




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__	:				Optional[Any]  =       getLogger(__name__)


SCREAMING_SNAKE_CASE__	:				int  =       'cuda' if torch.cuda.is_available() else 'cpu'

def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE = 8			,_SCREAMING_SNAKE_CASE = DEFAULT_DEVICE			,_SCREAMING_SNAKE_CASE=False			,_SCREAMING_SNAKE_CASE="summarization"			,_SCREAMING_SNAKE_CASE=None			,**_SCREAMING_SNAKE_CASE			,)     ->			Dict:
       lowerCamelCase       :				List[Any]      			= Path(_SCREAMING_SNAKE_CASE   ).open("w"			,encoding="utf-8"   )
       lowerCamelCase       :				Dict      			= str(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[str]      			= AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
       if fpaa:
              lowerCamelCase       :				Tuple      			= model.half()

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

       lowerCamelCase       :				str      			= time.time()
       # update config with task specific params
       use_task_specific_params(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       if prefix is None:
              lowerCamelCase       :				Tuple      			= prefix or getattr(model.config			,"prefix"			,""   ) or ""
       for examples_chunk in tqdm(list(chunks(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )   )   ):
              lowerCamelCase       :				Dict      			= [prefix + text for text in examples_chunk]
              lowerCamelCase       :				int      			= tokenizer(_SCREAMING_SNAKE_CASE			,return_tensors="pt"			,truncation=_SCREAMING_SNAKE_CASE			,padding="longest"   ).to(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				str      			= model.generate(
                  input_ids=batch.input_ids			,attention_mask=batch.attention_mask			,**_SCREAMING_SNAKE_CASE			,)
              lowerCamelCase       :				Tuple      			= 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       :				List[Any]      			= int(time.time() - start_time   )  # seconds
       lowerCamelCase       :				Any      			= len(_SCREAMING_SNAKE_CASE   )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			,4   )}

def 					A				(				)     ->			Optional[Any]:
       return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"   )

def 					A				(				_SCREAMING_SNAKE_CASE=True   )     ->			int:
       lowerCamelCase       :				Tuple      			= 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       :				Optional[int]      			= parser.parse_known_args()
       lowerCamelCase       :				str      			= 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       :				Any      			= [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              lowerCamelCase       :				Optional[int]      			= 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       :				Optional[int]      			= calculate_bleu if "translation" in args.task else calculate_rouge
       lowerCamelCase       :				List[str]      			= [x.rstrip() for x in open(args.save_path   ).readlines()]
       lowerCamelCase       :				List[str]      			= [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       :				Tuple      			= 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)












import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Optional[Any]     					=				CONFIG_NAME







    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = True					,  UpperCamelCase__ = 8					,  UpperCamelCase__ = "lanczos"					,  UpperCamelCase__ = True					,  UpperCamelCase__ = False					,  )					->				Optional[int]:
           super().__init__()







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				PIL.Image.Image:
           if images.ndim == 3:
                  lowerCamelCase       :				str      			= images[None, ...]
           lowerCamelCase       :				Any      			= (images * 255).round().astype("uint8"					)
           if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  lowerCamelCase       :				List[Any]      			= [Image.fromarray(image.squeeze()					,  mode="L"					) for image in images]
           else:
                  lowerCamelCase       :				List[Any]      			= [Image.fromarray(UpperCamelCase__					) for image in images]

           return pil_images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				np.ndarray:
           if not isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  lowerCamelCase       :				List[str]      			= [images]
           lowerCamelCase       :				Union[str, Any]      			= [np.array(UpperCamelCase__					).astype(np.floataa					) / 255.0 for image in images]
           lowerCamelCase       :				List[str]      			= np.stack(UpperCamelCase__					,  axis=0					)

           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				torch.FloatTensor:
           if images.ndim == 3:
                  lowerCamelCase       :				Union[str, Any]      			= images[..., None]

           lowerCamelCase       :				List[Any]      			= torch.from_numpy(images.transpose(0					,  3					,  1					,  2					)					)
           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				np.ndarray:
           lowerCamelCase       :				List[str]      			= images.cpu().permute(0					,  2					,  3					,  1					).float().numpy()
           return images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				int:
           return 2.0 * images - 1.0







