


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, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import EfficientNetImageProcessor

class   lowerCamelCase						(	unittest.TestCase  ):

       def __init__(      self						,    a__						,    a__=13						,    a__=3						,    a__=18						,    a__=30						,    a__=4_00						,    a__=True						,    a__=None						,    a__=True						,    a__=[0.5, 0.5, 0.5]						,    a__=[0.5, 0.5, 0.5]						,    ):






        '''simple docstring'''






        snake_case :			str							=   size if size is not None else {'''height''': 18, '''width''': 18}
        snake_case :			Optional[int]							=   parent
        snake_case :			Optional[int]							=   batch_size
        snake_case :			Optional[Any]							=   num_channels
        snake_case :			List[str]							=   image_size
        snake_case :			Union[str, Any]							=   min_resolution
        snake_case :			Dict							=   max_resolution
        snake_case :			Optional[Any]							=   do_resize
        snake_case :			str							=   size
        snake_case :			Union[str, Any]							=   do_normalize
        snake_case :			int							=   image_mean
        snake_case :			Optional[int]							=   image_std



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }







@require_torch
@require_vision
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   EfficientNetImageProcessor if is_vision_available() else None

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   EfficientNetImageProcessorTester(self       )

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   self.image_processing_class(**self.image_processor_dict       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_						,    '''image_mean'''       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_						,    '''image_std'''       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_						,    '''do_normalize'''       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_						,    '''do_resize'''       )       )
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_						,    '''size'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   self.image_processing_class.from_dict(self.image_processor_dict       )
        self.assertEqual(image_processor.size						,    {'''height''': 18, '''width''': 18}       )

        snake_case :			int							=   self.image_processing_class.from_dict(self.image_processor_dict						,    size=42       )
        self.assertEqual(image_processor.size						,    {'''height''': 42, '''width''': 42}       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        snake_case :			Tuple							=   prepare_image_inputs(self.image_processor_tester						,    equal_resolution=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_						,    Image.Image       )

        # Test not batched input
        snake_case :			int							=   image_processing(image_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_images.shape						,    (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Tuple							=   image_processing(SCREAMING_SNAKE_CASE_						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_images.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			int							=   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        snake_case :			Tuple							=   prepare_image_inputs(self.image_processor_tester						,    equal_resolution=SCREAMING_SNAKE_CASE_						,    numpify=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_						,    np.ndarray       )

        # Test not batched input
        snake_case :			Optional[int]							=   image_processing(image_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_images.shape						,    (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )						,    )

        # Test batched
        snake_case :			Optional[Any]							=   image_processing(SCREAMING_SNAKE_CASE_						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_images.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )						,    )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        snake_case :			Optional[Any]							=   prepare_image_inputs(self.image_processor_tester						,    equal_resolution=SCREAMING_SNAKE_CASE_						,    torchify=SCREAMING_SNAKE_CASE_       )
        for image in image_inputs:
         self.assertIsInstance(SCREAMING_SNAKE_CASE_						,    torch.Tensor       )

        # Test not batched input
        snake_case :			Union[str, Any]							=   image_processing(image_inputs[0]						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_images.shape						,    (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )						,    )

        # Test batched
        snake_case :			int							=   image_processing(SCREAMING_SNAKE_CASE_						,    return_tensors='''pt'''       ).pixel_values
        self.assertEqual(
            encoded_images.shape						,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )						,    )








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_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

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

       __snake_case						       =   time.time()
       # update config with task specific params
       use_task_specific_params(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   tokenizer.batch_decode(__snake_case    ,skip_special_tokens=__snake_case    ,clean_up_tokenization_spaces=__snake_case					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   argparse.ArgumentParser()
       parser.add_argument('model_name'    ,type=__snake_case    ,help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path'    ,type=__snake_case    ,help='like cnn_dm/test.source'					)
       parser.add_argument('save_path'    ,type=__snake_case    ,help='where to save summaries'					)
       parser.add_argument('--reference_path'    ,type=__snake_case    ,required=__snake_case    ,help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path'    ,type=__snake_case    ,required=__snake_case    ,default='metrics.json'    ,help='where to save metrics'					)
       parser.add_argument('--device'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='will be added to the begininng of src examples'					)
       parser.add_argument('--task'    ,type=__snake_case    ,default='summarization'    ,help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs'    ,type=__snake_case    ,default=8    ,required=__snake_case    ,help='batch size'					)
       parser.add_argument(
           '--n_obs'    ,type=__snake_case    ,default=-1    ,required=__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=__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
       __snake_case       ,       __snake_case						       =   parser.parse_known_args()
       __snake_case						       =   parse_numeric_n_bool_cl_kwargs(__snake_case					)
       if parsed_args and verbose:
              print(f'''parsed the following generate kwargs: {parsed_args}'''					)
       with open(args.input_path					) as f:
              __snake_case						       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __snake_case						       =   examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__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.'''					)
       __snake_case						       =   generate_summaries_or_translations(
           __snake_case    ,args.save_path    ,args.model_name    ,batch_size=args.bs    ,device=args.device    ,fpaa=args.fpaa    ,task=args.task    ,prefix=args.prefix    ,**__snake_case    ,)

       if args.reference_path is None:
              return {}

       # Compute scores
       __snake_case						       =   calculate_bleu if 'translation' in args.task else calculate_rouge
       __snake_case						       =   [x.rstrip() for x in open(args.save_path					).readlines()]
       __snake_case						       =   [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__snake_case					)]
       __snake_case						       =   score_fn(__snake_case    ,__snake_case					)
       scores.update(__snake_case					)

       if args.dump_args:
              scores.update(__snake_case					)
       if args.info:
              __snake_case						       =   args.info

       if verbose:
              print(__snake_case					)

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