

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							str										=     IFPipeline
			_UpperCAmelCase   :							Tuple										=     TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
			_UpperCAmelCase   :							Any										=     TEXT_TO_IMAGE_BATCH_PARAMS
			_UpperCAmelCase   :							Union[str, Any]										=     PipelineTesterMixin.required_optional_params - {'''latents'''}



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self._get_dummy_components()



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]=0):
									if str(lowerCAmelCase__).startswith("mps"):
															SCREAMING_SNAKE_CASE_: int  		=       torch.manual_seed(lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Generator(device=lowerCAmelCase__).manual_seed(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       {
									    "prompt": "A painting of a squirrel eating a burger",
									    "generator": generator,
									    "num_inference_steps": 2,
									    "output_type": "numpy",
									}

									return inputs



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != "cuda"   ,						reason="float16 requires CUDA")
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1E-1)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									self._test_save_load_local()



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1E-2   ,						)




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()   ,						reason="XFormers attention is only available with CUDA and `xformers` installed"   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







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




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									# if

									SCREAMING_SNAKE_CASE_: str  		=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,						variant="fp16"   ,						torch_dtype=torch.floataa)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFSuperResolutionPipeline.from_pretrained(
									    "DeepFloyd/IF-II-L-v1.0"   ,						variant="fp16"   ,						torch_dtype=torch.floataa   ,						text_encoder=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to("cuda")

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       pipe_a.encode_prompt("anime turtle"   ,						device="cuda")

									del pipe_a.tokenizer
									del pipe_a.text_encoder
									gc.collect()

									SCREAMING_SNAKE_CASE_: Any  		=       None
									SCREAMING_SNAKE_CASE_: int  		=       None

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									SCREAMING_SNAKE_CASE_: Any  		=       IFImgaImgPipeline(**pipe_a.components)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFImgaImgSuperResolutionPipeline(**pipe_a.components)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if_imgaimg(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       IFInpaintingPipeline(**pipe_a.components)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       IFInpaintingSuperResolutionPipeline(**pipe_a.components)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if_inpainting(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Generator(device="cpu").manual_seed(0)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       output.images[0]

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

									SCREAMING_SNAKE_CASE_: Any  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									SCREAMING_SNAKE_CASE_: Any  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)

									# pipeline 2

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: Any  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

									assert image.shape == (256, 256, 3)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_SNAKE_CASE_: Dict  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int]):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: str  		=       output.images[0]

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

									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									SCREAMING_SNAKE_CASE_: int  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)

									# pipeline 2

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						original_image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Any  		=       output.images[0]

									assert image.shape == (256, 256, 3)

									SCREAMING_SNAKE_CASE_: Dict  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_SNAKE_CASE_: Any  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     int):
									# pipeline 1

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(1)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Generator(device="cpu").manual_seed(0)
									SCREAMING_SNAKE_CASE_: Tuple  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						mask_image=lowerCAmelCase__   ,						num_inference_steps=2   ,						generator=lowerCAmelCase__   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: int  		=       output.images[0]

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

									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)

									# pipeline 2

									_start_torch_memory_measurement()

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.Generator(device="cpu").manual_seed(0)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(0)).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       floats_tensor((1, 3, 256, 256)   ,						rng=random.Random(1)).to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       pipe_a(
									    prompt_embeds=lowerCAmelCase__   ,						negative_prompt_embeds=lowerCAmelCase__   ,						image=lowerCAmelCase__   ,						mask_image=lowerCAmelCase__   ,						original_image=lowerCAmelCase__   ,						generator=lowerCAmelCase__   ,						num_inference_steps=2   ,						output_type="np"   ,						)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       output.images[0]

									assert image.shape == (256, 256, 3)

									SCREAMING_SNAKE_CASE_: List[str]  		=       torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									SCREAMING_SNAKE_CASE_: List[Any]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy")
									assert_mean_pixel_difference(lowerCAmelCase__   ,						lowerCAmelCase__)







def 			A_      (    ):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()













"""simple docstring"""





import re
import string

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__					=   "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

SCREAMING_SNAKE_CASE__					=   "\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

SCREAMING_SNAKE_CASE__					=   "\n"

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





      def     _snake_case			(       self )		->     Optional[int]:
            return datasets.MetricInfo(
                description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""string"""      ,						id="""sequence""" ),
                        """references""": datasets.Value("""string"""      ,						id="""sequence""" ),
                    } )      ,						reference_urls=[]      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=None      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						)		->     Optional[Any]:
            if regexes_to_ignore is not None:
                  for s in regexes_to_ignore:
                        lowerCAmelCase  						=       np.array([re.sub(lowercase      ,						""""""      ,						lowercase ) for x in predictions] )
                        lowerCAmelCase  						=       np.array([re.sub(lowercase      ,						""""""      ,						lowercase ) for x in references] )
            else:
                  lowerCAmelCase  						=       np.asarray(lowercase )
                  lowerCAmelCase  						=       np.asarray(lowercase )

            if ignore_case:
                  lowerCAmelCase  						=       np.char.lower(lowercase )
                  lowerCAmelCase  						=       np.char.lower(lowercase )

            if ignore_punctuation:
                  lowerCAmelCase  						=       string.punctuation.maketrans(""""""      ,						""""""      ,						string.punctuation )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )

            if ignore_numbers:
                  lowerCAmelCase  						=       string.digits.maketrans(""""""      ,						""""""      ,						string.digits )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )
                  lowerCAmelCase  						=       np.char.translate(lowercase      ,						table=lowercase )

            lowerCAmelCase  						=       predictions == references

            return {"exact_match": np.mean(lowercase ) * 100}






