

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


_a			=       logging.get_logger(__name__)

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






class 							A_			(		snake_case__ ):
					_lowercase      :					str       =     'mobilevitv2'


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

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

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







class 							A_			(		snake_case__ ):
					_lowercase      :					Tuple       =     version.parse('1.11' )


					@property
					def   UpperCAmelCase  (							self      :		Any )							->     Mapping[str, Mapping[int, str]]:
						return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})] )


					@property
					def   UpperCAmelCase  (							self      :		int )							->     Mapping[str, Mapping[int, str]]:
						if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})] )
						else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})] )


					@property
					def   UpperCAmelCase  (							self      :		Optional[Any] )							->     float:
						return 1E-4








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


_a			=       getLogger(__name__)


_a			=       '''cuda''' if torch.cuda.is_available() else '''cpu'''







def _a			(	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		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

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

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







def _a			(	)   ->   Dict:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				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
 __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				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.'''		)
 __lowerCAmelCase:       Tuple            =				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
 __lowerCAmelCase:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       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:
  __lowerCAmelCase:       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)