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				Dict:
           return (images / 2 + 0.5).clamp(0					,  1					)







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				PIL.Image.Image:
           lowerCamelCase       :				Union[str, Any]      			= image.convert("RGB"					)
           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				PIL.Image.Image:
           if height is None:
                  lowerCamelCase       :				Any      			= image.height
           if width is None:
                  lowerCamelCase       :				Union[str, Any]      			= image.width

           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= (
               x - x % self.config.vae_scale_factor for x in (width, height)
           )  # resize to integer multiple of vae_scale_factor
           lowerCamelCase       :				Optional[Any]      			= image.resize((width, height)					,  resample=PIL_INTERPOLATION[self.config.resample]					)
           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = None					,  )					->				torch.Tensor:
           lowerCamelCase       :				Union[str, Any]      			= (PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(UpperCamelCase__					,  UpperCamelCase__					):
                  lowerCamelCase       :				Optional[int]      			= [image]
           elif not (isinstance(UpperCamelCase__					,  UpperCamelCase__					) and all(isinstance(UpperCamelCase__					,  UpperCamelCase__					) for i in image					)):
                  raise ValueError(
                      F'''Input is in incorrect format: {[type(UpperCamelCase__					) for i in image]}. Currently, we only support {", ".join(UpperCamelCase__					)}'''					)

           if isinstance(image[0]					,  PIL.Image.Image					):
                  if self.config.do_convert_rgb:
                         lowerCamelCase       :				Tuple      			= [self.convert_to_rgb(UpperCamelCase__					) for i in image]
                  if self.config.do_resize:
                         lowerCamelCase       :				Tuple      			= [self.resize(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					) for i in image]
                  lowerCamelCase       :				Optional[Any]      			= self.pil_to_numpy(UpperCamelCase__					)  # to np
                  lowerCamelCase       :				List[str]      			= self.numpy_to_pt(UpperCamelCase__					)  # to pt

           elif isinstance(image[0]					,  np.ndarray					):
                  lowerCamelCase       :				Optional[int]      			= np.concatenate(UpperCamelCase__					,  axis=0					) if image[0].ndim == 4 else np.stack(UpperCamelCase__					,  axis=0					)
                  lowerCamelCase       :				Optional[int]      			= self.numpy_to_pt(UpperCamelCase__					)
                  lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Optional[int]      			= image.shape
                  if self.config.do_resize and (
                      height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                  ):
                         raise ValueError(
                             F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                             F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''					)

           elif isinstance(image[0]					,  torch.Tensor					):
                  lowerCamelCase       :				Dict      			= torch.cat(UpperCamelCase__					,  axis=0					) if image[0].ndim == 4 else torch.stack(UpperCamelCase__					,  axis=0					)
                  lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= image.shape

                  # don't need any preprocess if the image is latents
                  if channel == 4:
                         return image

                  if self.config.do_resize and (
                      height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                  ):
                         raise ValueError(
                             F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                             F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''					)

        # expected range [0,1], normalize to [-1,1]
           lowerCamelCase       :				str      			= self.config.do_normalize
           if image.min() < 0:
                  warnings.warn(
                      "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "
                      F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''					,  UpperCamelCase__					,  )
                  lowerCamelCase       :				Optional[Any]      			= False

           if do_normalize:
                  lowerCamelCase       :				int      			= self.normalize(UpperCamelCase__					)

           return image







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = None					,  )					->				Dict:
           if not isinstance(UpperCamelCase__					,  torch.Tensor					):
                  raise ValueError(
                      F'''Input for postprocessing is in incorrect format: {type(UpperCamelCase__					)}. We only support pytorch tensor'''					)
           if output_type not in ["latent", "pt", "np", "pil"]:
                  lowerCamelCase       :				str      			= (
                      F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                      "`pil`, `np`, `pt`, `latent`"
                  )
                  deprecate("Unsupported output_type"					,  "1.0.0"					,  UpperCamelCase__					,  standard_warn=UpperCamelCase__					)
                  lowerCamelCase       :				Optional[Any]      			= "np"

