




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        __lowerCAmelCase					(			UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						unittest.TestCase					):
			snake_case_ :					Optional[int]						=	StableDiffusionImageVariationPipeline
			snake_case_ :					Dict						=	IMAGE_VARIATION_PARAMS
			snake_case_ :					str						=	IMAGE_VARIATION_BATCH_PARAMS
			snake_case_ :					Any						=	frozenset([]					)
			# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			snake_case_ :					List[Any]						=	frozenset([]					)







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									torch.manual_seed(0 )
									_UpperCAmelCase						=	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       ,       )
									_UpperCAmelCase						=	PNDMScheduler(skip_prk_steps=snake_case__ )
									torch.manual_seed(0 )
									_UpperCAmelCase						=	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 )
									_UpperCAmelCase						=	CLIPVisionConfig(
									    hidden_size=32       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-05       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       image_size=32       ,       patch_size=4       ,       )
									_UpperCAmelCase						=	CLIPVisionModelWithProjection(snake_case__ )
									_UpperCAmelCase						=	CLIPImageProcessor(crop_size=32       ,       size=32 )

									_UpperCAmelCase						=	{
									    "unet": unet,
									    "scheduler": scheduler,
									    "vae": vae,
									    "image_encoder": image_encoder,
									    "feature_extractor": feature_extractor,
									    "safety_checker": None,
									}
									return components







			def UpperCamelCase     (			self       :     int       ,       snake_case__       :     Union[str, Any]       ,       snake_case__       :     str=0 ):




									"""simple docstring"""


									_UpperCAmelCase						=	floats_tensor((1, 3, 32, 32)       ,       rng=random.Random(snake_case__ ) )
									_UpperCAmelCase						=	image.cpu().permute(0       ,       2       ,       3       ,       1 )[0]
									_UpperCAmelCase						=	Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((32, 32) )
									if str(snake_case__ ).startswith("mps" ):
															_UpperCAmelCase						=	torch.manual_seed(snake_case__ )
									else:
															_UpperCAmelCase						=	torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
									_UpperCAmelCase						=	{
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 2,
									    "guidance_scale": 6.0,
									    "output_type": "numpy",
									}
									return inputs







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									_UpperCAmelCase						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase						=	self.get_dummy_components()
									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline(**snake_case__ )
									_UpperCAmelCase						=	sd_pipe.to(snake_case__ )
									sd_pipe.set_progress_bar_config(disable=snake_case__ )

									_UpperCAmelCase						=	self.get_dummy_inputs(snake_case__ )
									_UpperCAmelCase						=	sd_pipe(**snake_case__ ).images
									_UpperCAmelCase						=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_UpperCAmelCase						=	np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921] )

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







			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase						=	self.get_dummy_components()
									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline(**snake_case__ )
									_UpperCAmelCase						=	sd_pipe.to(snake_case__ )
									sd_pipe.set_progress_bar_config(disable=snake_case__ )

									_UpperCAmelCase						=	self.get_dummy_inputs(snake_case__ )
									_UpperCAmelCase						=	2 * [inputs["image"]]
									_UpperCAmelCase						=	sd_pipe(**snake_case__ )

									_UpperCAmelCase						=	output.images

									_UpperCAmelCase						=	image[-1, -3:, -3:, -1]

									assert image.shape == (2, 64, 64, 3)
									_UpperCAmelCase						=	np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289] )

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







			def UpperCamelCase     (			self       :     int ):




									"""simple docstring"""


									super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






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







			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def UpperCamelCase     (			self       :     Optional[Any]       ,       snake_case__       :     List[Any]       ,       snake_case__       :     int="cpu"       ,       snake_case__       :     Any=torch.floataa       ,       snake_case__       :     Union[str, Any]=0 ):




									"""simple docstring"""


									_UpperCAmelCase						=	torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
									_UpperCAmelCase						=	load_image(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
									    "/stable_diffusion_imgvar/input_image_vermeer.png" )
									_UpperCAmelCase						=	np.random.RandomState(snake_case__ ).standard_normal((1, 4, 64, 64) )
									_UpperCAmelCase						=	torch.from_numpy(snake_case__ ).to(device=snake_case__       ,       dtype=snake_case__ )
									_UpperCAmelCase						=	{
									    "image": init_image,
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs







			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline.from_pretrained(
									    "lambdalabs/sd-image-variations-diffusers"       ,       safety_checker=snake_case__ )
									_UpperCAmelCase						=	sd_pipe.to(snake_case__ )
									sd_pipe.set_progress_bar_config(disable=snake_case__ )

									_UpperCAmelCase						=	self.get_inputs(snake_case__ )
									_UpperCAmelCase						=	sd_pipe(**snake_case__ ).images
									_UpperCAmelCase						=	image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase						=	np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379] )
									assert np.abs(image_slice - expected_slice ).max() < 6e-3







