

'''simple docstring'''
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


lowerCamelCase				:   int      				=  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def  _lowerCAmelCase      (		_UpperCamelCase		:  str       )   ->    Tuple:
     """simple docstring"""




     if "token" in model_name_or_path:
          return "rag_token"
     if "sequence" in model_name_or_path:
          return "rag_sequence"
     if "bart" in model_name_or_path:
          return "bart"
     return None


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Optional[Any]:
     """simple docstring"""




     return max(metric_fn(_UpperCamelCase							,				_UpperCamelCase       ) for gt in ground_truths       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Tuple							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=[line.strip() for line in open(_UpperCamelCase							,				'r'       ).readlines()]
     _SCREAMING_SNAKE_CASE												=[]

     if args.gold_data_mode == "qa":
          _SCREAMING_SNAKE_CASE												=pd.read_csv(_UpperCamelCase							,				sep='\t'							,				header=_UpperCamelCase       )
          for answer_list in data[1]:
               _SCREAMING_SNAKE_CASE												=ast.literal_eval(_UpperCamelCase       )
               answers.append(_UpperCamelCase       )
     else:
          _SCREAMING_SNAKE_CASE												=[line.strip() for line in open(_UpperCamelCase							,				'r'       ).readlines()]
          _SCREAMING_SNAKE_CASE												=[[reference] for reference in references]

     _SCREAMING_SNAKE_CASE												=_SCREAMING_SNAKE_CASE												=_SCREAMING_SNAKE_CASE												=0
     for prediction, ground_truths in zip(_UpperCamelCase							,				_UpperCamelCase       ):
          total += 1
          em += metric_max_over_ground_truths(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )
          fa += metric_max_over_ground_truths(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=1_00.0 * em / total
     _SCREAMING_SNAKE_CASE												=1_00.0 * fa / total

     logger.info(f"F1: {fa:.2f}"       )
     logger.info(f"EM: {em:.2f}"       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Union[str, Any]							,				_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  List[str]       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=args.k
     _SCREAMING_SNAKE_CASE												=[line.strip() for line in open(_UpperCamelCase							,				'r'       ).readlines()]
     _SCREAMING_SNAKE_CASE												=[line.strip() for line in open(_UpperCamelCase							,				'r'       ).readlines()]

     _SCREAMING_SNAKE_CASE												=_SCREAMING_SNAKE_CASE												=0
     for hypo, reference in zip(_UpperCamelCase							,				_UpperCamelCase       ):
          _SCREAMING_SNAKE_CASE												=set(hypo.split('\t'       )[:k]       )
          _SCREAMING_SNAKE_CASE												=set(reference.split('\t'       )       )
          total += 1
          em += len(hypo_provenance & ref_provenance       ) / k

     _SCREAMING_SNAKE_CASE												=1_00.0 * em / total
     logger.info(f"Precision@{k}: {em: .2f}"       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  Tuple       )   ->    Union[str, Any]:
     """simple docstring"""




     def strip_title(_UpperCamelCase		:  Union[str, Any]       ):
          if title.startswith('"'       ):
               _SCREAMING_SNAKE_CASE												=title[1:]
          if title.endswith('"'       ):
               _SCREAMING_SNAKE_CASE												=title[:-1]
          return title

     _SCREAMING_SNAKE_CASE												=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         _UpperCamelCase							,				return_tensors='pt'							,				padding=_UpperCamelCase							,				truncation=_UpperCamelCase							,				)['input_ids'].to(args.device       )

     _SCREAMING_SNAKE_CASE												=rag_model.rag.question_encoder(_UpperCamelCase       )
     _SCREAMING_SNAKE_CASE												=question_enc_outputs[0]

