



'''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


A_				:					Dict							=      logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()




def        snake_case_   (	lowerCAmelCase_						)->						Union[str, Any]:





       '''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        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]
       _UpperCAmelCase    :		List[str]     	=					[]

       if args.gold_data_mode == "qa":
              _UpperCAmelCase    :		Any     	=					pd.read_csv(lowerCAmelCase_			,  sep="""\t"""			,  header=lowerCAmelCase_						)
              for answer_list in data[1]:
                     _UpperCAmelCase    :		int     	=					ast.literal_eval(lowerCAmelCase_						)
                     answers.append(lowerCAmelCase_						)
       else:
              _UpperCAmelCase    :		Tuple     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]
              _UpperCAmelCase    :		str     	=					[[reference] for reference in references]

       _UpperCAmelCase    :		Tuple     	=					0
       for prediction, ground_truths in zip(lowerCAmelCase_			,  lowerCAmelCase_						):
              total += 1
              em += metric_max_over_ground_truths(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
              fa += metric_max_over_ground_truths(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		List[Any]     	=					1_0_0.0 * em / total
       _UpperCAmelCase    :		Dict     	=					1_0_0.0 * fa / total

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




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					args.k
       _UpperCAmelCase    :		Tuple     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]
       _UpperCAmelCase    :		List[str]     	=					[line.strip() for line in open(lowerCAmelCase_			,  """r"""						).readlines()]

       _UpperCAmelCase    :		List[Any]     	=					0
       for hypo, reference in zip(lowerCAmelCase_			,  lowerCAmelCase_						):
              _UpperCAmelCase    :		List[Any]     	=					set(hypo.split("""\t"""						)[:k]						)
              _UpperCAmelCase    :		Optional[int]     	=					set(reference.split("""\t"""						)						)
              total += 1
              em += len(hypo_provenance & ref_provenance						) / k

       _UpperCAmelCase    :		Optional[Any]     	=					1_0_0.0 * em / total
       logger.info(F'''Precision@{k}: {em: .2f}'''						)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						Any:





       '''simple docstring'''


       def strip_title(lowerCAmelCase_						):
              if title.startswith("""\""""						):
                     _UpperCAmelCase    :		str     	=					title[1:]
              if title.endswith("""\""""						):
                     _UpperCAmelCase    :		Tuple     	=					title[:-1]
              return title

       _UpperCAmelCase    :		List[Any]     	=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           lowerCAmelCase_			,  return_tensors="""pt"""			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_			,  )["""input_ids"""].to(args.device						)

       _UpperCAmelCase    :		Any     	=					rag_model.rag.question_encoder(lowerCAmelCase_						)
       _UpperCAmelCase    :		Optional[int]     	=					question_enc_outputs[0]

       _UpperCAmelCase    :		Optional[int]     	=					rag_model.retriever(
           lowerCAmelCase_			,  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    :		List[str]     	=					rag_model.retriever.index.get_doc_dicts(result.doc_ids						)
       _UpperCAmelCase    :		List[str]     	=					[]
       for docs in all_docs:
              _UpperCAmelCase    :		Union[str, Any]     	=					[strip_title(lowerCAmelCase_						) for title in docs["""title"""]]
              provenance_strings.append("""\t""".join(lowerCAmelCase_						)						)
       return provenance_strings




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       with torch.no_grad():
              _UpperCAmelCase    :		List[Any]     	=					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  lowerCAmelCase_			,  return_tensors="""pt"""			,  padding=lowerCAmelCase_			,  truncation=lowerCAmelCase_						)

              _UpperCAmelCase    :		str     	=					inputs_dict.input_ids.to(args.device						)
              _UpperCAmelCase    :		Optional[Any]     	=					inputs_dict.attention_mask.to(args.device						)
              _UpperCAmelCase    :		Any     	=					rag_model.generate(  # rag_model overwrites generate
                  lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  num_beams=args.num_beams			,  min_length=args.min_length			,  max_length=args.max_length			,  early_stopping=lowerCAmelCase_			,  num_return_sequences=1			,  bad_words_ids=[[0, 0]]			,  )
              _UpperCAmelCase    :		str     	=					rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase_			,  skip_special_tokens=lowerCAmelCase_						)

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

              return answers




def        snake_case_   (	)->						int:





       '''simple docstring'''


