



'''simple docstring'''






import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


UpperCamelCase__:   List[str]								=			"\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n"


UpperCamelCase__:   Optional[Any]								=			"\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"


UpperCamelCase__:   Union[str, Any]								=			r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n    >>> metric = datasets.load_metric(\"competition_math\")\n    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n    >>> print(results)\n    {'accuracy': 1.0}\n"

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







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		Tuple:
								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/hendrycks/math'''		,			codebase_urls=['''https://github.com/hendrycks/math''']		,			)







	def      A	(						self			:     int		,			__snake_case			:     int		,			__snake_case			:     List[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       str 							=		0.0
								for i, j in zip(__snake_case		,			__snake_case		):
															n_correct += 1.0 if math_equivalence.is_equiv(__snake_case		,			__snake_case		) else 0.0
								UpperCAmelCase :       Union[str, Any] 							=		n_correct / len(__snake_case		)
								return {
								    "accuracy": accuracy,
								}










'''simple docstring'''






import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard

						from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline

@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     str		)			->		int:
								with tempfile.TemporaryDirectory() as tmpdirname:
															# pipeline has Flax weights
															UpperCAmelCase :       Optional[Any] 							=		FlaxDiffusionPipeline.from_pretrained(
															    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		,			cache_dir=__snake_case		)

															UpperCAmelCase :       str 							=		[t[-1] for t in os.walk(os.path.join(__snake_case		,			os.listdir(__snake_case		)[0]		,			'''snapshots'''		)		)]
															UpperCAmelCase :       str 							=		[item for sublist in all_root_files for item in sublist]

															# None of the downloaded files should be a PyTorch file even if we have some here:
															# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
															assert not any(f.endswith('''.bin'''		) for f in files		)

@slow
@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       str 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Optional[Any] 							=		4

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Tuple 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 64, 64, 3)
								if jax.device_count() == 8:
															assert np.abs(np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 4.1_51_47_45		) < 1E-3
															assert np.abs(np.abs(__snake_case		,			dtype=np.floataa		).sum() - 4_99_47.8_75		) < 5E-1

								UpperCAmelCase :       Union[str, Any] 							=		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
								assert len(__snake_case		) == num_samples







	def      A	(						self			:     List[Any]		)			->		List[str]:
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''flax'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Optional[Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Any 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       int 							=		num_samples * [prompt]
								UpperCAmelCase :       Union[str, Any] 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		shard(__snake_case		)

								UpperCAmelCase :       Tuple 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.05_65_24_01)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_38_38_08.2)		) < 5E-1







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		)

								UpperCAmelCase :       Dict 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       List[str] 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     int		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		)

								UpperCAmelCase :       List[str] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[int] 							=		jax.device_count()
								UpperCAmelCase :       List[str] 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       str 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.04_00_39_06)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		FlaxDDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			set_alpha_to_one=__snake_case		,			steps_offset=1		,			)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			scheduler=__snake_case		,			safety_checker=__snake_case		,			)
								UpperCAmelCase :       Tuple 							=		scheduler.create_state()

								UpperCAmelCase :       Dict 							=		scheduler_state

								UpperCAmelCase :       str 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       int 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Union[str, Any] 							=		50

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Any 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       str 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[str] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Dict 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images

								assert images.shape == (num_samples, 1, 512, 512, 3)
								if jax.device_count() == 8:
															assert np.abs((np.abs(images[0, 0, :2, :2, -2:]		,			dtype=np.floataa		).sum() - 0.0_45_04_39_45)		) < 1E-3
															assert np.abs((np.abs(__snake_case		,			dtype=np.floataa		).sum() - 2_34_76_93.5)		) < 5E-1







	def      A	(						self			:     Any		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		(
								    '''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
								    ''' field, close up, split lighting, cinematic'''
								)

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       str 							=		jax.random.split(jax.random.PRNGKey(0		)		,			__snake_case		)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			)

								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[str] 							=		shard(__snake_case		)
								UpperCAmelCase :       Any 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       Optional[int] 							=		images[2, 0, 256, 10:17, 1]

								# With memory efficient attention
								UpperCAmelCase							,		UpperCAmelCase :       Any 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		,			use_memory_efficient_attention=__snake_case		,			)

								UpperCAmelCase :       int 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		shard(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images_eff.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       int 							=		images[2, 0, 256, 10:17, 1]

								# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
								# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
								assert abs(slice_eff - slice		).max() < 1E-2





