
'''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


lowerCAmelCase :  Dict      =      getLogger(__name__)


lowerCAmelCase :  Optional[int]      =      'cuda' if torch.cuda.is_available() else 'cpu'







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

							UpperCamelCase					       =			AutoTokenizer.from_pretrained(A)
							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(A						,			A)
							if prefix is None:
														UpperCamelCase					       =			prefix or getattr(model.config						,			'prefix'						,			'') or ''
							for examples_chunk in tqdm(list(chunks(A						,			A))):
														UpperCamelCase					       =			[prefix + text for text in examples_chunk]
														UpperCamelCase					       =			tokenizer(A						,			return_tensors='pt'						,			truncation=A						,			padding='longest').to(A)
														UpperCamelCase					       =			model.generate(
														    input_ids=batch.input_ids						,			attention_mask=batch.attention_mask						,			**A						,			)
														UpperCamelCase					       =			tokenizer.batch_decode(A						,			skip_special_tokens=A						,			clean_up_tokenization_spaces=A)
														for hypothesis in dec:
																					fout.write(hypothesis + '\n')
																					fout.flush()
							fout.close()
							UpperCamelCase					       =			int(time.time() - start_time)  # seconds
							UpperCamelCase					       =			len(A)
							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_(			A		:						Tuple=True):
							UpperCamelCase					       =			argparse.ArgumentParser()
							parser.add_argument('model_name'						,			type=A						,			help='like facebook/bart-large-cnn,t5-base, etc.')
							parser.add_argument('input_path'						,			type=A						,			help='like cnn_dm/test.source')
							parser.add_argument('save_path'						,			type=A						,			help='where to save summaries')
							parser.add_argument('--reference_path'						,			type=A						,			required=A						,			help='like cnn_dm/test.target')
							parser.add_argument('--score_path'						,			type=A						,			required=A						,			default='metrics.json'						,			help='where to save metrics')
							parser.add_argument('--device'						,			type=A						,			required=A						,			default=A						,			help='cuda, cuda:1, cpu etc.')
							parser.add_argument(
							    '--prefix'						,			type=A						,			required=A						,			default=A						,			help='will be added to the begininng of src examples')
							parser.add_argument('--task'						,			type=A						,			default='summarization'						,			help='used for task_specific_params + metrics')
							parser.add_argument('--bs'						,			type=A						,			default=8						,			required=A						,			help='batch size')
							parser.add_argument(
							    '--n_obs'						,			type=A						,			default=-1						,			required=A						,			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=A						,			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(A)
							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=A)
							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(
							    A						,			args.save_path						,			args.model_name						,			batch_size=args.bs						,			device=args.device						,			fpaa=args.fpaa						,			task=args.task						,			prefix=args.prefix						,			**A						,			)

							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(A)]
							UpperCamelCase					       =			score_fn(A						,			A)
							scores.update(A)

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

							if verbose:
														print(A)

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






'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm







def 						A_(			A		:						int						,			A		:						bool = False):
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(A)
							np.random.seed(A)
							torch.manual_seed(A)
							if is_xpu_available():
														torch.xpu.manual_seed_all(A)
							elif is_npu_available():
														torch.npu.manual_seed_all(A)
							else:
														torch.cuda.manual_seed_all(A)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(A)







def 						A_(			A		:						Optional[RNGType] = None						,			A		:						Optional[torch.Generator] = None):
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														UpperCamelCase					       =			torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														UpperCamelCase					       =			torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														UpperCamelCase					       =			torch.tensor(xm.get_rng_state())
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														UpperCamelCase					       =			torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														UpperCamelCase					       =			torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														UpperCamelCase					       =			generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(A)
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(A)
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(A)
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(A)
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item())
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(A)







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




