



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:					Any      =						'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[int]="summarization"					,	A			:       int=None					,	**A			:       Union[str, Any]					,	)						->			str:
					"""simple docstring"""





					a   	=  Path(A_			).open("w"					,	encoding="utf-8"			)
					a   	=  str(A_			)
					a   	=  AutoModelForSeqaSeqLM.from_pretrained(A_			).to(A_			)
					if fpaa:
										a   	=  model.half()

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

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






def        a( )						->			int:
					"""simple docstring"""





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






def        a( A			:       Optional[Any]=True			)						->			Tuple:
					"""simple docstring"""





					a   	=  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
					a   	=  parser.parse_known_args()
					a   	=  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:
										a   	=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										a   	=  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.'''			)
					a   	=  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
					a   	=  calculate_bleu if 'translation' in args.task else calculate_rouge
					a   	=  [x.rstrip() for x in open(args.save_path			).readlines()]
					a   	=  [x.rstrip() for x in open(args.reference_path			).readlines()][: len(A_			)]
					a   	=  score_fn(A_					,	A_			)
					scores.update(A_			)

					if args.dump_args:
										scores.update(A_			)
					if args.info:
										a   	=  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"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase							:       Optional[Any]       					=		logging.get_logger(__name__)

_UpperCamelCase							:       int       					=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''mobilevitv2'''





    def __init__(				self      :   Any							,						A      :   Any=3							,						A      :   Any=2_5_6							,						A      :   Optional[int]=2							,						A      :   str=2.0							,						A      :   Union[str, Any]="swish"							,						A      :   str=3							,						A      :   Tuple=3_2							,						A      :   Optional[int]=0.1							,						A      :   str=0.02							,						A      :   int=1E-5							,						A      :   List[Any]=5_1_2							,						A      :   str=[6, 1_2, 1_8]							,						A      :   Any=0.1							,						A      :   Union[str, Any]=2_5_5							,						A      :   Optional[Any]=[2, 4, 3]							,						A      :   List[Any]=[1_2_8, 1_9_2, 2_5_6]							,						A      :   Tuple=1.0							,						A      :   Union[str, Any]=2							,						A      :   List[Any]=0.0							,						A      :   int=0.0							,						**A      :   Optional[Any]							,						):


      '''simple docstring'''




      super().__init__(**A     )

      a	:     Optional[Any]							=  num_channels
      a	:     List[Any]							=  image_size
      a	:     Tuple							=  patch_size
      a	:     Optional[Any]							=  expand_ratio
      a	:     Any							=  hidden_act
      a	:     Dict							=  conv_kernel_size
      a	:     Optional[int]							=  output_stride
      a	:     Optional[Any]							=  initializer_range
      a	:     Tuple							=  layer_norm_eps
      a	:     Optional[int]							=  n_attn_blocks
      a	:     Tuple							=  base_attn_unit_dims
      a	:     List[str]							=  width_multiplier
      a	:     Dict							=  ffn_multiplier
      a	:     Dict							=  ffn_dropout
      a	:     Optional[int]							=  attn_dropout
      a	:     Tuple							=  classifier_dropout_prob

      # decode head attributes for semantic segmentation
      a	:     str							=  aspp_out_channels
      a	:     List[Any]							=  atrous_rates
      a	:     Optional[int]							=  aspp_dropout_prob
      a	:     Optional[Any]							=  semantic_loss_ignore_index






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	version.parse('''1.11'''			)





    @property
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      if self.task == "image-classification":
        return OrderedDict([('logits', {0: 'batch'})]     )
      else:
        return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      return 1E-4




