





import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   lowerCamelCase_					:						Dict      = False


if is_vision_available():
   from PIL import Image
else:

   class 			_UpperCAmelCase  :



         '''simple docstring'''

         @staticmethod
         def  lowerCamelCase_      (					*snake_case_ ,				**snake_case_  ):

           """simple docstring"""

           pass

@is_pipeline_test
@require_vision
class 			_UpperCAmelCase  (       unittest.TestCase		):



      '''simple docstring'''
      lowercase_				:			Optional[Any]        =       MODEL_FOR_VISION_2_SEQ_MAPPING
      lowercase_				:			int        =       TF_MODEL_FOR_VISION_2_SEQ_MAPPING

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	List[str]    						=  pipeline('image-to-text' ,				model=lowercase_ ,				tokenizer=lowercase_ ,				image_processor=lowercase_  )
        A_  :	List[str]    						=  [
            Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  ),
            './tests/fixtures/tests_samples/COCO/000000039769.png',
        ]
        return pipe, examples

      def  lowerCamelCase_      (					self ,				snake_case_ ,				snake_case_  ):

        """simple docstring"""

        A_  :	str    						=  pipe(lowercase_  )
        self.assertEqual(
            lowercase_ ,				[
                [{'generated_text': ANY(lowercase_  )}],
                [{'generated_text': ANY(lowercase_  )}],
            ] ,				)

      @require_tf
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	str    						=  pipeline('image-to-text' ,				model='hf-internal-testing/tiny-random-vit-gpt2' ,				framework='tf'  )
        A_  :	Any    						=  './tests/fixtures/tests_samples/COCO/000000039769.png'

        A_  :	Union[str, Any]    						=  pipe(lowercase_  )
        self.assertEqual(
            lowercase_ ,				[
                {
                    'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                },
            ] ,				)

        A_  :	int    						=  pipe([image, image]  )
        self.assertEqual(
            lowercase_ ,				[
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
            ] ,				)

        A_  :	List[str]    						=  pipe(lowercase_ ,				max_new_tokens=1  )
        self.assertEqual(
            lowercase_ ,				[{'generated_text': 'growth'}] ,				)

      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Optional[int]    						=  pipeline('image-to-text' ,				model='hf-internal-testing/tiny-random-vit-gpt2'  )
        A_  :	Any    						=  './tests/fixtures/tests_samples/COCO/000000039769.png'

        A_  :	List[str]    						=  pipe(lowercase_  )
        self.assertEqual(
            lowercase_ ,				[
                {
                    'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                },
            ] ,				)

        A_  :	Any    						=  pipe([image, image]  )
        self.assertEqual(
            lowercase_ ,				[
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
                [
                    {
                        'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                    }
                ],
            ] ,				)

      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  pipeline('image-to-text' ,				model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'  )
        A_  :	str    						=  './tests/fixtures/tests_samples/COCO/000000039769.png'
        A_  :	Tuple    						=  'a photo of'

        A_  :	int    						=  pipe(lowercase_ ,				prompt=lowercase_  )
        self.assertTrue(outputs[0]['generated_text'].startswith(lowercase_  )  )

      @slow
      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Tuple    						=  pipeline('image-to-text' ,				model='ydshieh/vit-gpt2-coco-en'  )
        A_  :	int    						=  './tests/fixtures/tests_samples/COCO/000000039769.png'

        A_  :	Optional[Any]    						=  pipe(lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]  )

        A_  :	Optional[Any]    						=  pipe([image, image]  )
        self.assertEqual(
            lowercase_ ,				[
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
            ] ,				)

      @slow
      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Union[str, Any]    						=  pipeline('image-to-text' ,				model='Salesforce/blip-image-captioning-base'  )
        A_  :	List[str]    						=  'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
        A_  :	Any    						=  Image.open(requests.get(lowercase_ ,				stream=lowercase_  ).raw  )

        A_  :	int    						=  pipe(lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]  )

      @slow
      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  pipeline('image-to-text' ,				model='microsoft/git-base-coco'  )
        A_  :	str    						=  'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
        A_  :	Optional[Any]    						=  Image.open(requests.get(lowercase_ ,				stream=lowercase_  ).raw  )

        A_  :	Tuple    						=  pipe(lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'a cartoon of a purple character.'}]  )

      @slow
      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	List[Any]    						=  pipeline('image-to-text' ,				model='Salesforce/blip-image-captioning-base'  )
        A_  :	Optional[int]    						=  'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
        A_  :	Tuple    						=  Image.open(requests.get(lowercase_ ,				stream=lowercase_  ).raw  )

        A_  :	str    						=  'a photography of'

        A_  :	List[str]    						=  pipe(lowercase_ ,				prompt=lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'a photography of a volcano'}]  )

        with self.assertRaises(lowercase_  ):
          A_  :	Tuple    						=  pipe([image, image] ,				prompt=[prompt, prompt]  )

      @slow
      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Any    						=  pipeline('image-to-text' ,				model='microsoft/git-base-coco'  )
        A_  :	int    						=  'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
        A_  :	Any    						=  Image.open(requests.get(lowercase_ ,				stream=lowercase_  ).raw  )

        A_  :	Union[str, Any]    						=  'a photo of a'

        A_  :	Tuple    						=  pipe(lowercase_ ,				prompt=lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]  )

        with self.assertRaises(lowercase_  ):
          A_  :	Dict    						=  pipe([image, image] ,				prompt=[prompt, prompt]  )

      @unittest.skipIf(
          not is_torch_greater_or_equal_than_1_11 ,				reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'  )
      @slow
      @require_torch
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Dict    						=  pipeline('image-to-text' ,				model='google/pix2struct-ai2d-base'  )
        A_  :	Optional[Any]    						=  'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
        A_  :	Optional[Any]    						=  Image.open(requests.get(lowercase_ ,				stream=lowercase_  ).raw  )

        A_  :	Union[str, Any]    						=  'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

        A_  :	Union[str, Any]    						=  pipe(lowercase_ ,				prompt=lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'ash cloud'}]  )

        with self.assertRaises(lowercase_  ):
          A_  :	List[str]    						=  pipe([image, image] ,				prompt=[prompt, prompt]  )

      @slow
      @require_tf
      def  lowerCamelCase_      (					self  ):

        """simple docstring"""

        A_  :	Any    						=  pipeline('image-to-text' ,				model='ydshieh/vit-gpt2-coco-en' ,				framework='tf'  )
        A_  :	List[Any]    						=  './tests/fixtures/tests_samples/COCO/000000039769.png'

        A_  :	Optional[int]    						=  pipe(lowercase_  )
        self.assertEqual(lowercase_ ,				[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]  )

        A_  :	int    						=  pipe([image, image]  )
        self.assertEqual(
            lowercase_ ,				[
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
            ] ,				)






"""simple docstring"""




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_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

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

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





def 						UpperCAmelCase__		(					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  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_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  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_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  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_  :	Optional[Any]    						=  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_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

  if args.dump_args:
    scores.update(_UpperCAmelCase )
  if args.info:
    A_  :	Any    						=  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)