code: 
  
  
  
  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
  
  
  __UpperCAmelCase      = logging.getLogger(__name__)
  logging.basicConfig(level=logging.INFO)
  
  transformers_logging.set_verbosity_info()
  
  
  def 	UpperCamelCase			(     snake_case__ :					Optional[int]			)			->  List[str]:
     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 	UpperCamelCase			(     snake_case__ :					List[Any] ,  snake_case__ :					Dict ,  snake_case__ :					Any			)			->  int:
     return max(metric_fn(snake_case__ ,  snake_case__			) for gt in ground_truths			)
  
  
  def 	UpperCamelCase			(     snake_case__ :					Optional[int] ,  snake_case__ :					List[str] ,  snake_case__ :					Optional[int]			)			->  int:
     UpperCamelCase					:						Optional[Any]					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]
     UpperCamelCase					:						str					=			[]
  
     if args.gold_data_mode == "qa":
        UpperCamelCase					:						Optional[Any]					=			pd.read_csv(snake_case__ ,  sep='\t' ,  header=snake_case__			)
        for answer_list in data[1]:
           UpperCamelCase					:						Tuple					=			ast.literal_eval(snake_case__			)
           answers.append(snake_case__			)
     else:
        UpperCamelCase					:						Tuple					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]
        UpperCamelCase					:						List[str]					=			[[reference] for reference in references]
  
     UpperCamelCase					:						int					=			0
     for prediction, ground_truths in zip(snake_case__ ,  snake_case__			):
        total += 1
        em += metric_max_over_ground_truths(snake_case__ ,  snake_case__ ,  snake_case__			)
        fa += metric_max_over_ground_truths(snake_case__ ,  snake_case__ ,  snake_case__			)
  
     UpperCamelCase					:						List[Any]					=			100.0 * em / total
     UpperCamelCase					:						Optional[Any]					=			100.0 * fa / total
  
     logger.info(F"""F1: {fa:.2f}"""			)
     logger.info(F"""EM: {em:.2f}"""			)
  
  
  def 	UpperCamelCase			(     snake_case__ :					List[Any] ,  snake_case__ :					List[str] ,  snake_case__ :					List[str]			)			->  Optional[Any]:
     UpperCamelCase					:						List[Any]					=			args.k
     UpperCamelCase					:						Dict					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]
     UpperCamelCase					:						Optional[Any]					=			[line.strip() for line in open(snake_case__ ,  'r'			).readlines()]
  
     UpperCamelCase					:						Optional[Any]					=			0
     for hypo, reference in zip(snake_case__ ,  snake_case__			):
        UpperCamelCase					:						Dict					=			set(hypo.split('\t'			)[:k]			)
        UpperCamelCase					:						int					=			set(reference.split('\t'			)			)
        total += 1
        em += len(hypo_provenance & ref_provenance			) / k
  
     UpperCamelCase					:						Optional[int]					=			100.0 * em / total
     logger.info(F"""Precision@{k}: {em: .2f}"""			)
  
  
  def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					List[str] ,  snake_case__ :					Any			)			->  str:
     def strip_title(snake_case__ :					Optional[int]			):
        if title.startswith('\"'			):
           UpperCamelCase					:						List[str]					=			title[1:]
        if title.endswith('\"'			):
           UpperCamelCase					:						Dict					=			title[:-1]
        return title
  
     UpperCamelCase					:						int					=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
         snake_case__ ,  return_tensors='pt' ,  padding=snake_case__ ,  truncation=snake_case__ ,  )["input_ids"].to(args.device			)
  
     UpperCamelCase					:						Union[str, Any]					=			rag_model.rag.question_encoder(snake_case__			)
     UpperCamelCase					:						Tuple					=			question_enc_outputs[0]
  
     UpperCamelCase					:						str					=			rag_model.retriever(
         snake_case__ ,  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' ,  )
     UpperCamelCase					:						Optional[int]					=			rag_model.retriever.index.get_doc_dicts(result.doc_ids			)
     UpperCamelCase					:						Union[str, Any]					=			[]
     for docs in all_docs:
        UpperCamelCase					:						Dict					=			[strip_title(snake_case__			) for title in docs["title"]]
        provenance_strings.append('\t'.join(snake_case__			)			)
     return provenance_strings
  
  
  def 	UpperCamelCase			(     snake_case__ :					Any ,  snake_case__ :					Union[str, Any] ,  snake_case__ :					List[str]			)			->  List[Any]:
     with torch.no_grad():
        UpperCamelCase					:						Union[str, Any]					=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
            snake_case__ ,  return_tensors='pt' ,  padding=snake_case__ ,  truncation=snake_case__			)
  
