
"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase		:    int			=       logging.get_logger(__name__)




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    ['''pixel_values''']






							def __init__(       self    :      Union[str, Any]    ,			snake_case__    :      bool = True    ,			snake_case__    :      Optional[Dict[str, int]] = None    ,			snake_case__    :      PILImageResampling = PILImageResampling.BILINEAR    ,			snake_case__    :      bool = True    ,			snake_case__    :      Union[int, float] = 1 / 2_5_5    ,			snake_case__    :      bool = True    ,			snake_case__    :      Optional[Union[float, List[float]]] = None    ,			snake_case__    :      Optional[Union[float, List[float]]] = None    ,			**snake_case__    :      int    ,			):






												"""simple docstring"""



												super().__init__(**snake_case__     )
												__a:    Any		     =       size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
												__a:    int		     =       get_size_dict(snake_case__     )
												__a:    Union[str, Any]		     =       do_resize
												__a:    Optional[Any]		     =       do_rescale
												__a:    str		     =       do_normalize
												__a:    str		     =       size
												__a:    Tuple		     =       resample
												__a:    Tuple		     =       rescale_factor
												__a:    Any		     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												__a:    List[str]		     =       image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      np.ndarray    ,			snake_case__    :      Dict[str, int]    ,			snake_case__    :      PILImageResampling = PILImageResampling.BILINEAR    ,			snake_case__    :      Optional[Union[str, ChannelDimension]] = None    ,			**snake_case__    :      Union[str, Any]    ,			):






												"""simple docstring"""



												__a:    Any		     =       get_size_dict(snake_case__     )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""     )
												return resize(
												    snake_case__    ,			size=(size["""height"""], size["""width"""])    ,			resample=snake_case__    ,			data_format=snake_case__    ,			**snake_case__     )






							def 				__lowercase	(       self    :      str    ,			snake_case__    :      np.ndarray    ,			snake_case__    :      float    ,			snake_case__    :      Optional[Union[str, ChannelDimension]] = None    ,			**snake_case__    :      List[Any]     ):






												"""simple docstring"""



												return rescale(snake_case__    ,			scale=snake_case__    ,			data_format=snake_case__    ,			**snake_case__     )






							def 				__lowercase	(       self    :      int    ,			snake_case__    :      np.ndarray    ,			snake_case__    :      Union[float, List[float]]    ,			snake_case__    :      Union[float, List[float]]    ,			snake_case__    :      Optional[Union[str, ChannelDimension]] = None    ,			**snake_case__    :      int    ,			):






												"""simple docstring"""



												return normalize(snake_case__    ,			mean=snake_case__    ,			std=snake_case__    ,			data_format=snake_case__    ,			**snake_case__     )






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      ImageInput    ,			snake_case__    :      Optional[bool] = None    ,			snake_case__    :      Dict[str, int] = None    ,			snake_case__    :      PILImageResampling = None    ,			snake_case__    :      Optional[bool] = None    ,			snake_case__    :      Optional[float] = None    ,			snake_case__    :      Optional[bool] = None    ,			snake_case__    :      Optional[Union[float, List[float]]] = None    ,			snake_case__    :      Optional[Union[float, List[float]]] = None    ,			snake_case__    :      Optional[Union[str, TensorType]] = None    ,			snake_case__    :      Union[str, ChannelDimension] = ChannelDimension.FIRST    ,			**snake_case__    :      Dict    ,			):






												"""simple docstring"""



												__a:    Optional[int]		     =       do_resize if do_resize is not None else self.do_resize
												__a:    str		     =       do_rescale if do_rescale is not None else self.do_rescale
												__a:    List[Any]		     =       do_normalize if do_normalize is not None else self.do_normalize
												__a:    List[str]		     =       resample if resample is not None else self.resample
												__a:    Any		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
												__a:    int		     =       image_mean if image_mean is not None else self.image_mean
												__a:    List[Any]		     =       image_std if image_std is not None else self.image_std

												__a:    str		     =       size if size is not None else self.size
												__a:    Optional[int]		     =       get_size_dict(snake_case__     )

												__a:    Optional[int]		     =       make_list_of_images(snake_case__     )

												if not valid_images(snake_case__     ):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""     )

												if do_resize and size is None:
																	raise ValueError("""Size must be specified if do_resize is True."""     )

												if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""     )

												# All transformations expect numpy arrays.
												__a:    List[str]		     =       [to_numpy_array(snake_case__     ) for image in images]

												if do_resize:
																	__a:    Tuple		     =       [self.resize(image=snake_case__    ,			size=snake_case__    ,			resample=snake_case__     ) for image in images]

												if do_rescale:
																	__a:    Tuple		     =       [self.rescale(image=snake_case__    ,			scale=snake_case__     ) for image in images]

												if do_normalize:
																	__a:    Optional[Any]		     =       [self.normalize(image=snake_case__    ,			mean=snake_case__    ,			std=snake_case__     ) for image in images]

												__a:    Tuple		     =       [to_channel_dimension_format(snake_case__    ,			snake_case__     ) for image in images]

												__a:    Optional[Any]		     =       {"""pixel_values""": images}
												return BatchFeature(data=snake_case__    ,			tensor_type=snake_case__     )


"""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


lowercase		:    Dict			=       getLogger(__name__)


lowercase		:    Dict			=       'cuda' if torch.cuda.is_available() else 'cpu'


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_ = 8						,					lowerCamelCase_ = DEFAULT_DEVICE						,					lowerCamelCase_=False						,					lowerCamelCase_="summarization"						,					lowerCamelCase_=None						,					**lowerCamelCase_						,					)  ->      Dict:


					'''simple docstring'''
					__a:    str		     =       Path(lowerCamelCase_			).open("""w"""						,					encoding="""utf-8"""			)
					__a:    Tuple		     =       str(lowerCamelCase_			)
					__a:    List[Any]		     =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).to(lowerCamelCase_			)
					if fpaa:
										__a:    List[str]		     =       model.half()

					__a:    Optional[int]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_			)
					logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

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


def      lowerCamelCase_     (						)  ->      str:


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


def      lowerCamelCase_     (						lowerCamelCase_=True			)  ->      Any:


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

					if args.reference_path is None:
										return {}

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

					if args.dump_args:
										scores.update(lowerCamelCase_			)
					if args.info:
										__a:    Optional[Any]		     =       args.info

					if verbose:
										print(lowerCamelCase_			)

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