


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  tempfile.mkdtemp()

        __a					=  BlipImageProcessor()
        __a					=  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""    )

        __a					=  BlipProcessor(__A  ,       __A    )

        processor.save_pretrained(self.tmpdirname    )


     def 	snake_case_  (	self  ,       **__A    ):
        return AutoProcessor.from_pretrained(self.tmpdirname  ,       **__A    ).tokenizer


     def 	snake_case_  (	self  ,       **__A    ):
        return AutoProcessor.from_pretrained(self.tmpdirname  ,       **__A    ).image_processor


     def 	snake_case_  (	self    ):
        shutil.rmtree(self.tmpdirname    )


     def 	snake_case_  (	self    ):
        __a					=  [np.random.randint(255  ,       size=(3, 30, 400)  ,       dtype=np.uinta    )]

        __a					=  [Image.fromarray(np.moveaxis(__A  ,       0  ,       -1    )    ) for x in image_inputs]

        return image_inputs


     def 	snake_case_  (	self    ):
        __a					=  BlipProcessor(tokenizer=self.get_tokenizer()  ,       image_processor=self.get_image_processor()    )
        processor.save_pretrained(self.tmpdirname    )

        __a					=  self.get_tokenizer(bos_token="""(BOS)"""  ,       eos_token="""(EOS)"""    )
        __a					=  self.get_image_processor(do_normalize=__A  ,       padding_value=1.0    )

        __a					=  BlipProcessor.from_pretrained(
            self.tmpdirname  ,       bos_token="""(BOS)"""  ,       eos_token="""(EOS)"""  ,       do_normalize=__A  ,       padding_value=1.0    )

        self.assertEqual(processor.tokenizer.get_vocab()  ,       tokenizer_add_kwargs.get_vocab()    )
        self.assertIsInstance(processor.tokenizer  ,       __A    )

        self.assertEqual(processor.image_processor.to_json_string()  ,       image_processor_add_kwargs.to_json_string()    )
        self.assertIsInstance(processor.image_processor  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_image_processor()
        __a					=  self.get_tokenizer()

        __a					=  BlipProcessor(tokenizer=__A  ,       image_processor=__A    )

        __a					=  self.prepare_image_inputs()

        __a					=  image_processor(__A  ,       return_tensors="""np"""    )
        __a					=  processor(images=__A  ,       return_tensors="""np"""    )

        for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()  ,       input_processor[key].sum()  ,       delta=1E-2    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_image_processor()
        __a					=  self.get_tokenizer()

        __a					=  BlipProcessor(tokenizer=__A  ,       image_processor=__A    )

        __a					=  """lower newer"""

        __a					=  processor(text=__A    )

        __a					=  tokenizer(__A  ,       return_token_type_ids=__A    )

        for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]  ,       encoded_processor[key]    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_image_processor()
        __a					=  self.get_tokenizer()

        __a					=  BlipProcessor(tokenizer=__A  ,       image_processor=__A    )

        __a					=  """lower newer"""
        __a					=  self.prepare_image_inputs()

        __a					=  processor(text=__A  ,       images=__A    )

        self.assertListEqual(list(inputs.keys()    )  ,       ["""pixel_values""", """input_ids""", """attention_mask"""]    )

        # test if it raises when no input is passed
        with pytest.raises(__A    ):
           processor()


     def 	snake_case_  (	self    ):
        __a					=  self.get_image_processor()
        __a					=  self.get_tokenizer()

        __a					=  BlipProcessor(tokenizer=__A  ,       image_processor=__A    )

        __a					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __a					=  processor.batch_decode(__A    )
        __a					=  tokenizer.batch_decode(__A    )

        self.assertListEqual(__A  ,       __A    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_image_processor()
        __a					=  self.get_tokenizer()

        __a					=  BlipProcessor(tokenizer=__A  ,       image_processor=__A    )

        __a					=  """lower newer"""
        __a					=  self.prepare_image_inputs()

        __a					=  processor(text=__A  ,       images=__A    )

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertListEqual(list(inputs.keys()    )  ,       ["""pixel_values""", """input_ids""", """attention_mask"""]    )