        UpperCamelCase					:						Any					=			inputs_dict.input_ids.to(args.device			)
        UpperCamelCase					:						List[Any]					=			inputs_dict.attention_mask.to(args.device			)
        UpperCamelCase					:						List[str]					=			rag_model.generate(  # rag_model overwrites generate
            snake_case__ ,  attention_mask=snake_case__ ,  num_beams=args.num_beams ,  min_length=args.min_length ,  max_length=args.max_length ,  early_stopping=snake_case__ ,  num_return_sequences=1 ,  bad_words_ids=[[0, 0]] ,  )
        UpperCamelCase					:						Dict					=			rag_model.retriever.generator_tokenizer.batch_decode(snake_case__ ,  skip_special_tokens=snake_case__			)
  
        if args.print_predictions:
           for q, a in zip(snake_case__ ,  snake_case__			):
              logger.info('Q: {} - A: {}'.format(snake_case__ ,  snake_case__			)			)
  
        return answers
  
  
  def 	UpperCamelCase			(     )			->  str:
     UpperCamelCase					:						Dict					=			argparse.ArgumentParser()
     parser.add_argument(
         '--model_type' ,  choices=['rag_sequence', 'rag_token', 'bart'] ,  type=snake_case__ ,  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=snake_case__ ,  choices=['exact', 'compressed', 'legacy'] ,  type=snake_case__ ,  help='RAG model retriever type' ,  )
     parser.add_argument(
         '--index_path' ,  default=snake_case__ ,  type=snake_case__ ,  help='Path to the retrieval index' ,  )
     parser.add_argument('--n_docs' ,  default=5 ,  type=snake_case__ ,  help='Number of retrieved docs'			)
     parser.add_argument(
         '--model_name_or_path' ,  default=snake_case__ ,  type=snake_case__ ,  required=snake_case__ ,  help='Path to pretrained checkpoints or model identifier from huggingface.co/models' ,  )
     parser.add_argument(
         '--eval_mode' ,  choices=['e2e', 'retrieval'] ,  default='e2e' ,  type=snake_case__ ,  help=(
             'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
             ' precision@k.'
         ) ,  )
     parser.add_argument('--k' ,  default=1 ,  type=snake_case__ ,  help='k for the precision@k calculation'			)
     parser.add_argument(
         '--evaluation_set' ,  default=snake_case__ ,  type=snake_case__ ,  required=snake_case__ ,  help='Path to a file containing evaluation samples' ,  )
     parser.add_argument(
         '--gold_data_path' ,  default=snake_case__ ,  type=snake_case__ ,  required=snake_case__ ,  help='Path to a tab-separated file with gold samples' ,  )
     parser.add_argument(
         '--gold_data_mode' ,  default='qa' ,  type=snake_case__ ,  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=snake_case__ ,  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=snake_case__ ,  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=snake_case__ ,  help='Number of beams to be used when generating answers' ,  )
     parser.add_argument('--min_length' ,  default=1 ,  type=snake_case__ ,  help='Min length of the generated answers'			)
     parser.add_argument('--max_length' ,  default=50 ,  type=snake_case__ ,  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.' ,  )
     UpperCamelCase					:						List[str]					=			parser.parse_args()
     UpperCamelCase					:						Tuple					=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)
     return args
  
  
  def 	UpperCamelCase			(     snake_case__ :					List[Any]			)			->  Dict:
     UpperCamelCase					:						Optional[int]					=			{}
     if args.model_type is None:
        UpperCamelCase					:						Union[str, Any]					=			infer_model_type(args.model_name_or_path			)
        assert args.model_type is not None
     if args.model_type.startswith('rag'			):
        UpperCamelCase					:						Optional[int]					=			RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
        UpperCamelCase					:						Tuple					=			args.n_docs
        if args.index_name is not None:
           UpperCamelCase					:						List[str]					=			args.index_name
        if args.index_path is not None:
           UpperCamelCase					:						List[str]					=			args.index_path
     else:
        UpperCamelCase					:						str					=			BartForConditionalGeneration
  
     UpperCamelCase					:						Any					=			(
         [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' ,  snake_case__			)
  