     _SCREAMING_SNAKE_CASE												=rag_model.retriever(
         _UpperCamelCase							,				question_enc_pool_output.cpu().detach().to(torch.floataa       ).numpy()							,				prefix=rag_model.rag.generator.config.prefix							,				n_docs=rag_model.config.n_docs							,				return_tensors='pt'							,				)
     _SCREAMING_SNAKE_CASE												=rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
     _SCREAMING_SNAKE_CASE												=[]
     for docs in all_docs:
          _SCREAMING_SNAKE_CASE												=[strip_title(_UpperCamelCase       ) for title in docs['title']]
          provenance_strings.append('\t'.join(_UpperCamelCase       )       )
     return provenance_strings


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[Any]							,				_UpperCamelCase		:  Optional[int]							,				_UpperCamelCase		:  str       )   ->    List[str]:
     """simple docstring"""




     with torch.no_grad():
          _SCREAMING_SNAKE_CASE												=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
              _UpperCamelCase							,				return_tensors='pt'							,				padding=_UpperCamelCase							,				truncation=_UpperCamelCase       )

          _SCREAMING_SNAKE_CASE												=inputs_dict.input_ids.to(args.device       )
          _SCREAMING_SNAKE_CASE												=inputs_dict.attention_mask.to(args.device       )
          _SCREAMING_SNAKE_CASE												=rag_model.generate(  # rag_model overwrites generate
              _UpperCamelCase							,				attention_mask=_UpperCamelCase							,				num_beams=args.num_beams							,				min_length=args.min_length							,				max_length=args.max_length							,				early_stopping=_UpperCamelCase							,				num_return_sequences=1							,				bad_words_ids=[[0, 0]]							,				)
          _SCREAMING_SNAKE_CASE												=rag_model.retriever.generator_tokenizer.batch_decode(_UpperCamelCase							,				skip_special_tokens=_UpperCamelCase       )

          if args.print_predictions:
               for q, a in zip(_UpperCamelCase							,				_UpperCamelCase       ):
                    logger.info('Q: {} - A: {}'.format(_UpperCamelCase							,				_UpperCamelCase       )       )

          return answers


def  _lowerCAmelCase      (		)   ->    str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=argparse.ArgumentParser()
     parser.add_argument(
         '--model_type'							,				choices=['rag_sequence', 'rag_token', 'bart']							,				type=_UpperCamelCase							,				help=(
             'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
             ' model_name_or_path'
         )							,				)
     parser.add_argument(
         '--index_name'							,				default=_UpperCamelCase							,				choices=['exact', 'compressed', 'legacy']							,				type=_UpperCamelCase							,				help='RAG model retriever type'							,				)
     parser.add_argument(
         '--index_path'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				help='Path to the retrieval index'							,				)
     parser.add_argument('--n_docs'							,				default=5							,				type=_UpperCamelCase							,				help='Number of retrieved docs'       )
     parser.add_argument(
         '--model_name_or_path'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='Path to pretrained checkpoints or model identifier from huggingface.co/models'							,				)
     parser.add_argument(
         '--eval_mode'							,				choices=['e2e', 'retrieval']							,				default='e2e'							,				type=_UpperCamelCase							,				help=(
             'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
             ' precision@k.'
         )							,				)
     parser.add_argument('--k'							,				default=1							,				type=_UpperCamelCase							,				help='k for the precision@k calculation'       )
     parser.add_argument(
         '--evaluation_set'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='Path to a file containing evaluation samples'							,				)
     parser.add_argument(
         '--gold_data_path'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='Path to a tab-separated file with gold samples'							,				)
     parser.add_argument(
         '--gold_data_mode'							,				default='qa'							,				type=_UpperCamelCase							,				choices=['qa', 'ans']							,				help=(
             'Format of the gold data file'
             'qa - a single line in the following format: question [tab] answer_list'
             'ans - a single line of the gold file contains the expected answer string'
         )							,				)
     parser.add_argument(
         '--predictions_path'							,				type=_UpperCamelCase							,				default='predictions.txt'							,				help='Name of the predictions file, to be stored in the checkpoints directory'							,				)
     parser.add_argument(
         '--eval_all_checkpoints'							,				action='store_true'							,				help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'							,				)
     parser.add_argument(
         '--eval_batch_size'							,				default=8							,				type=_UpperCamelCase							,				help='Batch size per GPU/CPU for evaluation.'							,				)
     parser.add_argument(
         '--recalculate'							,				help='Recalculate predictions even if the prediction file exists'							,				action='store_true'							,				)
     parser.add_argument(
         '--num_beams'							,				default=4							,				type=_UpperCamelCase							,				help='Number of beams to be used when generating answers'							,				)
     parser.add_argument('--min_length'							,				default=1							,				type=_UpperCamelCase							,				help='Min length of the generated answers'       )
     parser.add_argument('--max_length'							,				default=50							,				type=_UpperCamelCase							,				help='Max length of the generated answers'       )