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							    =							getLogger(__name__)


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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 8		,				lowerCAmelCase__ = DEFAULT_DEVICE		,				lowerCAmelCase__=False		,				lowerCAmelCase__="summarization"		,				lowerCAmelCase__=None		,				**lowerCAmelCase__		,				):
   __a					=  Path(lowerCAmelCase__   ).open("""w"""		,				encoding="""utf-8"""   )
   __a					=  str(lowerCAmelCase__   )
   __a					=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__   ).to(lowerCAmelCase__   )
   if fpaa:
      __a					=  model.half()

   __a					=  AutoTokenizer.from_pretrained(lowerCAmelCase__   )
   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(lowerCAmelCase__		,				lowerCAmelCase__   )
   if prefix is None:
      __a					=  prefix or getattr(model.config		,				"""prefix"""		,				""""""   ) or """"""
   for examples_chunk in tqdm(list(chunks(lowerCAmelCase__		,				lowerCAmelCase__   )   )   ):
      __a					=  [prefix + text for text in examples_chunk]
      __a					=  tokenizer(lowerCAmelCase__		,				return_tensors="""pt"""		,				truncation=lowerCAmelCase__		,				padding="""longest"""   ).to(lowerCAmelCase__   )
      __a					=  model.generate(
          input_ids=batch.input_ids		,				attention_mask=batch.attention_mask		,				**lowerCAmelCase__		,				)
      __a					=  tokenizer.batch_decode(lowerCAmelCase__		,				skip_special_tokens=lowerCAmelCase__		,				clean_up_tokenization_spaces=lowerCAmelCase__   )
      for hypothesis in dec:
         fout.write(hypothesis + """\n"""   )
         fout.flush()
   fout.close()
   __a					=  int(time.time() - start_time   )  # seconds
   __a					=  len(lowerCAmelCase__   )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,				4   )}




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




def  a   (lowerCAmelCase__=True   ):
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""model_name"""		,				type=lowerCAmelCase__		,				help="""like facebook/bart-large-cnn,t5-base, etc."""   )
   parser.add_argument("""input_path"""		,				type=lowerCAmelCase__		,				help="""like cnn_dm/test.source"""   )
   parser.add_argument("""save_path"""		,				type=lowerCAmelCase__		,				help="""where to save summaries"""   )
   parser.add_argument("""--reference_path"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				help="""like cnn_dm/test.target"""   )
   parser.add_argument("""--score_path"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default="""metrics.json"""		,				help="""where to save metrics"""   )
   parser.add_argument("""--device"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default=lowerCAmelCase__		,				help="""cuda, cuda:1, cpu etc."""   )
   parser.add_argument(
       """--prefix"""		,				type=lowerCAmelCase__		,				required=lowerCAmelCase__		,				default=lowerCAmelCase__		,				help="""will be added to the begininng of src examples"""   )
   parser.add_argument("""--task"""		,				type=lowerCAmelCase__		,				default="""summarization"""		,				help="""used for task_specific_params + metrics"""   )
   parser.add_argument("""--bs"""		,				type=lowerCAmelCase__		,				default=8		,				required=lowerCAmelCase__		,				help="""batch size"""   )
   parser.add_argument(
       """--n_obs"""		,				type=lowerCAmelCase__		,				default=-1		,				required=lowerCAmelCase__		,				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=lowerCAmelCase__		,				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(lowerCAmelCase__   )
   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=lowerCAmelCase__   )

   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(
       lowerCAmelCase__		,				args.save_path		,				args.model_name		,				batch_size=args.bs		,				device=args.device		,				fpaa=args.fpaa		,				task=args.task		,				prefix=args.prefix		,				**lowerCAmelCase__		,				)

   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(lowerCAmelCase__   )]
   __a					=  score_fn(lowerCAmelCase__		,				lowerCAmelCase__   )
   scores.update(lowerCAmelCase__   )

   if args.dump_args:
      scores.update(lowerCAmelCase__   )
   if args.info:
      __a					=  args.info

   if verbose:
      print(lowerCAmelCase__   )

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



