

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


lowerCAmelCase							:  Tuple            =						getLogger(__name__)


lowerCAmelCase							:  Optional[Any]            =						"""cuda""" if torch.cuda.is_available() else """cpu"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = 8    ,     _UpperCAmelCase = DEFAULT_DEVICE    ,     _UpperCAmelCase=False    ,     _UpperCAmelCase="summarization"    ,     _UpperCAmelCase=None    ,     **_UpperCAmelCase    ,     ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Path(_UpperCAmelCase   ).open("w"    ,     encoding="utf-8"   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	str(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase   ).to(_UpperCAmelCase   )
						if fpaa:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	model.half()

						SCREAMING_SNAKE_CASE_:			int  		=	AutoTokenizer.from_pretrained(_UpperCAmelCase   )
						logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"   )  # if this is wrong, check config.model_type.

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







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







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

						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"   )

						SCREAMING_SNAKE_CASE_:			List[Any]  		=	generate_summaries_or_translations(
						    _UpperCAmelCase    ,     args.save_path    ,     args.model_name    ,     batch_size=args.bs    ,     device=args.device    ,     fpaa=args.fpaa    ,     task=args.task    ,     prefix=args.prefix    ,     **_UpperCAmelCase    ,     )

						if args.reference_path is None:
												return {}

						# Compute scores
						SCREAMING_SNAKE_CASE_:			Dict  		=	calculate_bleu if "translation" in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE_:			int  		=	[x.rstrip() for x in open(args.save_path   ).readlines()]
						SCREAMING_SNAKE_CASE_:			Any  		=	[x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_UpperCAmelCase   )]
						SCREAMING_SNAKE_CASE_:			dict  		=	score_fn(_UpperCAmelCase    ,     _UpperCAmelCase   )
						scores.update(_UpperCAmelCase   )

						if args.dump_args:
												scores.update(_UpperCAmelCase   )
						if args.info:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	args.info

						if verbose:
												print(_UpperCAmelCase   )

						if args.score_path is not None:
												json.dump(_UpperCAmelCase    ,     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 re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
						from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
						from tensorflow.keras.optimizers import Adam



class   __lowercase    (			tf.keras.optimizers.schedules.LearningRateSchedule     ):




			"""simple docstring"""







			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					float       ,						lowerCAmelCase__  :					Callable       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					float = 1.0       ,						lowerCAmelCase__  :					str = None       ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_:			str  		=	initial_learning_rate
									SCREAMING_SNAKE_CASE_:			Dict  		=	warmup_steps
									SCREAMING_SNAKE_CASE_:			Any  		=	power
									SCREAMING_SNAKE_CASE_:			int  		=	decay_schedule_fn
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	name



			def __call__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Any):
									with tf.name_scope(self.name or "WarmUp") as name:
															# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
															# learning rate will be `global_step/num_warmup_steps * init_lr`.
															SCREAMING_SNAKE_CASE_:			Any  		=	tf.cast(lowerCAmelCase__       ,						tf.floataa)
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tf.cast(self.warmup_steps       ,						tf.floataa)
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	global_step_float / warmup_steps_float
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.initial_learning_rate * tf.math.pow(lowerCAmelCase__       ,						self.power)
															return tf.cond(
															    global_step_float < warmup_steps_float       ,						lambda: warmup_learning_rate       ,						lambda: self.decay_schedule_fn(step - self.warmup_steps)       ,						name=lowerCAmelCase__       ,						)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									return {
									    "initial_learning_rate": self.initial_learning_rate,
									    "decay_schedule_fn": self.decay_schedule_fn,
									    "warmup_steps": self.warmup_steps,
									    "power": self.power,
									    "name": self.name,
									}







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = 0.0    ,     _UpperCAmelCase = 0.9    ,     _UpperCAmelCase = 0.9_9_9    ,     _UpperCAmelCase = 1e-8    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = 0.0    ,     _UpperCAmelCase = 1.0    ,     _UpperCAmelCase = None    ,     ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tf.keras.optimizers.schedules.PolynomialDecay(
						    initial_learning_rate=_UpperCAmelCase    ,     decay_steps=num_train_steps - num_warmup_steps    ,     end_learning_rate=init_lr * min_lr_ratio    ,     power=_UpperCAmelCase    ,     )
						if num_warmup_steps:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	WarmUp(
												    initial_learning_rate=_UpperCAmelCase    ,     decay_schedule_fn=_UpperCAmelCase    ,     warmup_steps=_UpperCAmelCase    ,     )
						if weight_decay_rate > 0.0:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	AdamWeightDecay(
												    learning_rate=_UpperCAmelCase    ,     weight_decay_rate=_UpperCAmelCase    ,     beta_a=_UpperCAmelCase    ,     beta_a=_UpperCAmelCase    ,     epsilon=_UpperCAmelCase    ,     clipnorm=_UpperCAmelCase    ,     global_clipnorm=_UpperCAmelCase    ,     exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]    ,     include_in_weight_decay=_UpperCAmelCase    ,     )
						else:
												SCREAMING_SNAKE_CASE_:			int  		=	tf.keras.optimizers.Adam(
												    learning_rate=_UpperCAmelCase    ,     beta_a=_UpperCAmelCase    ,     beta_a=_UpperCAmelCase    ,     epsilon=_UpperCAmelCase    ,     clipnorm=_UpperCAmelCase    ,     global_clipnorm=_UpperCAmelCase    ,     )
						# We return the optimizer and the LR scheduler in order to better track the
						# evolution of the LR independently of the optimizer.
						return optimizer, lr_schedule



