

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_a			=       logging.get_logger(__name__)  # pylint: disable=invalid-name

_a			=       '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Tuple=8		)   ->   Optional[int]:

	"""simple docstring"""






	__lowerCAmelCase:       List[str]            =				height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	__lowerCAmelCase:       int            =				width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor







def _a			(	SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      Any=5_12   , SCREAMING_SNAKE_CASE		:      Optional[Any]=5_12		)   ->   List[Any]:

	"""simple docstring"""






	__lowerCAmelCase:       Tuple            =				pil_image.resize((w, h)   , resample=Image.BICUBIC   , reducing_gap=1		)
	__lowerCAmelCase:       List[str]            =				np.array(pil_image.convert('RGB'		)		)
	__lowerCAmelCase:       Union[str, Any]            =				arr.astype(np.floataa		) / 1_27.5 - 1
	__lowerCAmelCase:       Optional[Any]            =				np.transpose(SCREAMING_SNAKE_CASE   , [2, 0, 1]		)
	__lowerCAmelCase:       Dict            =				torch.from_numpy(SCREAMING_SNAKE_CASE		).unsqueeze(0		)
	return image






class 							A_			(		snake_case__ ):


					def __init__(							self      :		Any      ,  UpperCAmelCase      :		UNetaDConditionModel      ,  UpperCAmelCase      :		DDPMScheduler      ,  UpperCAmelCase      :		VQModel      ,  )							->     Optional[Any]:
						super().__init__()

						self.register_modules(
						    unet=UpperCAmelCase      ,  scheduler=UpperCAmelCase      ,  movq=UpperCAmelCase      ,  )
						__lowerCAmelCase:       Optional[int]            =				2 ** (len(self.movq.config.block_out_channels ) - 1)


					def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Union[str, Any] )							->     str:
						# get the original timestep using init_timestep
						__lowerCAmelCase:       Any            =				min(int(num_inference_steps * strength )      ,  UpperCAmelCase )

						__lowerCAmelCase:       Optional[Any]            =				max(num_inference_steps - init_timestep      ,  0 )
						__lowerCAmelCase:       Any            =				self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start


					def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		str      ,  UpperCAmelCase      :		Optional[int]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Optional[Any]=None )							->     str:
						if not isinstance(UpperCAmelCase      ,  (torch.Tensor, PIL.Image.Image, list) ):
							raise ValueError(
							    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase )}''' )

						__lowerCAmelCase:       Any            =				image.to(device=UpperCAmelCase      ,  dtype=UpperCAmelCase )

						__lowerCAmelCase:       List[str]            =				batch_size * num_images_per_prompt

						if image.shape[1] == 4:
							__lowerCAmelCase:       List[Any]            =				image

						else:
							if isinstance(UpperCAmelCase      ,  UpperCAmelCase ) and len(UpperCAmelCase ) != batch_size:
								raise ValueError(
								    F'''You have passed a list of generators of length {len(UpperCAmelCase )}, but requested an effective batch'''
								    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

							elif isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
								__lowerCAmelCase:       int            =				[
								    self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(UpperCAmelCase )
								]
								__lowerCAmelCase:       Optional[Any]            =				torch.cat(UpperCAmelCase      ,  dim=0 )
							else:
								__lowerCAmelCase:       Optional[int]            =				self.movq.encode(UpperCAmelCase ).latent_dist.sample(UpperCAmelCase )

							__lowerCAmelCase:       Optional[Any]            =				self.movq.config.scaling_factor * init_latents

						__lowerCAmelCase:       Tuple            =				torch.cat([init_latents]      ,  dim=0 )

						__lowerCAmelCase:       Optional[int]            =				init_latents.shape
						__lowerCAmelCase:       Optional[Any]            =				randn_tensor(UpperCAmelCase      ,  generator=UpperCAmelCase      ,  device=UpperCAmelCase      ,  dtype=UpperCAmelCase )

						# get latents
						__lowerCAmelCase:       Any            =				self.scheduler.add_noise(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )

						__lowerCAmelCase:       Optional[int]            =				init_latents

						return latents


					def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		Optional[int]=0 )							->     List[str]:
						if is_accelerate_available():
							from accelerate import cpu_offload
						else:
							raise ImportError('Please install accelerate via `pip install accelerate`' )

