





'''simple docstring'''






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 		lowerCamelCase  (      _A      ,   _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   StableDiffusionInstructPixaPixPipeline
	snake_case_      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
	snake_case_      =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	snake_case_      =   IMAGE_TO_IMAGE_IMAGE_PARAMS
	snake_case_      =   IMAGE_TO_IMAGE_IMAGE_PARAMS



	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Any						=   UNetaDConditionModel(
			    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=8		,      out_channels=4		,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,      cross_attention_dim=32		,      )
			lowerCAmelCase     :    Any						=   PNDMScheduler(skip_prk_steps=a_	)
			torch.manual_seed(0	)
			lowerCAmelCase     :    str						=   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		,      )
			torch.manual_seed(0	)
			lowerCAmelCase     :    Dict						=   CLIPTextConfig(
			    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_000		,      )
			lowerCAmelCase     :    Tuple						=   CLIPTextModel(a_	)
			lowerCAmelCase     :    Dict						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

			lowerCAmelCase     :    Any						=   {
			    "unet": unet,
			    "scheduler": scheduler,
			    "vae": vae,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "safety_checker": None,
			    "feature_extractor": None,
			}
			return components



	def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
			lowerCAmelCase     :    Dict						=   floats_tensor((1, 3, 32, 32)		,      rng=random.Random(a_	)	).to(a_	)
			lowerCAmelCase     :    Optional[int]						=   image.cpu().permute(0		,      2		,      3		,      1	)[0]
			lowerCAmelCase     :    int						=   Image.fromarray(np.uinta(a_	)	).convert("RGB"	)
			if str(a_	).startswith("mps"	):
					lowerCAmelCase     :    List[str]						=   torch.manual_seed(a_	)
			else:
					lowerCAmelCase     :    List[str]						=   torch.Generator(device=a_	).manual_seed(a_	)
			lowerCAmelCase     :    List[Any]						=   {
			    "prompt": "A painting of a squirrel eating a burger",
			    "image": image,
			    "generator": generator,
			    "num_inference_steps": 2,
			    "guidance_scale": 6.0,
			    "image_guidance_scale": 1,
			    "output_type": "numpy",
			}
			return inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase     :    Optional[Any]						=   self.get_dummy_components()
			lowerCAmelCase     :    List[str]						=   StableDiffusionInstructPixaPixPipeline(**a_	)
			lowerCAmelCase     :    List[Any]						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Tuple						=   self.get_dummy_inputs(a_	)
			lowerCAmelCase     :    Dict						=   sd_pipe(**a_	).images
			lowerCAmelCase     :    Dict						=   image[0, -3:, -3:, -1]
			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase     :    List[str]						=   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Union[str, Any]						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase     :    List[str]						=   self.get_dummy_components()
			lowerCAmelCase     :    Dict						=   StableDiffusionInstructPixaPixPipeline(**a_	)
			lowerCAmelCase     :    Dict						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Any						=   self.get_dummy_inputs(a_	)
			lowerCAmelCase     :    Any						=   "french fries"
			lowerCAmelCase     :    int						=   sd_pipe(**a_		,      negative_prompt=a_	)
			lowerCAmelCase     :    str						=   output.images
			lowerCAmelCase     :    Tuple						=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase     :    List[Any]						=   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase     :    List[Any]						=   self.get_dummy_components()
			lowerCAmelCase     :    Optional[Any]						=   StableDiffusionInstructPixaPixPipeline(**a_	)
			lowerCAmelCase     :    Tuple						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Optional[Any]						=   self.get_dummy_inputs(a_	)
			lowerCAmelCase     :    Optional[int]						=   [inputs["prompt"]] * 2

			lowerCAmelCase     :    Any						=   np.array(inputs["image"]	).astype(np.floataa	) / 255.0
			lowerCAmelCase     :    List[str]						=   torch.from_numpy(a_	).unsqueeze(0	).to(a_	)
			lowerCAmelCase     :    List[str]						=   image / 2 + 0.5
			lowerCAmelCase     :    List[str]						=   image.permute(0		,      3		,      1		,      2	)
			lowerCAmelCase     :    List[Any]						=   image.repeat(2		,      1		,      1		,      1	)

