
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


__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     getLogger(__name__)


__SCREAMING_SNAKE_CASE			:   int         =     'cuda' if torch.cuda.is_available() else 'cpu'
def     UpperCAmelCase__							(       __magic_name__						:     List[str]							,  __magic_name__						:     str							,  __magic_name__						:     str							,  __magic_name__						:     int = 8							,  __magic_name__						:     str = DEFAULT_DEVICE							,  __magic_name__						:     List[str]=False							,  __magic_name__						:     Union[str, Any]="summarization"							,  __magic_name__						:     Dict=None							,  **__magic_name__						:     Union[str, Any]							,  ):


					'''simple docstring'''

					lowerCAmelCase		:					str  	=     Path(__magic_name__  ).open('''w'''							,  encoding='''utf-8'''  )
					lowerCAmelCase		:					Optional[int]  	=     str(__magic_name__  )
					lowerCAmelCase		:					Optional[int]  	=     AutoModelForSeqaSeqLM.from_pretrained(__magic_name__  ).to(__magic_name__  )
					if fpaa:
										lowerCAmelCase		:					List[str]  	=     model.half()

					lowerCAmelCase		:					List[Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
					logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''  )  # if this is wrong, check config.model_type.

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


					'''simple docstring'''

					return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''  )
def     UpperCAmelCase__							(       __magic_name__						:     Any=True  ):


					'''simple docstring'''

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

					if args.reference_path is None:
										return {}

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

					if args.dump_args:
										scores.update(__magic_name__  )
					if args.info:
										lowerCAmelCase		:					Any  	=     args.info

					if verbose:
										print(__magic_name__  )

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


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
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_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	PaintByExamplePipeline
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		_lowerCAmelCase		=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCAmelCase		=	frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess





		def       _A							(							self    :	Dict				):
							torch.manual_seed(0				)
							lowerCAmelCase		:					Optional[int]  	=     UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)						,  layers_per_block=2						,  sample_size=3_2						,  in_channels=9						,  out_channels=4						,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,  cross_attention_dim=3_2						,  )
							lowerCAmelCase		:					List[str]  	=     PNDMScheduler(skip_prk_steps=lowerCamelCase__				)
							torch.manual_seed(0				)
							lowerCAmelCase		:					List[str]  	=     AutoencoderKL(
							    block_out_channels=[3_2, 6_4]						,  in_channels=3						,  out_channels=3						,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,  latent_channels=4						,  )
							torch.manual_seed(0				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPVisionConfig(
							    hidden_size=3_2						,  projection_dim=3_2						,  intermediate_size=3_7						,  layer_norm_eps=1E-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  image_size=3_2						,  patch_size=4						,  )
							lowerCAmelCase		:					List[Any]  	=     PaintByExampleImageEncoder(lowerCamelCase__						,  proj_size=3_2				)
							lowerCAmelCase		:					Union[str, Any]  	=     CLIPImageProcessor(crop_size=3_2						,  size=3_2				)

							lowerCAmelCase		:					str  	=     {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''image_encoder''': image_encoder,
							    '''safety_checker''': None,
							    '''feature_extractor''': feature_extractor,
							}
							return components





		def       _A							(							self    :	str						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					List[Any]  	=     np.array(image.convert('''RGB'''				)				)
							lowerCAmelCase		:					int  	=     image[None].transpose(0						,  3						,  1						,  2				)
							lowerCAmelCase		:					Any  	=     torch.from_numpy(lowerCamelCase__				).to(dtype=torch.floataa				) / 1_2_7.5 - 1.0
							return image





		def       _A							(							self    :	str						,  lowerCamelCase__    :	Union[str, Any]="cpu"						,  lowerCamelCase__    :	Any=0				):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							lowerCAmelCase		:					str  	=     floats_tensor((1, 3, 3_2, 3_2)						,  rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
							lowerCAmelCase		:					Any  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					Dict  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(image + 4				)				).convert('''RGB'''				).resize((6_4, 6_4)				)
							lowerCAmelCase		:					Optional[int]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				).resize((3_2, 3_2)				)

							if str(lowerCamelCase__				).startswith('''mps'''				):
												lowerCAmelCase		:					Dict  	=     torch.manual_seed(lowerCamelCase__				)
							else:
												lowerCAmelCase		:					str  	=     torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     {
							    '''example_image''': example_image,
							    '''image''': init_image,
							    '''mask_image''': mask_image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs





		def       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Tuple  	=     self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline(**lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     pipe.to('''cpu'''				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Tuple  	=     self.get_dummy_inputs()
							lowerCAmelCase		:					List[str]  	=     pipe(**lowerCamelCase__				)
							lowerCAmelCase		:					Optional[Any]  	=     output.images

							lowerCAmelCase		:					Optional[int]  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							lowerCAmelCase		:					List[Any]  	=     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2





		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					List[str]  	=     '''cpu'''
							lowerCAmelCase		:					Union[str, Any]  	=     self.get_dummy_inputs()
							inputs.pop('''mask_image'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     self.convert_to_pt(inputs.pop('''image'''				)				)
							lowerCAmelCase		:					Dict  	=     image.clamp(0						,  1				) / 2

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					str  	=     PaintByExamplePipeline(**self.get_dummy_components()				)
							lowerCAmelCase		:					Tuple  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					Optional[int]  	=     pipe(image=lowerCamelCase__						,  mask_image=mask_image[:, 0]						,  **lowerCamelCase__				)
							lowerCAmelCase		:					Optional[int]  	=     output.images

							lowerCAmelCase		:					int  	=     image.cpu().permute(0						,  2						,  3						,  1				)[0]
							lowerCAmelCase		:					int  	=     mask_image.cpu().permute(0						,  2						,  3						,  1				)[0]

							lowerCAmelCase		:					List[str]  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)
							lowerCAmelCase		:					str  	=     Image.fromarray(np.uinta(lowerCamelCase__				)				).convert('''RGB'''				)

							lowerCAmelCase		:					List[Any]  	=     pipe(**self.get_dummy_inputs()				)
							lowerCAmelCase		:					Any  	=     output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()				).max() < 5E-2





		def       _A							(							self    :	Optional[Any]				):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



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





		def       _A							(							self    :	Tuple				):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def       _A							(							self    :	Optional[int]				):
							# make sure here that pndm scheduler skips prk
							lowerCAmelCase		:					Union[str, Any]  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/dog_in_bucket.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/mask.png'''				)
							lowerCAmelCase		:					Any  	=     load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/panda.jpg'''				)

							lowerCAmelCase		:					Union[str, Any]  	=     PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''				)
							lowerCAmelCase		:					str  	=     pipe.to(lowerCamelCase__				)
							pipe.set_progress_bar_config(disable=lowerCamelCase__				)

							lowerCAmelCase		:					int  	=     torch.manual_seed(3_2_1				)
							lowerCAmelCase		:					List[str]  	=     pipe(
							    image=lowerCamelCase__						,  mask_image=lowerCamelCase__						,  example_image=lowerCamelCase__						,  generator=lowerCamelCase__						,  guidance_scale=5.0						,  num_inference_steps=5_0						,  output_type='''np'''						,  )

							lowerCAmelCase		:					Any  	=     output.images

							lowerCAmelCase		:					Tuple  	=     image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase		:					Dict  	=     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