           if output_type == "latent":
                  return image

           if do_denormalize is None:
                  lowerCamelCase       :				List[str]      			= [self.config.do_normalize] * image.shape[0]

           lowerCamelCase       :				Dict      			= torch.stack(
               [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

           if output_type == "pt":
                  return image

           lowerCamelCase       :				Any      			= self.pt_to_numpy(UpperCamelCase__					)

           if output_type == "np":
                  return image

           if output_type == "pil":
                  return self.numpy_to_pil(UpperCamelCase__					)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Union[str, Any]     					=				CONFIG_NAME







    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = True					,  UpperCamelCase__ = 8					,  UpperCamelCase__ = "lanczos"					,  UpperCamelCase__ = True					,  )					->				Tuple:
           super().__init__()







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				int:
           if images.ndim == 3:
                  lowerCamelCase       :				Union[str, Any]      			= images[None, ...]
           lowerCamelCase       :				Any      			= (images * 255).round().astype("uint8"					)
           if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  lowerCamelCase       :				Any      			= [Image.fromarray(image.squeeze()					,  mode="L"					) for image in images]
           else:
                  lowerCamelCase       :				Dict      			= [Image.fromarray(image[:, :, :3]					) for image in images]

           return pil_images







    @staticmethod
    def    _lowercase					(   UpperCamelCase__					)					->				Optional[int]:
           return image[:, :, 1] * 2**8 + image[:, :, 2]







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Any:
           if images.ndim == 3:
                  lowerCamelCase       :				Dict      			= images[None, ...]
           lowerCamelCase       :				Any      			= images[:, :, :, 3:]
           if images.shape[-1] == 6:
                  lowerCamelCase       :				List[str]      			= (images_depth * 255).round().astype("uint8"					)
                  lowerCamelCase       :				Any      			= [
                      Image.fromarray(self.rgblike_to_depthmap(UpperCamelCase__					)					,  mode="I;16"					) for image_depth in images_depth
                  ]
           elif images.shape[-1] == 4:
                  lowerCamelCase       :				Any      			= (images_depth * 65535.0).astype(np.uintaa					)
                  lowerCamelCase       :				Any      			= [Image.fromarray(UpperCamelCase__					,  mode="I;16"					) for image_depth in images_depth]
           else:
                  raise Exception("Not supported"					)

           return pil_images







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = "pil"					,  UpperCamelCase__ = None					,  )					->				Dict:
           if not isinstance(UpperCamelCase__					,  torch.Tensor					):
                  raise ValueError(
                      F'''Input for postprocessing is in incorrect format: {type(UpperCamelCase__					)}. We only support pytorch tensor'''					)
           if output_type not in ["latent", "pt", "np", "pil"]:
                  lowerCamelCase       :				List[str]      			= (
                      F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                      "`pil`, `np`, `pt`, `latent`"
                  )
                  deprecate("Unsupported output_type"					,  "1.0.0"					,  UpperCamelCase__					,  standard_warn=UpperCamelCase__					)
                  lowerCamelCase       :				Tuple      			= "np"

           if do_denormalize is None:
                  lowerCamelCase       :				Union[str, Any]      			= [self.config.do_normalize] * image.shape[0]

           lowerCamelCase       :				List[Any]      			= torch.stack(
               [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

           lowerCamelCase       :				Union[str, Any]      			= self.pt_to_numpy(UpperCamelCase__					)

           if output_type == "np":
                  if image.shape[-1] == 6:
                         lowerCamelCase       :				List[str]      			= np.stack([self.rgblike_to_depthmap(im[:, :, 3:]					) for im in image]					,  axis=0					)
                  else:
                         lowerCamelCase       :				Optional[Any]      			= image[:, :, :, 3:]
                  return image[:, :, :, :3], image_depth

           if output_type == "pil":
                  return self.numpy_to_pil(UpperCamelCase__					), self.numpy_to_depth(UpperCamelCase__					)
           else:
                  raise Exception(F'''This type {output_type} is not supported'''					)






