




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


SCREAMING_SNAKE_CASE_						:					int			   =       getLogger(__name__)


SCREAMING_SNAKE_CASE_						:					Any			   =       '''cuda''' if torch.cuda.is_available() else '''cpu'''
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = 8   ,    UpperCAmelCase_ = DEFAULT_DEVICE   ,    UpperCAmelCase_=False   ,    UpperCAmelCase_="summarization"   ,    UpperCAmelCase_=None   ,    **UpperCAmelCase_   ,    ):
    a__      =       Path(UpperCAmelCase_		).open("""w"""   ,    encoding="""utf-8"""		)
    a__      =       str(UpperCAmelCase_		)
    a__      =       AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_		).to(UpperCAmelCase_		)
    if fpaa:
        a__      =       model.half()

    a__      =       AutoTokenizer.from_pretrained(UpperCAmelCase_		)
    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(UpperCAmelCase_   ,    UpperCAmelCase_		)
    if prefix is None:
        a__      =       prefix or getattr(model.config   ,    """prefix"""   ,    """"""		) or """"""
    for examples_chunk in tqdm(list(chunks(UpperCAmelCase_   ,    UpperCAmelCase_		)		)		):
        a__      =       [prefix + text for text in examples_chunk]
        a__      =       tokenizer(UpperCAmelCase_   ,    return_tensors="""pt"""   ,    truncation=UpperCAmelCase_   ,    padding="""longest"""		).to(UpperCAmelCase_		)
        a__      =       model.generate(
            input_ids=batch.input_ids   ,    attention_mask=batch.attention_mask   ,    **UpperCAmelCase_   ,    )
        a__      =       tokenizer.batch_decode(UpperCAmelCase_   ,    skip_special_tokens=UpperCAmelCase_   ,    clean_up_tokenization_spaces=UpperCAmelCase_		)
        for hypothesis in dec:
            fout.write(hypothesis + """\n"""		)
            fout.flush()
    fout.close()
    a__      =       int(time.time() - start_time		)  # seconds
    a__      =       len(UpperCAmelCase_		)
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   ,    4		)}
def        __lowerCAmelCase ():
    return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""		)
def        __lowerCAmelCase (UpperCAmelCase_=True		):
    a__      =       argparse.ArgumentParser()
    parser.add_argument("""model_name"""   ,    type=UpperCAmelCase_   ,    help="""like facebook/bart-large-cnn,t5-base, etc."""		)
    parser.add_argument("""input_path"""   ,    type=UpperCAmelCase_   ,    help="""like cnn_dm/test.source"""		)
    parser.add_argument("""save_path"""   ,    type=UpperCAmelCase_   ,    help="""where to save summaries"""		)
    parser.add_argument("""--reference_path"""   ,    type=UpperCAmelCase_   ,    required=UpperCAmelCase_   ,    help="""like cnn_dm/test.target"""		)
    parser.add_argument("""--score_path"""   ,    type=UpperCAmelCase_   ,    required=UpperCAmelCase_   ,    default="""metrics.json"""   ,    help="""where to save metrics"""		)
    parser.add_argument("""--device"""   ,    type=UpperCAmelCase_   ,    required=UpperCAmelCase_   ,    default=UpperCAmelCase_   ,    help="""cuda, cuda:1, cpu etc."""		)
    parser.add_argument(
        """--prefix"""   ,    type=UpperCAmelCase_   ,    required=UpperCAmelCase_   ,    default=UpperCAmelCase_   ,    help="""will be added to the begininng of src examples"""		)
    parser.add_argument("""--task"""   ,    type=UpperCAmelCase_   ,    default="""summarization"""   ,    help="""used for task_specific_params + metrics"""		)
    parser.add_argument("""--bs"""   ,    type=UpperCAmelCase_   ,    default=8   ,    required=UpperCAmelCase_   ,    help="""batch size"""		)
    parser.add_argument(
        """--n_obs"""   ,    type=UpperCAmelCase_   ,    default=-1   ,    required=UpperCAmelCase_   ,    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=UpperCAmelCase_   ,    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(UpperCAmelCase_		)
    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=UpperCAmelCase_		)
    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(
        UpperCAmelCase_   ,    args.save_path   ,    args.model_name   ,    batch_size=args.bs   ,    device=args.device   ,    fpaa=args.fpaa   ,    task=args.task   ,    prefix=args.prefix   ,    **UpperCAmelCase_   ,    )

