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		:     List[str]         =	getLogger(__name__)


SCREAMING_SNAKE_CASE		:     int         =	"cuda" if torch.cuda.is_available() else "cpu"






def     lowerCAmelCase_			(   _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     :				Optional[int]=False		, _SCREAMING_SNAKE_CASE     :				Optional[int]="summarization"		, _SCREAMING_SNAKE_CASE     :				Dict=None		, **_SCREAMING_SNAKE_CASE     :				int		, ):
						UpperCamelCase_		:							List[str]  =		Path(_SCREAMING_SNAKE_CASE			).open("""w"""		, encoding="""utf-8"""			)
						UpperCamelCase_		:							Dict  =		str(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Optional[int]  =		AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
						if fpaa:
												UpperCamelCase_		:							Optional[Any]  =		model.half()

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

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






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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]=True			):
						UpperCamelCase_		:							int  =		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
						UpperCamelCase_,UpperCamelCase_		:							int  =		parser.parse_known_args()
						UpperCamelCase_		:							List[str]  =		parse_numeric_n_bool_cl_kwargs(_SCREAMING_SNAKE_CASE			)
						if parsed_args and verbose:
												print(f'''parsed the following generate kwargs: {parsed_args}'''			)
						UpperCamelCase_		:							List[str]  =		[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path			).readlines()]
						if args.n_obs > 0:
												UpperCamelCase_		:							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.'''			)

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError("""Can't mix --fp16 and --device cpu"""			)

						UpperCamelCase_		:							Any  =		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
						UpperCamelCase_		:							Optional[int]  =		calculate_bleu if """translation""" in args.task else calculate_rouge
						UpperCamelCase_		:							Optional[Any]  =		[x.rstrip() for x in open(args.save_path			).readlines()]
						UpperCamelCase_		:							Dict  =		[x.rstrip() for x in open(args.reference_path			).readlines()][: len(_SCREAMING_SNAKE_CASE			)]
						UpperCamelCase_		:							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:
												UpperCamelCase_		:							Optional[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)







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
	import torch

if is_tf_available():
	import tensorflow as tf

class        UpperCamelCase						(				__a				):
						a__		:str      =			['''image_processor''']
						a__		:str      =			'''SamImageProcessor'''





						def __init__(self			,     __UpperCamelCase						)				->				int:
												super().__init__(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		self.image_processor
												UpperCamelCase_		:							int  =		-10
												UpperCamelCase_		:							Dict  =		self.image_processor.size["""longest_edge"""]





						def __call__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				BatchEncoding:
												UpperCamelCase_		:							int  =		self.image_processor(
												    __UpperCamelCase			,     return_tensors=__UpperCamelCase			,     **__UpperCamelCase			,     )

												# pop arguments that are not used in the foward but used nevertheless
												UpperCamelCase_		:							Optional[int]  =		encoding_image_processor["""original_sizes"""]

												if hasattr(__UpperCamelCase			,     """numpy"""						):  # Checks if Torch or TF tensor
																		UpperCamelCase_		:							Dict  =		original_sizes.numpy()

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							str  =		self._check_and_preprocess_points(
												    input_points=__UpperCamelCase			,     input_labels=__UpperCamelCase			,     input_boxes=__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		self._normalize_and_convert(
												    __UpperCamelCase			,     __UpperCamelCase			,     input_points=__UpperCamelCase			,     input_labels=__UpperCamelCase			,     input_boxes=__UpperCamelCase			,     return_tensors=__UpperCamelCase			,     )

												return encoding_image_processor





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase="pt"			,     )				->				Tuple:
												if input_points is not None:
																		if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																								UpperCamelCase_		:							str  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     original_sizes[0]						) for point in input_points
																								]
																		else:
																								UpperCamelCase_		:							List[str]  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     __UpperCamelCase						)
																								    for point, original_size in zip(__UpperCamelCase			,     __UpperCamelCase						)
																								]
																		# check that all arrays have the same shape
																		if not all(point.shape == input_points[0].shape for point in input_points						):
																								if input_labels is not None:
																														UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		self._pad_points_and_labels(__UpperCamelCase			,     __UpperCamelCase						)