     UpperCamelCase					:						Dict					=			get_scores if args.eval_mode == "e2e" else get_precision_at_k
     UpperCamelCase					:						Optional[int]					=			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(snake_case__ ,  args.predictions_path ,  args.gold_data_path			)
           continue
  
        logger.info('***** Running evaluation for {} *****'.format(snake_case__			)			)
        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'			):
           UpperCamelCase					:						Optional[int]					=			RagRetriever.from_pretrained(snake_case__ ,  **snake_case__			)
           UpperCamelCase					:						Tuple					=			model_class.from_pretrained(snake_case__ ,  retriever=snake_case__ ,  **snake_case__			)
           model.retriever.init_retrieval()
        else:
           UpperCamelCase					:						List[str]					=			model_class.from_pretrained(snake_case__ ,  **snake_case__			)
        model.to(args.device			)
  
        with open(args.evaluation_set ,  'r'			) as eval_file, open(args.predictions_path ,  'w'			) as preds_file:
           UpperCamelCase					:						List[Any]					=			[]
           for line in tqdm(snake_case__			):
              questions.append(line.strip()			)
              if len(snake_case__			) == args.eval_batch_size:
                 UpperCamelCase					:						Optional[Any]					=			evaluate_batch_fn(snake_case__ ,  snake_case__ ,  snake_case__			)
                 preds_file.write('\n'.join(snake_case__			) + '\n'			)
                 preds_file.flush()
                 UpperCamelCase					:						Optional[int]					=			[]
           if len(snake_case__			) > 0:
              UpperCamelCase					:						int					=			evaluate_batch_fn(snake_case__ ,  snake_case__ ,  snake_case__			)
              preds_file.write('\n'.join(snake_case__			)			)
              preds_file.flush()
  
           score_fn(snake_case__ ,  args.predictions_path ,  args.gold_data_path			)
  
  
  if __name__ == "__main__":
      __UpperCAmelCase      = get_args()
      main(args)
  
  
  
  
style_context: 
  
  
  
  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 ViltImageProcessor
  
  
  
  class        lowerCAmelCase_	(						unittest.TestCase							):
         def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=7,    SCREAMING_SNAKE_CASE_=3,    SCREAMING_SNAKE_CASE_=18,    SCREAMING_SNAKE_CASE_=30,    SCREAMING_SNAKE_CASE_=400,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
            UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
            UpperCamelCase					:						Dict					=			parent
            UpperCamelCase					:						str					=			batch_size
            UpperCamelCase					:						str					=			num_channels
            UpperCamelCase					:						Optional[int]					=			image_size
            UpperCamelCase					:						List[Any]					=			min_resolution
            UpperCamelCase					:						Tuple					=			max_resolution
            UpperCamelCase					:						Tuple					=			do_resize
            UpperCamelCase					:						Any					=			size
            UpperCamelCase					:						int					=			size_divisor
            UpperCamelCase					:						str					=			do_normalize
            UpperCamelCase					:						Optional[int]					=			image_mean
            UpperCamelCase					:						List[str]					=			image_std
         def       snake_case_ (    self )  ->	List[Any]:
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "size_divisor": self.size_divisor,
            }
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
            if not batched:
               UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
               UpperCamelCase					:						int					=			image_inputs[0]
               if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.size
               else:
                  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
               UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
               if h < w:
                  UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			size, scale * w
               else:
                  UpperCamelCase			,  UpperCamelCase					:						List[str]					=			scale * h, size
  
               UpperCamelCase					:						int					=			int((1333 / 800) * size )
               if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                  UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                  UpperCamelCase					:						List[Any]					=			newh * scale
                  UpperCamelCase					:						Optional[int]					=			neww * scale
  
               UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
               UpperCamelCase			,  UpperCamelCase					:						int					=			(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )
  
            else:
               UpperCamelCase					:						Dict					=			[]
               for image in image_inputs:
                  UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                  expected_values.append((expected_height, expected_width) )
               UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
               UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
  
            return expected_height, expected_width
  
  
  
  
  
  
  @require_torch
  @require_vision
  class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
         UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
         def       snake_case_ (    self )  ->	Union[str, Any]:
            UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
         @property
         def       snake_case_ (    self )  ->	Dict:
            return self.image_processor_tester.prepare_image_processor_dict()
         def       snake_case_ (    self )  ->	Union[str, Any]:
            UpperCamelCase					:						List[str]					=			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' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
         def       snake_case_ (    self )  ->	List[Any]:
            UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size,    {'shortest_edge': 30} )
  
            UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
            self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
         def       snake_case_ (    self )  ->	Dict:
            pass
         def       snake_case_ (    self )  ->	str:
            # Initialize image_processing
            UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            UpperCamelCase					:						Any					=			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
            UpperCamelCase					:						int					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
         def       snake_case_ (    self )  ->	List[str]:
            # Initialize image_processing
            UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            UpperCamelCase					:						Optional[Any]					=			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
            UpperCamelCase					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
  
  
  
  
  
  
         def       snake_case_ (    self )  ->	Optional[Any]:
            # Initialize image_processing
            UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            UpperCamelCase					:						Dict					=			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
            UpperCamelCase					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
  
  
  
  