			lowerCAmelCase     :    Tuple						=   sd_pipe(**a_	).images
			lowerCAmelCase     :    Any						=   image[-1, -3:, -3:, -1]

			assert image.shape == (2, 32, 32, 3)
			lowerCAmelCase     :    Any						=   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase     :    int						=   self.get_dummy_components()
			lowerCAmelCase     :    int						=   EulerAncestralDiscreteScheduler(
			    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="scaled_linear"	)
			lowerCAmelCase     :    str						=   StableDiffusionInstructPixaPixPipeline(**a_	)
			lowerCAmelCase     :    int						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    List[str]						=   self.get_dummy_inputs(a_	)
			lowerCAmelCase     :    Optional[int]						=   sd_pipe(**a_	).images
			lowerCAmelCase     :    Dict						=   image[0, -3:, -3:, -1]

			lowerCAmelCase     :    int						=   [round(a_		,      4	) for x in image_slice.flatten().tolist()]
			print(",".join([str(a_	) for x in slice]	)	)

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase     :    Optional[Any]						=   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			super().test_inference_batch_single_identical(expected_max_diff=3e-3	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   self.get_dummy_components()
			lowerCAmelCase     :    str						=   StableDiffusionInstructPixaPixPipeline(**a_	)
			lowerCAmelCase     :    Dict						=   VaeImageProcessor(do_resize=a_		,      do_normalize=a_	)
			lowerCAmelCase     :    Optional[Any]						=   pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    str						=   pipe(**self.get_dummy_inputs_by_type(a_		,      input_image_type="pt"	)	)[0]

			lowerCAmelCase     :    List[Any]						=   components["vae"]
			lowerCAmelCase     :    int						=   self.get_dummy_inputs_by_type(a_		,      input_image_type="pt"	)

			for image_param in self.image_latents_params:
					if image_param in inputs.keys():
							lowerCAmelCase     :    Tuple						=   vae.encode(inputs[image_param]	).latent_dist.mode()

			lowerCAmelCase     :    int						=   pipe(**a_	)[0]

			lowerCAmelCase     :    Dict						=   np.abs(out - out_latents_inputs	).max()
			self.assertLess(a_		,      1e-4		,      "passing latents as image input generate different result from passing image"	)






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



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



	def   _lowerCamelCase    (   self		,      a_=0	):
			lowerCAmelCase     :    Tuple						=   torch.manual_seed(a_	)
			lowerCAmelCase     :    str						=   load_image(
			    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"	)
			lowerCAmelCase     :    Any						=   {
			    "prompt": "turn him into a cyborg",
			    "image": image,
			    "generator": generator,
			    "num_inference_steps": 3,
			    "guidance_scale": 7.5,
			    "image_guidance_scale": 1.0,
			    "output_type": "numpy",
			}
			return inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    "timbrooks/instruct-pix2pix"		,      safety_checker=a_	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)
			pipe.enable_attention_slicing()

			lowerCAmelCase     :    Optional[Any]						=   self.get_inputs()
			lowerCAmelCase     :    str						=   pipe(**a_	).images
			lowerCAmelCase     :    Tuple						=   image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase     :    int						=   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]	)

			assert np.abs(expected_slice - image_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    "timbrooks/instruct-pix2pix"		,      safety_checker=a_	)
			lowerCAmelCase     :    Optional[Any]						=   LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)
			pipe.enable_attention_slicing()

			lowerCAmelCase     :    Tuple						=   self.get_inputs()
			lowerCAmelCase     :    Union[str, Any]						=   pipe(**a_	).images
			lowerCAmelCase     :    Optional[int]						=   image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase     :    Tuple						=   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]	)