																		UpperCamelCase_		:							Dict  =		np.array(__UpperCamelCase						)

												if input_labels is not None:
																		UpperCamelCase_		:							Optional[Any]  =		np.array(__UpperCamelCase						)

												if input_boxes is not None:
																		if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																								UpperCamelCase_		:							Tuple  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     original_sizes[0]			,     is_bounding_box=__UpperCamelCase						)
																								    for box in input_boxes
																								]
																		else:
																								UpperCamelCase_		:							Any  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     __UpperCamelCase			,     is_bounding_box=__UpperCamelCase						)
																								    for box, original_size in zip(__UpperCamelCase			,     __UpperCamelCase						)
																								]
																		UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)

												if input_boxes is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Union[str, Any]  =		torch.from_numpy(__UpperCamelCase						)
																								# boxes batch size of 1 by default
																								UpperCamelCase_		:							Any  =		input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							List[str]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# boxes batch size of 1 by default
																								UpperCamelCase_		:							Optional[int]  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_boxes.shape						) != 3 else input_boxes
																		encoding_image_processor.update({"""input_boxes""": input_boxes}						)
												if input_points is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Optional[Any]  =		torch.from_numpy(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							List[Any]  =		input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							Optional[Any]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Dict  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_points.shape						) != 4 else input_points
																		encoding_image_processor.update({"""input_points""": input_points}						)
												if input_labels is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Optional[int]  =		torch.from_numpy(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Tuple  =		input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							Optional[Any]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Dict  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_labels.shape						) != 3 else input_labels
																		encoding_image_processor.update({"""input_labels""": input_labels}						)

												return encoding_image_processor





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Tuple:
												UpperCamelCase_		:							Union[str, Any]  =		max([point.shape[0] for point in input_points]						)
												UpperCamelCase_		:							Any  =		[]
												for i, point in enumerate(__UpperCamelCase						):
																		if point.shape[0] != expected_nb_points:
																								UpperCamelCase_		:							Optional[int]  =		np.concatenate(
																								    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]			,     axis=0						)
																								UpperCamelCase_		:							Dict  =		np.append(input_labels[i]			,     [self.point_pad_value]						)
																		processed_input_points.append(__UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		processed_input_points
												return input_points, input_labels





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=False						)				->				np.ndarray:
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		original_size
												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		self.image_processor._get_preprocess_shape(__UpperCamelCase			,     longest_edge=__UpperCamelCase						)
												UpperCamelCase_		:							str  =		deepcopy(__UpperCamelCase						).astype(__UpperCamelCase						)

												if is_bounding_box:
																		UpperCamelCase_		:							int  =		coords.reshape(-1			,     2			,     2						)

												UpperCamelCase_		:							Tuple  =		coords[..., 0] * (new_w / old_w)
												UpperCamelCase_		:							Optional[int]  =		coords[..., 1] * (new_h / old_h)

												if is_bounding_box:
																		UpperCamelCase_		:							Optional[Any]  =		coords.reshape(-1			,     4						)

												return coords





						def        A_							(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     )				->				List[str]:
												if input_points is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):  # Checks for TF or Torch tensor
																								UpperCamelCase_		:							int  =		input_points.numpy().tolist()

																		if not isinstance(__UpperCamelCase			,     __UpperCamelCase						) or not isinstance(input_points[0]			,     __UpperCamelCase						):
																								raise ValueError("""Input points must be a list of list of floating points."""						)
																		UpperCamelCase_		:							List[Any]  =		[np.array(__UpperCamelCase						) for input_point in input_points]
												else:
																		UpperCamelCase_		:							str  =		None

												if input_labels is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):
																								UpperCamelCase_		:							Any  =		input_labels.numpy().tolist()

																		if not isinstance(__UpperCamelCase			,     __UpperCamelCase						) or not isinstance(input_labels[0]			,     __UpperCamelCase						):
																								raise ValueError("""Input labels must be a list of list integers."""						)
																		UpperCamelCase_		:							List[str]  =		[np.array(__UpperCamelCase						) for label in input_labels]
												else:
																		UpperCamelCase_		:							List[str]  =		None

												if input_boxes is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):
																								UpperCamelCase_		:							Dict  =		input_boxes.numpy().tolist()

																		if (
																		    not isinstance(__UpperCamelCase			,     __UpperCamelCase						)
																		    or not isinstance(input_boxes[0]			,     __UpperCamelCase						)
																		    or not isinstance(input_boxes[0][0]			,     __UpperCamelCase						)
																		):
																								raise ValueError("""Input boxes must be a list of list of list of floating points."""						)
																		UpperCamelCase_		:							Tuple  =		[np.array(__UpperCamelCase						).astype(np.floataa						) for box in input_boxes]
												else:
																		UpperCamelCase_		:							List[str]  =		None

												return input_points, input_labels, input_boxes





						@property
						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							str  =		self.image_processor.model_input_names
												return list(dict.fromkeys(__UpperCamelCase						)						)





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				Optional[Any]:
												return self.image_processor.post_process_masks(*__UpperCamelCase			,     **__UpperCamelCase						)