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001       ,						lowerCAmelCase__  :					float = 0.9       ,						lowerCAmelCase__  :					float = 0.999       ,						lowerCAmelCase__  :					float = 1E-7       ,						lowerCAmelCase__  :					bool = False       ,						lowerCAmelCase__  :					float = 0.0       ,						lowerCAmelCase__  :					Optional[List[str]] = None       ,						lowerCAmelCase__  :					Optional[List[str]] = None       ,						lowerCAmelCase__  :					str = "AdamWeightDecay"       ,						**lowerCAmelCase__  :					int       ,						):
									super().__init__(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	weight_decay_rate
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	include_in_weight_decay
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	exclude_from_weight_decay



			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (			cls  :					Dict       ,						lowerCAmelCase__  :					Dict):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{"WarmUp": WarmUp}
									return super(lowerCAmelCase__       ,						cls).from_config(lowerCAmelCase__       ,						custom_objects=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Optional[int]):
									super(lowerCAmelCase__       ,						self)._prepare_local(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tf.constant(
									    self.weight_decay_rate       ,						name="adam_weight_decay_rate")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Tuple):
									SCREAMING_SNAKE_CASE_:			str  		=	self._do_use_weight_decay(var.name)
									if do_decay:
															return var.assign_sub(
															    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]       ,						use_locking=self._use_locking       ,						)
									return tf.no_op()



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Optional[Any]=None       ,						**lowerCAmelCase__  :					List[str]):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Any  		=	list(zip(*lowerCAmelCase__))
									return super(lowerCAmelCase__       ,						self).apply_gradients(zip(lowerCAmelCase__       ,						lowerCAmelCase__)       ,						name=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Tuple):
									if apply_state is None:
															return self._decayed_lr_t[var_dtype], {}

									SCREAMING_SNAKE_CASE_:			Dict  		=	apply_state or {}
									SCREAMING_SNAKE_CASE_:			List[str]  		=	apply_state.get((var_device, var_dtype))
									if coefficients is None:
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._fallback_apply_state(lowerCAmelCase__       ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:			Any  		=	coefficients

									return coefficients["lr_t"], {"apply_state": apply_state}



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Tuple=None):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._decay_weights_op(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									with tf.control_dependencies([decay]):
															return super(lowerCAmelCase__       ,						self)._resource_apply_dense(lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Dict       ,						lowerCAmelCase__  :					Dict=None):
									SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			List[str]  		=	self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	self._decay_weights_op(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__)
									with tf.control_dependencies([decay]):
															return super(lowerCAmelCase__       ,						self)._resource_apply_sparse(lowerCAmelCase__       ,						lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	super().get_config()
									config.update({"weight_decay_rate": self.weight_decay_rate})
									return config




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Tuple):
									if self.weight_decay_rate == 0:
															return False

									if self._include_in_weight_decay:
															for r in self._include_in_weight_decay:
																					if re.search(lowerCAmelCase__       ,						lowerCAmelCase__) is not None:
																											return True

									if self._exclude_from_weight_decay:
															for r in self._exclude_from_weight_decay:
																					if re.search(lowerCAmelCase__       ,						lowerCAmelCase__) is not None:
																											return False
									return True







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	[]
									SCREAMING_SNAKE_CASE_:			Any  		=	None



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									if self._accum_steps is None:
															SCREAMING_SNAKE_CASE_:			Tuple  		=	tf.Variable(
															    tf.constant(0       ,						dtype=tf.intaa)       ,						trainable=lowerCAmelCase__       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)

									return self._accum_steps.value()



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									if not self._gradients:
															raise ValueError("The accumulator should be called first to initialize the gradients")
									return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]



			def __call__(			self  :					str       ,						lowerCAmelCase__  :					Tuple):
									if not self._gradients:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.step  # Create the step variable.
															self._gradients.extend(
															    [
															        tf.Variable(
															            tf.zeros_like(lowerCAmelCase__)       ,						trainable=lowerCAmelCase__       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)
															        if gradient is not None
															        else gradient
															        for gradient in gradients
															    ])
									if len(lowerCAmelCase__) != len(self._gradients):
															raise ValueError(F"Expected {len(self._gradients)} gradients, but got {len(lowerCAmelCase__)}")

									for accum_gradient, gradient in zip(self._gradients       ,						lowerCAmelCase__):
															if accum_gradient is not None and gradient is not None:
																					accum_gradient.assign_add(lowerCAmelCase__)

									self._accum_steps.assign_add(1)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									if not self._gradients:
															return
									self._accum_steps.assign(0)
									for gradient in self._gradients:
															if gradient is not None:
																					gradient.assign(tf.zeros_like(lowerCAmelCase__))






