




'''simple docstring'''







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


_lowerCamelCase							:						List[Any]								=  getLogger(__name__)


_lowerCamelCase							:						List[str]								=  'cuda' if torch.cuda.is_available() else 'cpu'






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_ = 8						,							lowercase_ = DEFAULT_DEVICE						,							lowercase_=False						,							lowercase_="summarization"						,							lowercase_=None						,							**lowercase_						,							):



		'''simple docstring'''







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

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

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






def 		_lowercase		(	):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_=True  ):



		'''simple docstring'''







		_snake_case	       =       argparse.ArgumentParser()
		parser.add_argument("model_name"						,							type=a_						,							help="like facebook/bart-large-cnn,t5-base, etc."  )
		parser.add_argument("input_path"						,							type=a_						,							help="like cnn_dm/test.source"  )
		parser.add_argument("save_path"						,							type=a_						,							help="where to save summaries"  )
		parser.add_argument("--reference_path"						,							type=a_						,							required=a_						,							help="like cnn_dm/test.target"  )
		parser.add_argument("--score_path"						,							type=a_						,							required=a_						,							default="metrics.json"						,							help="where to save metrics"  )
		parser.add_argument("--device"						,							type=a_						,							required=a_						,							default=a_						,							help="cuda, cuda:1, cpu etc."  )
		parser.add_argument(
		    "--prefix"						,							type=a_						,							required=a_						,							default=a_						,							help="will be added to the begininng of src examples"  )
		parser.add_argument("--task"						,							type=a_						,							default="summarization"						,							help="used for task_specific_params + metrics"  )
		parser.add_argument("--bs"						,							type=a_						,							default=8						,							required=a_						,							help="batch size"  )
		parser.add_argument(
		    "--n_obs"						,							type=a_						,							default=-1						,							required=a_						,							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=a_						,							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
		_snake_case	       =       parser.parse_known_args()
		_snake_case	       =       parse_numeric_n_bool_cl_kwargs(a_  )
		if parsed_args and verbose:
				print(f'''parsed the following generate kwargs: {parsed_args}'''  )
		with open(args.input_path  ) as f:
				_snake_case	       =       [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
		if args.n_obs > 0:
				_snake_case	       =       examples[: args.n_obs]
		Path(args.save_path  ).parent.mkdir(exist_ok=a_  )
		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.'''  )
		_snake_case	       =       generate_summaries_or_translations(
		    a_						,							args.save_path						,							args.model_name						,							batch_size=args.bs						,							device=args.device						,							fpaa=args.fpaa						,							task=args.task						,							prefix=args.prefix						,							**a_						,							)

		if args.reference_path is None:
				return {}

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

		if args.dump_args:
				scores.update(a_  )
		if args.info:
				_snake_case	       =       args.info

		if verbose:
				print(a_  )

		if args.score_path is not None:
				json.dump(a_						,							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)







"""simple docstring"""

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	RePaintPipeline
	lowercase_        =	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	lowercase_        =	PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	lowercase_        =	IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			torch.manual_seed(0					)
			torch.manual_seed(0					)
			lowerCamelCase     :			Optional[Any]		=    UNetaDModel(
			    block_out_channels=(32, 64)		, layers_per_block=2		, sample_size=32		, in_channels=3		, out_channels=3		, down_block_types=('DownBlock2D', 'AttnDownBlock2D')		, up_block_types=('AttnUpBlock2D', 'UpBlock2D')		, )
			lowerCamelCase     :			Union[str, Any]		=    RePaintScheduler()
			lowerCamelCase     :			Dict		=    {'unet': unet, 'scheduler': scheduler}
			return components




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_=0					)		->							Optional[int]:
			if str(UpperCAmelCase_					).startswith('mps'					):
					lowerCamelCase     :			str		=    torch.manual_seed(UpperCAmelCase_					)
			else:
					lowerCamelCase     :			Tuple		=    torch.Generator(device=UpperCAmelCase_					).manual_seed(UpperCAmelCase_					)
			lowerCamelCase     :			str		=    np.random.RandomState(UpperCAmelCase_					).standard_normal((1, 3, 32, 32)					)
			lowerCamelCase     :			Tuple		=    torch.from_numpy(UpperCAmelCase_					).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[int]		=    (image > 0).to(device=UpperCAmelCase_		, dtype=torch.floataa					)
			lowerCamelCase     :			Optional[Any]		=    {
			    'image': image,
			    'mask_image': mask,
			    'generator': generator,
			    'num_inference_steps': 5,
			    'eta': 0.0,
			    'jump_length': 2,
			    'jump_n_sample': 2,
			    'output_type': 'numpy',
			}
			return inputs




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Any		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase     :			Union[str, Any]		=    self.get_dummy_components()
			lowerCamelCase     :			List[Any]		=    RePaintPipeline(**UpperCAmelCase_					)
			lowerCamelCase     :			int		=    sd_pipe.to(UpperCAmelCase_					)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    self.get_dummy_inputs(UpperCAmelCase_					)
			lowerCamelCase     :			Dict		=    sd_pipe(**UpperCAmelCase_					).images
			lowerCamelCase     :			List[Any]		=    image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase     :			Optional[Any]		=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

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




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return super().test_save_load_local()




	@unittest.skip('non-deterministic pipeline'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_inference_batch_single_identical()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							int:
			return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			return super().test_save_load_optional_components()




	@skip_mps
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			return super().test_attention_slicing_forward_pass()






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




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256.png'					)
			lowerCamelCase     :			str		=    load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'					)
			lowerCamelCase     :			Optional[Any]		=    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
			    'repaint/celeba_hq_256_result.npy'					)

			lowerCamelCase     :			Any		=    'google/ddpm-ema-celebahq-256'
			lowerCamelCase     :			List[Any]		=    UNetaDModel.from_pretrained(UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    RePaintScheduler.from_pretrained(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    RePaintPipeline(unet=UpperCAmelCase_		, scheduler=UpperCAmelCase_					).to(UpperCAmelCase_					)
			repaint.set_progress_bar_config(disable=UpperCAmelCase_					)
			repaint.enable_attention_slicing()

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)
			lowerCamelCase     :			str		=    repaint(
			    UpperCAmelCase_		, UpperCAmelCase_		, num_inference_steps=250		, eta=0.0		, jump_length=10		, jump_n_sample=10		, generator=UpperCAmelCase_		, output_type='np'		, )
			lowerCamelCase     :			Optional[int]		=    output.images[0]

			assert image.shape == (256, 256, 3)
			assert np.abs(expected_image - image					).mean() < 1E-2