    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(UpperCAmelCase_		)]
    a__      =       score_fn(UpperCAmelCase_   ,    UpperCAmelCase_		)
    scores.update(UpperCAmelCase_		)

    if args.dump_args:
        scores.update(UpperCAmelCase_		)
    if args.info:
        a__      =       args.info

    if verbose:
        print(UpperCAmelCase_		)

    if args.score_path is not None:
        json.dump(UpperCAmelCase_   ,    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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import SwinaSRImageProcessor
 from transformers.image_transforms import get_image_size







class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     def __init__(      self :       Optional[int]						,				A :       Dict						,				A :       Union[str, Any]=7						,				A :       List[str]=3						,				A :       Optional[int]=18						,				A :       Optional[int]=30						,				A :       Optional[Any]=4_00						,				A :       Optional[Any]=True						,				A :       Tuple=1 / 2_55						,				A :       Tuple=True						,				A :       Optional[int]=8						,				)			->			Any:






         """simple docstring"""
         a__      =       parent
         a__      =       batch_size
         a__      =       num_channels
         a__      =       image_size
         a__      =       min_resolution
         a__      =       max_resolution
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       do_pad
         a__      =       pad_size

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			Union[str, Any]:






         """simple docstring"""
         return {
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
             "pad_size": self.pad_size,
         }






     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       int=False						,				A :       Any=False						,				A :       List[Any]=False    )			->			Optional[Any]:






         """simple docstring"""
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
             a__      =       []
             for i in range(self.batch_size    ):
                 image_inputs.append(
                     np.random.randint(
                         2_55						,				size=(self.num_channels, self.max_resolution, self.max_resolution)						,				dtype=np.uinta    )    )
         else:
             a__      =       []
             for i in range(self.batch_size    ):
                 a__ ,		a__      =       np.random.choice(np.arange(self.min_resolution						,				self.max_resolution    )						,				2    )
                 image_inputs.append(np.random.randint(2_55						,				size=(self.num_channels, width, height)						,				dtype=np.uinta    )    )

         if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             a__      =       [Image.fromarray(np.moveaxis(A						,				0						,				-1    )    ) for x in image_inputs]

         if torchify:
             a__      =       [torch.from_numpy(A    ) for x in image_inputs]

         return image_inputs







@require_torch
@require_vision
class 		__UpperCAmelCase(		lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Dict						=    SwinaSRImageProcessor if is_vision_available() else None

     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Dict:






         """simple docstring"""
         a__      =       SwinaSRImageProcessingTester(self    )

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






         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			int:






         """simple docstring"""
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(A						,				"""do_rescale"""    )    )
         self.assertTrue(hasattr(A						,				"""rescale_factor"""    )    )
         self.assertTrue(hasattr(A						,				"""do_pad"""    )    )
         self.assertTrue(hasattr(A						,				"""pad_size"""    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         pass

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       Optional[Any]    )			->			List[str]:






         """simple docstring"""
         a__ ,		a__      =       get_image_size(A    )
         a__      =       self.image_processor_tester.pad_size

         a__      =       (old_height // size + 1) * size - old_height
         a__      =       (old_width // size + 1) * size - old_width
         return old_height + pad_height, old_width + pad_width

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			Union[str, Any]:






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				Image.Image    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(np.array(image_inputs[0]    )    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Optional[Any]:






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A						,				numpify=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				np.ndarray    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(image_inputs[0]    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Tuple:






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A						,				torchify=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				torch.Tensor    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(image_inputs[0]    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)