			assert np.abs(expected_slice - image_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    "timbrooks/instruct-pix2pix"		,      safety_checker=a_	)
			lowerCAmelCase     :    Any						=   DDIMScheduler.from_config(pipe.scheduler.config	)
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)
			pipe.enable_attention_slicing()

			lowerCAmelCase     :    Dict						=   self.get_inputs()
			lowerCAmelCase     :    Dict						=   pipe(**a_	).images
			lowerCAmelCase     :    List[Any]						=   image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCAmelCase     :    str						=   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]	)

			assert np.abs(expected_slice - image_slice	).max() < 1e-3



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   0

			def callback_fn(a_		,      a_		,      a_	) -> None:
					lowerCAmelCase     :    Optional[int]						=   True
					nonlocal number_of_steps
					number_of_steps += 1
					if step == 1:
							lowerCAmelCase     :    str						=   latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCAmelCase     :    List[Any]						=   latents[0, -3:, -3:, -1]
							lowerCAmelCase     :    Union[str, Any]						=   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]	)

							assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2
					elif step == 2:
							lowerCAmelCase     :    Any						=   latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCAmelCase     :    str						=   latents[0, -3:, -3:, -1]
							lowerCAmelCase     :    Tuple						=   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]	)

							assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5e-2

			lowerCAmelCase     :    Any						=   False

			lowerCAmelCase     :    Any						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    "timbrooks/instruct-pix2pix"		,      safety_checker=a_		,      torch_dtype=torch.floataa	)
			lowerCAmelCase     :    Tuple						=   pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)
			pipe.enable_attention_slicing()

			lowerCAmelCase     :    List[str]						=   self.get_inputs()
			pipe(**a_		,      callback=a_		,      callback_steps=1	)
			assert callback_fn.has_been_called
			assert number_of_steps == 3



	def   _lowerCamelCase    (   self	):
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCAmelCase     :    int						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    "timbrooks/instruct-pix2pix"		,      safety_checker=a_		,      torch_dtype=torch.floataa	)
			lowerCAmelCase     :    List[str]						=   pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)
			pipe.enable_attention_slicing(1	)
			pipe.enable_sequential_cpu_offload()

			lowerCAmelCase     :    Optional[Any]						=   self.get_inputs()
			lowerCAmelCase     :    List[str]						=   pipe(**a_	)

			lowerCAmelCase     :    str						=   torch.cuda.max_memory_allocated()
			# make sure that less than 2.2 GB is allocated
			assert mem_bytes < 2.2 * 10**9





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.get_inputs()
			# resize to resolution that is divisible by 8 but not 16 or 32
			lowerCAmelCase     :    Optional[Any]						=   inputs["image"].resize((504, 504)	)

			lowerCAmelCase     :    Dict						=   "timbrooks/instruct-pix2pix"
			lowerCAmelCase     :    Tuple						=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    a_		,      safety_checker=a_		,      )
			pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)
			pipe.enable_attention_slicing()

			lowerCAmelCase     :    List[str]						=   pipe(**a_	)
			lowerCAmelCase     :    Any						=   output.images[0]

			lowerCAmelCase     :    int						=   image[255:258, 383:386, -1]

			assert image.shape == (504, 504, 3)
			lowerCAmelCase     :    Union[str, Any]						=   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 5e-3










'''simple docstring'''






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

import datasets


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


lowerCAmelCase       		=      """\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


lowerCAmelCase       		=      R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION      ,   _KWARGS_DESCRIPTION       )
class 		lowerCamelCase  (      datasets.Metric       ):



	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/hendrycks/math"		,      codebase_urls=["https://github.com/hendrycks/math"]		,      )





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   0.0
			for i, j in zip(a_		,      a_	):
					n_correct += 1.0 if math_equivalence.is_equiv(a_		,      a_	) else 0.0
			lowerCAmelCase     :    List[str]						=   n_correct / len(a_	)
			return {
			    "accuracy": accuracy,
			}