     parser.add_argument(
         '--print_predictions'							,				action='store_true'							,				help='If True, prints predictions while evaluating.'							,				)
     parser.add_argument(
         '--print_docs'							,				action='store_true'							,				help='If True, prints docs retried while generating.'							,				)
     _SCREAMING_SNAKE_CASE												=parser.parse_args()
     _SCREAMING_SNAKE_CASE												=torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )
     return args


def  _lowerCAmelCase      (		_UpperCamelCase		:  Union[str, Any]       )   ->    Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												={}
     if args.model_type is None:
          _SCREAMING_SNAKE_CASE												=infer_model_type(args.model_name_or_path       )
          assert args.model_type is not None
     if args.model_type.startswith('rag'       ):
          _SCREAMING_SNAKE_CASE												=RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
          _SCREAMING_SNAKE_CASE												=args.n_docs
          if args.index_name is not None:
               _SCREAMING_SNAKE_CASE												=args.index_name
          if args.index_path is not None:
               _SCREAMING_SNAKE_CASE												=args.index_path
     else:
          _SCREAMING_SNAKE_CASE												=BartForConditionalGeneration

     _SCREAMING_SNAKE_CASE												=(
         [f.path for f in os.scandir(args.model_name_or_path       ) if f.is_dir()]
         if args.eval_all_checkpoints
         else [args.model_name_or_path]
     )

     logger.info('Evaluate the following checkpoints: %s'							,				_UpperCamelCase       )

     _SCREAMING_SNAKE_CASE												=get_scores if args.eval_mode == 'e2e' else get_precision_at_k
     _SCREAMING_SNAKE_CASE												=evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

     for checkpoint in checkpoints:
          if os.path.exists(args.predictions_path       ) and (not args.recalculate):
               logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path       )       )
               score_fn(_UpperCamelCase							,				args.predictions_path							,				args.gold_data_path       )
               continue

          logger.info('***** Running evaluation for {} *****'.format(_UpperCamelCase       )       )
          logger.info('  Batch size = %d'							,				args.eval_batch_size       )
          logger.info('  Predictions will be stored under {}'.format(args.predictions_path       )       )

          if args.model_type.startswith('rag'       ):
               _SCREAMING_SNAKE_CASE												=RagRetriever.from_pretrained(_UpperCamelCase							,				**_UpperCamelCase       )
               _SCREAMING_SNAKE_CASE												=model_class.from_pretrained(_UpperCamelCase							,				retriever=_UpperCamelCase							,				**_UpperCamelCase       )
               model.retriever.init_retrieval()
          else:
               _SCREAMING_SNAKE_CASE												=model_class.from_pretrained(_UpperCamelCase							,				**_UpperCamelCase       )
          model.to(args.device       )