						__lowerCAmelCase:       Optional[Any]            =				torch.device(F'''cuda:{gpu_id}''' )

						__lowerCAmelCase:       Tuple            =				[
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
								cpu_offload(UpperCAmelCase      ,  UpperCAmelCase )


					def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		Optional[Any]=0 )							->     List[str]:
						if is_accelerate_available() and is_accelerate_version('>='      ,  '0.17.0.dev0' ):
							from accelerate import cpu_offload_with_hook
						else:
							raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )

						__lowerCAmelCase:       Dict            =				torch.device(F'''cuda:{gpu_id}''' )

						if self.device.type != "cpu":
							self.to('cpu'      ,  silence_dtype_warnings=UpperCAmelCase )
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						__lowerCAmelCase:       List[str]            =				None
						for cpu_offloaded_model in [self.unet, self.movq]:
							__lowerCAmelCase:       Tuple            =				cpu_offload_with_hook(UpperCAmelCase      ,  UpperCAmelCase      ,  prev_module_hook=UpperCAmelCase )

						# We'll offload the last model manually.
						__lowerCAmelCase:       List[str]            =				hook


					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   UpperCAmelCase  (							self      :		Optional[int] )							->     Tuple:
						if not hasattr(self.unet      ,  '_hf_hook' ):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(UpperCAmelCase      ,  '_hf_hook' )
							    and hasattr(module._hf_hook      ,  'execution_device' )
							    and module._hf_hook.execution_device is not None
							):
								return torch.device(module._hf_hook.execution_device )
						return self.device


					@torch.no_grad()
					@replace_example_docstring(UpperCAmelCase )
					def __call__(							self      :		Tuple      ,  UpperCAmelCase      :		Union[torch.FloatTensor, List[torch.FloatTensor]]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, List[torch.FloatTensor]]      ,  UpperCAmelCase      :		torch.FloatTensor      ,  UpperCAmelCase      :		int = 5_1_2      ,  UpperCAmelCase      :		int = 5_1_2      ,  UpperCAmelCase      :		int = 1_0_0      ,  UpperCAmelCase      :		float = 4.0      ,  UpperCAmelCase      :		float = 0.3      ,  UpperCAmelCase      :		int = 1      ,  UpperCAmelCase      :		Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,  UpperCAmelCase      :		Optional[str] = "pil"      ,  UpperCAmelCase      :		bool = True      ,  )							->     List[str]:
						__lowerCAmelCase:       Union[str, Any]            =				self._execution_device

						__lowerCAmelCase:       List[Any]            =				guidance_scale > 1.0

						if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
							__lowerCAmelCase:       Tuple            =				torch.cat(UpperCAmelCase      ,  dim=0 )
						if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
							__lowerCAmelCase:       Tuple            =				torch.cat(UpperCAmelCase      ,  dim=0 )
						if isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
							__lowerCAmelCase:       Optional[int]            =				torch.cat(UpperCAmelCase      ,  dim=0 )

						__lowerCAmelCase:       Union[str, Any]            =				image_embeds.shape[0]

						if do_classifier_free_guidance:
							__lowerCAmelCase:       Dict            =				image_embeds.repeat_interleave(UpperCAmelCase      ,  dim=0 )
							__lowerCAmelCase:       Optional[Any]            =				negative_image_embeds.repeat_interleave(UpperCAmelCase      ,  dim=0 )
							__lowerCAmelCase:       Tuple            =				hint.repeat_interleave(UpperCAmelCase      ,  dim=0 )

						__lowerCAmelCase:       Optional[int]            =				torch.cat([negative_image_embeds, image_embeds]      ,  dim=0 ).to(dtype=self.unet.dtype      ,  device=UpperCAmelCase )
						__lowerCAmelCase:       Union[str, Any]            =				torch.cat([hint, hint]      ,  dim=0 ).to(dtype=self.unet.dtype      ,  device=UpperCAmelCase )

						if not isinstance(UpperCAmelCase      ,  UpperCAmelCase ):
							__lowerCAmelCase:       Optional[Any]            =				[image]
						if not all(isinstance(UpperCAmelCase      ,  (PIL.Image.Image, torch.Tensor) ) for i in image ):
							raise ValueError(
							    F'''Input is in incorrect format: {[type(UpperCAmelCase ) for i in image]}. Currently, we only support  PIL image and pytorch tensor''' )