			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	0

									def callback_fn(snake_case__       :     int       ,       snake_case__       :     int       ,       snake_case__       :     torch.FloatTensor ) -> None:
															_UpperCAmelCase						=	True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					_UpperCAmelCase						=	latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					_UpperCAmelCase						=	latents[0, -3:, -3:, -1]
																					_UpperCAmelCase						=	np.array(
																					    [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309] )

																					assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
															elif step == 2:
																					_UpperCAmelCase						=	latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					_UpperCAmelCase						=	latents[0, -3:, -3:, -1]
																					_UpperCAmelCase						=	np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273] )

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

									_UpperCAmelCase						=	False

									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline.from_pretrained(
									    "fusing/sd-image-variations-diffusers"       ,       safety_checker=snake_case__       ,       torch_dtype=torch.floataa       ,       )
									pipe.to(snake_case__ )
									pipe.set_progress_bar_config(disable=snake_case__ )
									pipe.enable_attention_slicing()

									_UpperCAmelCase						=	self.get_inputs(snake_case__       ,       dtype=torch.floataa )
									pipe(**snake_case__       ,       callback=snake_case__       ,       callback_steps=1 )
									assert callback_fn.has_been_called
									assert number_of_steps == inputs["num_inference_steps"]







			def UpperCamelCase     (			self       :     Any ):




									"""simple docstring"""


									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_UpperCAmelCase						=	"fusing/sd-image-variations-diffusers"
									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline.from_pretrained(
									    snake_case__       ,       safety_checker=snake_case__       ,       torch_dtype=torch.floataa )
									_UpperCAmelCase						=	pipe.to(snake_case__ )
									pipe.set_progress_bar_config(disable=snake_case__ )
									pipe.enable_attention_slicing(1 )
									pipe.enable_sequential_cpu_offload()

									_UpperCAmelCase						=	self.get_inputs(snake_case__       ,       dtype=torch.floataa )
									_UpperCAmelCase						=	pipe(**snake_case__ )

									_UpperCAmelCase						=	torch.cuda.max_memory_allocated()
									# make sure that less than 2.6 GB is allocated
									assert mem_bytes < 2.6 * 10**9






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







			def UpperCamelCase     (			self       :     List[str] ):




									"""simple docstring"""


									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def UpperCamelCase     (			self       :     Tuple       ,       snake_case__       :     Any       ,       snake_case__       :     Tuple="cpu"       ,       snake_case__       :     int=torch.floataa       ,       snake_case__       :     Union[str, Any]=0 ):




									"""simple docstring"""


									_UpperCAmelCase						=	torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
									_UpperCAmelCase						=	load_image(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
									    "/stable_diffusion_imgvar/input_image_vermeer.png" )
									_UpperCAmelCase						=	np.random.RandomState(snake_case__ ).standard_normal((1, 4, 64, 64) )
									_UpperCAmelCase						=	torch.from_numpy(snake_case__ ).to(device=snake_case__       ,       dtype=snake_case__ )
									_UpperCAmelCase						=	{
									    "image": init_image,
									    "latents": latents,
									    "generator": generator,
									    "num_inference_steps": 50,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs







			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers" )
									sd_pipe.to(snake_case__ )
									sd_pipe.set_progress_bar_config(disable=snake_case__ )

									_UpperCAmelCase						=	self.get_inputs(snake_case__ )
									_UpperCAmelCase						=	sd_pipe(**snake_case__ ).images[0]

									_UpperCAmelCase						=	load_numpy(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
									    "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy" )
									_UpperCAmelCase						=	np.abs(expected_image - image ).max()
									assert max_diff < 1e-3







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers" )
									_UpperCAmelCase						=	DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config )
									sd_pipe.to(snake_case__ )
									sd_pipe.set_progress_bar_config(disable=snake_case__ )

									_UpperCAmelCase						=	self.get_inputs(snake_case__ )
									_UpperCAmelCase						=	25
									_UpperCAmelCase						=	sd_pipe(**snake_case__ ).images[0]

									_UpperCAmelCase						=	load_numpy(
									    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
									    "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy" )
									_UpperCAmelCase						=	np.abs(expected_image - image ).max()
									assert max_diff < 1e-3










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowercase_      :							int    							=					getLogger(__name__)


lowercase_      :							str    							=					'cuda' if torch.cuda.is_available() else 'cpu'


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = 8      ,       snake_case_ = DEFAULT_DEVICE      ,       snake_case_=False      ,       snake_case_="summarization"      ,       snake_case_=None      ,       **snake_case_      ,       ):

						'''simple docstring'''


						_UpperCAmelCase						=	Path(snake_case_		).open("w"      ,       encoding="utf-8"		)
						_UpperCAmelCase						=	str(snake_case_		)
						_UpperCAmelCase						=	AutoModelForSeqaSeqLM.from_pretrained(snake_case_		).to(snake_case_		)
						if fpaa:
												_UpperCAmelCase						=	model.half()

						_UpperCAmelCase						=	AutoTokenizer.from_pretrained(snake_case_		)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""		)  # if this is wrong, check config.model_type.