          with open(args.evaluation_set							,				'r'       ) as eval_file, open(args.predictions_path							,				'w'       ) as preds_file:
               _SCREAMING_SNAKE_CASE												=[]
               for line in tqdm(_UpperCamelCase       ):
                    questions.append(line.strip()       )
                    if len(_UpperCamelCase       ) == args.eval_batch_size:
                         _SCREAMING_SNAKE_CASE												=evaluate_batch_fn(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )
                         preds_file.write('\n'.join(_UpperCamelCase       ) + '\n'       )
                         preds_file.flush()
                         _SCREAMING_SNAKE_CASE												=[]
               if len(_UpperCamelCase       ) > 0:
                    _SCREAMING_SNAKE_CASE												=evaluate_batch_fn(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )
                    preds_file.write('\n'.join(_UpperCamelCase       )       )
                    preds_file.flush()

               score_fn(_UpperCamelCase							,				args.predictions_path							,				args.gold_data_path       )


if __name__ == "__main__":
     lowerCamelCase				:   List[str]      				=  get_args()
     main(args)



'''simple docstring'''
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				:   Optional[Any]      				=  False


if is_vision_available():
     from PIL import Image
else:







     class 							A__					:





       @staticmethod
       def 		A						(	*_a   :			Any							,      **_a   :			str				)			->					Optional[int]:





            '''simple docstring'''






            pass







@is_pipeline_test
@require_vision
class 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_VISION_2_SEQ_MAPPING
  A__					=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING





  def 		A						(	self   :			List[str]							,      _a   :			List[Any]							,      _a   :			Any							,      _a   :			Dict				)			->					Any:





       '''simple docstring'''






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





  def 		A						(	self   :			str							,      _a   :			int							,      _a   :			Tuple				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               [{'generated_text': ANY(_a				)}],
               [{'generated_text': ANY(_a				)}],
           ]							,      )





  @require_tf
  def 		A						(	self   :			Any				)			->					Optional[int]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               {
                   'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
               },
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      max_new_tokens=1				)
       self.assertEqual(
           _a							,      [{'generated_text': 'growth'}]							,      )





  @require_torch
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-vit-gpt2'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(
           _a							,      [
               {
                   'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
               },
           ]							,      )

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
               [
                   {
                       'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                   }
               ],
           ]							,      )





  @require_torch
  def 		A						(	self   :			Union[str, Any]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'
       _SCREAMING_SNAKE_CASE												='a photo of'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertTrue(outputs[0]['generated_text'].startswith(_a				)				)





  @slow
  @require_torch
  def 		A						(	self   :			Any				)			->					List[str]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [{'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 		A						(	self   :			List[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='Salesforce/blip-image-captioning-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

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





  @slow
  @require_torch
  def 		A						(	self   :			str				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='microsoft/git-base-coco'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

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





  @slow
  @require_torch
  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='Salesforce/blip-image-captioning-base'				)
       _SCREAMING_SNAKE_CASE												='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
       _SCREAMING_SNAKE_CASE												=Image.open(requests.get(_a							,      stream=_a				).raw				)

       _SCREAMING_SNAKE_CASE												='a photography of'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a photography of a volcano'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @slow
  @require_torch
  def 		A						(	self   :			str				)			->					Dict:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE												='a photo of a'

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

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=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 		A						(	self   :			List[Any]				)			->					Union[str, Any]:





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE												='What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

       _SCREAMING_SNAKE_CASE												=pipe(_a							,      prompt=_a				)
       self.assertEqual(_a							,      [{'generated_text': 'ash cloud'}]				)

       with self.assertRaises(_a				):
            _SCREAMING_SNAKE_CASE												=pipe([image, image]							,      prompt=[prompt, prompt]				)





  @slow
  @require_tf
  def 		A						(	self   :			List[str]				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('image-to-text'							,      model='ydshieh/vit-gpt2-coco-en'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												='./tests/fixtures/tests_samples/COCO/000000039769.png'

       _SCREAMING_SNAKE_CASE												=pipe(_a				)
       self.assertEqual(_a							,      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)

       _SCREAMING_SNAKE_CASE												=pipe([image, image]				)
       self.assertEqual(
           _a							,      [
               [{'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 '}],
           ]							,      )
