

import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							['image_processor', 'tokenizer']
    lowerCamelCase		     =							'OwlViTImageProcessor'
    lowerCamelCase		     =							('CLIPTokenizer', 'CLIPTokenizerFast')


    def __init__(				self			:	Tuple,lowercase_			:	Optional[Any]=None,lowercase_			:	List[str]=None,**lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        A__   				=       None
        if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.',lowercase_,)
            A__   				=       kwargs.pop('feature_extractor'		)

        A__   				=       image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'		)
        if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'		)

        super().__init__(lowercase_,lowercase_		)


    def __call__(				self			:	Any,lowercase_			:	Optional[int]=None,lowercase_			:	Dict=None,lowercase_			:	str=None,lowercase_			:	Union[str, Any]="max_length",lowercase_			:	Tuple="np",**lowercase_			:	List[str]		)->   List[Any]:





        '''simple docstring'''







        if text is None and query_images is None and images is None:
            raise ValueError(
                'You have to specify at least one text or query image or image. All three cannot be none.'		)

        if text is not None:
            if isinstance(lowercase_,lowercase_		) or (isinstance(lowercase_,lowercase_		) and not isinstance(text[0],lowercase_		)):
                A__   				=       [self.tokenizer(lowercase_,padding=lowercase_,return_tensors=lowercase_,**lowercase_		)]

            elif isinstance(lowercase_,lowercase_		) and isinstance(text[0],lowercase_		):
                A__   				=       []

                # Maximum number of queries across batch
                A__   				=       max([len(lowercase_		) for t in text]		)

                # Pad all batch samples to max number of text queries
                for t in text:
                    if len(lowercase_		) != max_num_queries:
                        A__   				=       t + [' '] * (max_num_queries - len(lowercase_		))

                    A__   				=       self.tokenizer(lowercase_,padding=lowercase_,return_tensors=lowercase_,**lowercase_		)
                    encodings.append(lowercase_		)
            else:
                raise TypeError('Input text should be a string, a list of strings or a nested list of strings'		)

            if return_tensors == "np":
                A__   				=       np.concatenate([encoding['input_ids'] for encoding in encodings],axis=0		)
                A__   				=       np.concatenate([encoding['attention_mask'] for encoding in encodings],axis=0		)

            elif return_tensors == "jax" and is_flax_available():
                import jax.numpy as jnp

                A__   				=       jnp.concatenate([encoding['input_ids'] for encoding in encodings],axis=0		)
                A__   				=       jnp.concatenate([encoding['attention_mask'] for encoding in encodings],axis=0		)

            elif return_tensors == "pt" and is_torch_available():
                import torch

                A__   				=       torch.cat([encoding['input_ids'] for encoding in encodings],dim=0		)
                A__   				=       torch.cat([encoding['attention_mask'] for encoding in encodings],dim=0		)

            elif return_tensors == "tf" and is_tf_available():
                import tensorflow as tf

                A__   				=       tf.stack([encoding['input_ids'] for encoding in encodings],axis=0		)
                A__   				=       tf.stack([encoding['attention_mask'] for encoding in encodings],axis=0		)

            else:
                raise ValueError('Target return tensor type could not be returned'		)

            A__   				=       BatchEncoding()
            A__   				=       input_ids
            A__   				=       attention_mask

        if query_images is not None:
            A__   				=       BatchEncoding()
            A__   				=       self.image_processor(
                lowercase_,return_tensors=lowercase_,**lowercase_		).pixel_values
            A__   				=       query_pixel_values

        if images is not None:
            A__   				=       self.image_processor(lowercase_,return_tensors=lowercase_,**lowercase_		)

        if text is not None and images is not None:
            A__   				=       image_features.pixel_values
            return encoding
        elif query_images is not None and images is not None:
            A__   				=       image_features.pixel_values
            return encoding
        elif text is not None or query_images is not None:
            return encoding
        else:
            return BatchEncoding(data=dict(**lowercase_		),tensor_type=lowercase_		)


    def 						snake_case__					(				self			:	List[str],*lowercase_			:	List[Any],**lowercase_			:	Dict		)->   Optional[int]:





        '''simple docstring'''







        return self.image_processor.post_process(*lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	Dict,*lowercase_			:	Optional[int],**lowercase_			:	Optional[int]		)->   Optional[int]:





        '''simple docstring'''







        return self.image_processor.post_process_object_detection(*lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	str,*lowercase_			:	Optional[int],**lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        return self.image_processor.post_process_image_guided_detection(*lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	Any,*lowercase_			:	Any,**lowercase_			:	int		)->   Optional[Any]:





        '''simple docstring'''







        return self.tokenizer.batch_decode(*lowercase_,**lowercase_		)


    def 						snake_case__					(				self			:	List[str],*lowercase_			:	Dict,**lowercase_			:	List[str]		)->   Union[str, Any]:





        '''simple docstring'''







        return self.tokenizer.decode(*lowercase_,**lowercase_		)


    @property
    def 						snake_case__					(				self			:	int		)->   Tuple:





        '''simple docstring'''







        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',lowercase_,)
        return self.image_processor_class


    @property
    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''simple docstring'''







        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',lowercase_,)
        return self.image_processor




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_			=  getLogger(__name__)


lowercase_			=  "cuda" if torch.cuda.is_available() else "cpu"
def 						_snake_case(   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__      :     Optional[Any]=False   ,      SCREAMING_SNAKE_CASE__      :     Dict="summarization"   ,      SCREAMING_SNAKE_CASE__      :     Any=None   ,      **SCREAMING_SNAKE_CASE__      :     str   ,      )							->				Dict:





    '''simple docstring'''






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

    A__   				=       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__				)
    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(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    if prefix is None:
        A__   				=       prefix or getattr(model.config   ,      'prefix'   ,      ''				) or ''
    for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)				)				):
        A__   				=       [prefix + text for text in examples_chunk]
        A__   				=       tokenizer(SCREAMING_SNAKE_CASE__   ,      return_tensors='pt'   ,      truncation=SCREAMING_SNAKE_CASE__   ,      padding='longest'				).to(SCREAMING_SNAKE_CASE__				)
        A__   				=       model.generate(
            input_ids=batch.input_ids   ,      attention_mask=batch.attention_mask   ,      **SCREAMING_SNAKE_CASE__   ,      )
        A__   				=       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()
    A__   				=       int(time.time() - start_time				)  # seconds
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   ,      4				)}
def 						_snake_case(   )							->				Tuple:





    '''simple docstring'''






    return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				Dict:





    '''simple docstring'''






    A__   				=       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
    A__				,						A__   				=       parser.parse_known_args()
    A__   				=       parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__				)
    if parsed_args and verbose:
        print(f'parsed the following generate kwargs: {parsed_args}'				)
    A__   				=       [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
    if args.n_obs > 0:
        A__   				=       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.'				)

    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'				)

    A__   				=       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
    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(SCREAMING_SNAKE_CASE__				)]
    A__   				=       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:
        A__   				=       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)