						_UpperCAmelCase						=	time.time()
						# update config with task specific params
						use_task_specific_params(snake_case_      ,       snake_case_		)
						if prefix is None:
												_UpperCAmelCase						=	prefix or getattr(model.config      ,       "prefix"      ,       ""		) or ""
						for examples_chunk in tqdm(list(chunks(snake_case_      ,       snake_case_		)		)		):
												_UpperCAmelCase						=	[prefix + text for text in examples_chunk]
												_UpperCAmelCase						=	tokenizer(snake_case_      ,       return_tensors="pt"      ,       truncation=snake_case_      ,       padding="longest"		).to(snake_case_		)
												_UpperCAmelCase						=	model.generate(
												    input_ids=batch.input_ids      ,       attention_mask=batch.attention_mask      ,       **snake_case_      ,       )
												_UpperCAmelCase						=	tokenizer.batch_decode(snake_case_      ,       skip_special_tokens=snake_case_      ,       clean_up_tokenization_spaces=snake_case_		)
												for hypothesis in dec:
																		fout.write(hypothesis + "\n"		)
																		fout.flush()
						fout.close()
						_UpperCAmelCase						=	int(time.time() - start_time		)  # seconds
						_UpperCAmelCase						=	len(snake_case_		)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,       4		)}


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


						return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_=True		):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser()
						parser.add_argument("model_name"      ,       type=snake_case_      ,       help="like facebook/bart-large-cnn,t5-base, etc."		)
						parser.add_argument("input_path"      ,       type=snake_case_      ,       help="like cnn_dm/test.source"		)
						parser.add_argument("save_path"      ,       type=snake_case_      ,       help="where to save summaries"		)
						parser.add_argument("--reference_path"      ,       type=snake_case_      ,       required=snake_case_      ,       help="like cnn_dm/test.target"		)
						parser.add_argument("--score_path"      ,       type=snake_case_      ,       required=snake_case_      ,       default="metrics.json"      ,       help="where to save metrics"		)
						parser.add_argument("--device"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="cuda, cuda:1, cpu etc."		)
						parser.add_argument(
						    "--prefix"      ,       type=snake_case_      ,       required=snake_case_      ,       default=snake_case_      ,       help="will be added to the begininng of src examples"		)
						parser.add_argument("--task"      ,       type=snake_case_      ,       default="summarization"      ,       help="used for task_specific_params + metrics"		)
						parser.add_argument("--bs"      ,       type=snake_case_      ,       default=8      ,       required=snake_case_      ,       help="batch size"		)
						parser.add_argument(
						    "--n_obs"      ,       type=snake_case_      ,       default=-1      ,       required=snake_case_      ,       help="How many observations. Defaults to all."		)
						parser.add_argument("--fp16"      ,       action="store_true"		)
						parser.add_argument("--dump-args"      ,       action="store_true"      ,       help="print the custom hparams with the results"		)
						parser.add_argument(
						    "--info"      ,       nargs="?"      ,       type=snake_case_      ,       const=datetime_now()      ,       help=(
						        "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
						        " lang=en-ru. If no value is passed, the current datetime string will be used."
						    )      ,       )
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						_UpperCAmelCase				,					_UpperCAmelCase						=	parser.parse_known_args()
						_UpperCAmelCase						=	parse_numeric_n_bool_cl_kwargs(snake_case_		)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""		)
						with open(args.input_path		) as f:
												_UpperCAmelCase						=	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
						if args.n_obs > 0:
												_UpperCAmelCase						=	examples[: args.n_obs]
						Path(args.save_path		).parent.mkdir(exist_ok=snake_case_		)
						if args.reference_path is None and Path(args.score_path		).exists():
												warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""		)
						_UpperCAmelCase						=	generate_summaries_or_translations(
						    snake_case_      ,       args.save_path      ,       args.model_name      ,       batch_size=args.bs      ,       device=args.device      ,       fpaa=args.fpaa      ,       task=args.task      ,       prefix=args.prefix      ,       **snake_case_      ,       )

						if args.reference_path is None:
												return {}

						# Compute scores
						_UpperCAmelCase						=	calculate_bleu if "translation" in args.task else calculate_rouge
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.save_path		).readlines()]
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.reference_path		).readlines()][: len(snake_case_		)]
						_UpperCAmelCase						=	score_fn(snake_case_      ,       snake_case_		)
						scores.update(snake_case_		)

						if args.dump_args:
												scores.update(snake_case_		)
						if args.info:
												_UpperCAmelCase						=	args.info

						if verbose:
												print(snake_case_		)

						if args.score_path is not None:
												json.dump(snake_case_      ,       open(args.score_path      ,       "w"		)		)

						return scores


if __name__ == "__main__":
				# Usage for MT:
				# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
				run_generate(verbose=True)




