
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


_UpperCAmelCase   :					Any    =			getLogger(__name__)


_UpperCAmelCase   :					Tuple    =			"cuda" if torch.cuda.is_available() else "cpu"


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase = 8       ,			lowercase = DEFAULT_DEVICE       ,			lowercase=False       ,			lowercase="summarization"       ,			lowercase=None       ,			**lowercase       ,			)  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	Path(lowercase					).open('w'       ,			encoding='utf-8'					)
		UpperCamelCase            =	str(lowercase					)
		UpperCamelCase            =	AutoModelForSeqaSeqLM.from_pretrained(lowercase					).to(lowercase					)
		if fpaa:
				UpperCamelCase            =	model.half()

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

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


def  A			(   )  ->    Optional[Any]:
		'''simple docstring'''

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


def  A			(   lowercase=True					)  ->    List[str]:
		'''simple docstring'''

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

		if args.reference_path is None:
				return {}

		# Compute scores
		UpperCamelCase            =	calculate_bleu if 'translation' in args.task else calculate_rouge
		UpperCamelCase            =	[x.rstrip() for x in open(args.save_path					).readlines()]
		UpperCamelCase            =	[x.rstrip() for x in open(args.reference_path					).readlines()][: len(lowercase					)]
		UpperCamelCase            =	score_fn(lowercase       ,			lowercase					)
		scores.update(lowercase					)

		if args.dump_args:
				scores.update(lowercase					)
		if args.info:
				UpperCamelCase            =	args.info

		if verbose:
				print(lowercase					)

		if args.score_path is not None:
				json.dump(lowercase       ,			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 csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def  A			(   lowercase=None       ,			lowercase=None					)  ->    Tuple:
		'''simple docstring'''

		return field(default_factory=lambda: default       ,			metadata=lowercase					)


@dataclass
class 							lowercase   :
		__lowercase			:			str      	=							field(
		    metadata={"help": "The csv file to plot."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Disable logarithmic scale when plotting"}    ,  )
		__lowercase			:			bool      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={
		        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
		    }    ,  )
		__lowercase			:			Optional[str]      	=							field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,  )
		__lowercase			:			Optional[List[str]]      	=							list_field(
		    default=_SCREAMING_SNAKE_CASE    ,  metadata={"help": "List of model names that are used instead of the ones in the csv file."}  )






def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

		try:
				int(lowercase					)
				return True
		except ValueError:
				return False


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		try:
				float(lowercase					)
				return True
		except ValueError:
				return False


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

				with open(self.args.csv_file							, newline=''			) as csv_file:
						UpperCamelCase            =	csv.DictReader(A_			)
						for row in reader:
								UpperCamelCase            =	row['model']
								self.result_dict[model_name]["bsz"].append(int(row['batch_size']			)			)
								self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']			)			)
								if can_convert_to_int(row['result']			):
										# value is not None
										UpperCamelCase            =	int(row['result']			)
								elif can_convert_to_float(row['result']			):
										# value is not None
										UpperCamelCase            =	float(row['result']			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	plt.subplots()
				UpperCamelCase            =	'Time usage' if self.args.is_time else 'Memory usage'
				UpperCamelCase            =	title_str + ' for training' if self.args.is_train else title_str + ' for inference'

				if not self.args.no_log_scale:
						# set logarithm scales
						ax.set_xscale('log'			)
						ax.set_yscale('log'			)

				for axis in [ax.xaxis, ax.yaxis]:
						axis.set_major_formatter(ScalarFormatter()			)

				for model_name_idx, model_name in enumerate(self.result_dict.keys()			):
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['bsz']			)			)
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
						UpperCamelCase            =	self.result_dict[model_name]['result']

						((UpperCamelCase)      ,    (UpperCamelCase))            =	(
						    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
						)

						UpperCamelCase            =	(
						    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
						)

						for inner_loop_value in inner_loop_array:
								if self.args.plot_along_batch:
										UpperCamelCase            =	np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							, dtype=A_							, )
								else:
										UpperCamelCase            =	np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							, dtype=np.floataa							, )

								((UpperCamelCase)      ,    (UpperCamelCase))            =	(
								    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
								)

								UpperCamelCase            =	np.asarray(A_							, A_			)[: len(A_			)]
								plt.scatter(
								    A_							, A_							, label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''			)
								plt.plot(A_							, A_							, '--'			)

						title_str += F''' {label_model_name} vs.'''

				UpperCamelCase            =	title_str[:-4]
				UpperCamelCase            =	'Time in s' if self.args.is_time else 'Memory in MB'

				# plot
				plt.title(A_			)
				plt.xlabel(A_			)
				plt.ylabel(A_			)
				plt.legend()

				if self.args.figure_png_file is not None:
						plt.savefig(self.args.figure_png_file			)
				else:
						plt.show()






def  A			(   )  ->    Dict:
		'''simple docstring'''

		UpperCamelCase            =	HfArgumentParser(lowercase					)
		UpperCamelCase            =	parser.parse_args_into_dataclasses()[0]
		UpperCamelCase            =	Plot(args=lowercase					)
		plot.plot()


if __name__ == "__main__":
					main()
