowercase      ,				feature_extractor=self.dummy_extractor      ,				)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""A painting of a squirrel eating a burger"""
											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe([prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""	)

											__a       =		output.images

											__a       =		torch.Generator(device=__lowercase	).manual_seed(0	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=6.0      ,				num_inference_steps=2      ,				output_type="""np"""      ,				return_dict=__lowercase      ,				)[0]

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

											assert image.shape == (1, 64, 64, 3)
											__a       =		np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]	)

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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained(
											    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""      ,				safety_checker=__lowercase	)
											assert isinstance(__lowercase      ,				__lowercase	)
											assert isinstance(pipe.scheduler      ,				__lowercase	)
											assert pipe.safety_checker is None

											__a       =		pipe("""example prompt"""      ,				num_inference_steps=2	).images[0]
											assert image is not None

											# check that there's no error when saving a pipeline with one of the models being None
											with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(__lowercase	)
															__a       =		StableDiffusionPipeline.from_pretrained(__lowercase	)

											# sanity check that the pipeline still works
											assert pipe.safety_checker is None
											__a       =		pipe("""example prompt"""      ,				num_inference_steps=2	).images[0]
											assert image is not None







							@unittest.skipIf(torch_device != """cuda"""      ,				"""This test requires a GPU"""	)
							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		self.dummy_cond_unet
											__a       =		PNDMScheduler(skip_prk_steps=__lowercase	)
											__a       =		self.dummy_vae
											__a       =		self.dummy_text_encoder
											__a       =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

											# put models in fp16
											__a       =		unet.half()
											__a       =		vae.half()
											__a       =		bert.half()

											# make sure here that pndm scheduler skips prk
											__a       =		StableDiffusionPipeline(
											    unet=__lowercase      ,				scheduler=__lowercase      ,				vae=__lowercase      ,				text_encoder=__lowercase      ,				tokenizer=__lowercase      ,				safety_checker=__lowercase      ,				feature_extractor=self.dummy_extractor      ,				)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""A painting of a squirrel eating a burger"""
											__a       =		sd_pipe([prompt]      ,				num_inference_steps=2      ,				output_type="""np"""	).images

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






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





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      ,				safety_checker=__lowercase	)
											__a       =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		(
											    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
											    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
											    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
											    """ children from bahnhof zoo, detailed """
											)
											__a       =		4003660346
											__a       =		7

											# without safety guidance (sld_guidance_scale = 0)
											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=0      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

											assert image.shape == (1, 512, 512, 3)

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

											# without safety guidance (strong configuration)
											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=2000      ,				sld_warmup_steps=7      ,				sld_threshold=0.025      ,				sld_momentum_scale=0.5      ,				sld_mom_beta=0.7      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

											assert image.shape == (1, 512, 512, 3)

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





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""      ,				safety_checker=__lowercase	)
											__a       =		LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		"""padme amidala taking a bath artwork, safe for work, no nudity"""
											__a       =		2734971755
											__a       =		7

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=0      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

											assert image.shape == (1, 512, 512, 3)

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

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=2000      ,				sld_warmup_steps=7      ,				sld_threshold=0.025      ,				sld_momentum_scale=0.5      ,				sld_mom_beta=0.7      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

											assert image.shape == (1, 512, 512, 3)

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







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""	)
											__a       =		sd_pipe.to(__lowercase	)
											sd_pipe.set_progress_bar_config(disable=__lowercase	)

											__a       =		(
											    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
											    """ leyendecker"""
											)
											__a       =		1044355234
											__a       =		12

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=0      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	)

											assert image.shape == (1, 512, 512, 3)

											assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-7

											__a       =		torch.manual_seed(__lowercase	)
											__a       =		sd_pipe(
											    [prompt]      ,				generator=__lowercase      ,				guidance_scale=__lowercase      ,				num_inference_steps=50      ,				output_type="""np"""      ,				width=512      ,				height=512      ,				sld_guidance_scale=2000      ,				sld_warmup_steps=7      ,				sld_threshold=0.025      ,				sld_momentum_scale=0.5      ,				sld_mom_beta=0.7      ,				)

											__a       =		output.images
											__a       =		image[0, -3:, -3:, -1]
											__a       =		np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]	)
											assert image.shape == (1, 512, 512, 3)

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