						__lowerCAmelCase:       str            =				torch.cat([prepare_image(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase ) for i in image]      ,  dim=0 )
						__lowerCAmelCase:       Tuple            =				image.to(dtype=image_embeds.dtype      ,  device=UpperCAmelCase )

						__lowerCAmelCase:       Union[str, Any]            =				self.movq.encode(UpperCAmelCase )['latents']
						__lowerCAmelCase:       List[str]            =				latents.repeat_interleave(UpperCAmelCase      ,  dim=0 )
						self.scheduler.set_timesteps(UpperCAmelCase      ,  device=UpperCAmelCase )
						__lowerCAmelCase:       Any            =				self.get_timesteps(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
						__lowerCAmelCase:       Any            =				timesteps[:1].repeat(batch_size * num_images_per_prompt )
						__lowerCAmelCase:       Optional[int]            =				downscale_height_and_width(UpperCAmelCase      ,  UpperCAmelCase      ,  self.movq_scale_factor )
						__lowerCAmelCase:       List[Any]            =				self.prepare_latents(
						    UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  image_embeds.dtype      ,  UpperCAmelCase      ,  UpperCAmelCase )
						for i, t in enumerate(self.progress_bar(UpperCAmelCase ) ):
							# expand the latents if we are doing classifier free guidance
							__lowerCAmelCase:       Optional[Any]            =				torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

							__lowerCAmelCase:       List[str]            =				{'image_embeds': image_embeds, 'hint': hint}
							__lowerCAmelCase:       List[str]            =				self.unet(
							    sample=UpperCAmelCase      ,  timestep=UpperCAmelCase      ,  encoder_hidden_states=UpperCAmelCase      ,  added_cond_kwargs=UpperCAmelCase      ,  return_dict=UpperCAmelCase      ,  )[0]

							if do_classifier_free_guidance:
								__lowerCAmelCase:       str            =				noise_pred.split(latents.shape[1]      ,  dim=1 )
								__lowerCAmelCase:       str            =				noise_pred.chunk(2 )
								__lowerCAmelCase:       Optional[Any]            =				variance_pred.chunk(2 )
								__lowerCAmelCase:       Dict            =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
								__lowerCAmelCase:       List[str]            =				torch.cat([noise_pred, variance_pred_text]      ,  dim=1 )

							if not (
							    hasattr(self.scheduler.config      ,  'variance_type' )
							    and self.scheduler.config.variance_type in ["learned", "learned_range"]
							):
								__lowerCAmelCase:       Optional[int]            =				noise_pred.split(latents.shape[1]      ,  dim=1 )

							# compute the previous noisy sample x_t -> x_t-1

							__lowerCAmelCase:       Any            =				self.scheduler.step(
							    UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  generator=UpperCAmelCase      ,  )[0]

						# post-processing
						__lowerCAmelCase:       List[str]            =				self.movq.decode(UpperCAmelCase      ,  force_not_quantize=UpperCAmelCase )['sample']

						if output_type not in ["pt", "np", "pil"]:
							raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )

						if output_type in ["np", "pil"]:
							__lowerCAmelCase:       int            =				image * 0.5 + 0.5
							__lowerCAmelCase:       List[Any]            =				image.clamp(0      ,  1 )
							__lowerCAmelCase:       List[Any]            =				image.cpu().permute(0      ,  2      ,  3      ,  1 ).float().numpy()

						if output_type == "pil":
							__lowerCAmelCase:       Tuple            =				self.numpy_to_pil(UpperCAmelCase )

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=UpperCAmelCase )








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


_a			=       getLogger(__name__)


_a			=       '''cuda''' if torch.cuda.is_available() else '''cpu'''







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      int = 8   , SCREAMING_SNAKE_CASE		:      str = DEFAULT_DEVICE   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

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

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







def _a			(	)   ->   Dict:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






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

 if args.reference_path is None:
  return {}

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

 if args.dump_args:
  scores.update(SCREAMING_SNAKE_CASE		)
 if args.info:
  __lowerCAmelCase:       Any            =				args.info

 if verbose:
  print(SCREAMING_SNAKE_CASE		)

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