       _UpperCAmelCase    :		Optional[int]     	=					argparse.ArgumentParser()
       parser.add_argument(
           """--model_type"""			,  choices=["""rag_sequence""", """rag_token""", """bart"""]			,  type=lowerCAmelCase_			,  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=lowerCAmelCase_			,  choices=["""exact""", """compressed""", """legacy"""]			,  type=lowerCAmelCase_			,  help="""RAG model retriever type"""			,  )
       parser.add_argument(
           """--index_path"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  help="""Path to the retrieval index"""			,  )
       parser.add_argument("""--n_docs"""			,  default=5			,  type=lowerCAmelCase_			,  help="""Number of retrieved docs"""						)
       parser.add_argument(
           """--model_name_or_path"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  required=lowerCAmelCase_			,  help="""Path to pretrained checkpoints or model identifier from huggingface.co/models"""			,  )
       parser.add_argument(
           """--eval_mode"""			,  choices=["""e2e""", """retrieval"""]			,  default="""e2e"""			,  type=lowerCAmelCase_			,  help=(
               """Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
               """ precision@k."""
           )			,  )
       parser.add_argument("""--k"""			,  default=1			,  type=lowerCAmelCase_			,  help="""k for the precision@k calculation"""						)
       parser.add_argument(
           """--evaluation_set"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  required=lowerCAmelCase_			,  help="""Path to a file containing evaluation samples"""			,  )
       parser.add_argument(
           """--gold_data_path"""			,  default=lowerCAmelCase_			,  type=lowerCAmelCase_			,  required=lowerCAmelCase_			,  help="""Path to a tab-separated file with gold samples"""			,  )
       parser.add_argument(
           """--gold_data_mode"""			,  default="""qa"""			,  type=lowerCAmelCase_			,  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=lowerCAmelCase_			,  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=lowerCAmelCase_			,  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=lowerCAmelCase_			,  help="""Number of beams to be used when generating answers"""			,  )
       parser.add_argument("""--min_length"""			,  default=1			,  type=lowerCAmelCase_			,  help="""Min length of the generated answers"""						)
       parser.add_argument("""--max_length"""			,  default=50			,  type=lowerCAmelCase_			,  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    :		Any     	=					parser.parse_args()
       _UpperCAmelCase    :		Dict     	=					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)
       return args




def        snake_case_   (	lowerCAmelCase_						)->						Dict:





       '''simple docstring'''


       _UpperCAmelCase    :		Dict     	=					{}
       if args.model_type is None:
              _UpperCAmelCase    :		Tuple     	=					infer_model_type(args.model_name_or_path						)
              assert args.model_type is not None
       if args.model_type.startswith("""rag"""						):
              _UpperCAmelCase    :		List[Any]     	=					RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
              _UpperCAmelCase    :		str     	=					args.n_docs
              if args.index_name is not None:
                     _UpperCAmelCase    :		Tuple     	=					args.index_name
              if args.index_path is not None:
                     _UpperCAmelCase    :		str     	=					args.index_path
       else:
              _UpperCAmelCase    :		Union[str, Any]     	=					BartForConditionalGeneration

       _UpperCAmelCase    :		Optional[int]     	=					(
           [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"""			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		str     	=					get_scores if args.eval_mode == """e2e""" else get_precision_at_k
       _UpperCAmelCase    :		Union[str, Any]     	=					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(lowerCAmelCase_			,  args.predictions_path			,  args.gold_data_path						)
                     continue

              logger.info("""***** Running evaluation for {} *****""".format(lowerCAmelCase_						)						)
              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(lowerCAmelCase_			,  **lowerCAmelCase_						)
                     _UpperCAmelCase    :		int     	=					model_class.from_pretrained(lowerCAmelCase_			,  retriever=lowerCAmelCase_			,  **lowerCAmelCase_						)
                     model.retriever.init_retrieval()
              else:
                     _UpperCAmelCase    :		Optional[Any]     	=					model_class.from_pretrained(lowerCAmelCase_			,  **lowerCAmelCase_						)
              model.to(args.device						)

              with open(args.evaluation_set			,  """r"""						) as eval_file, open(args.predictions_path			,  """w"""						) as preds_file:
                     _UpperCAmelCase    :		List[str]     	=					[]
                     for line in tqdm(lowerCAmelCase_						):
                            questions.append(line.strip()						)
                            if len(lowerCAmelCase_						) == args.eval_batch_size:
                                   _UpperCAmelCase    :		Any     	=					evaluate_batch_fn(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
                                   preds_file.write("""\n""".join(lowerCAmelCase_						) + """\n"""						)
                                   preds_file.flush()
                                   _UpperCAmelCase    :		Optional[int]     	=					[]
                     if len(lowerCAmelCase_						) > 0:
                            _UpperCAmelCase    :		str     	=					evaluate_batch_fn(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)
                            preds_file.write("""\n""".join(lowerCAmelCase_						)						)
                            preds_file.flush()

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


if __name__ == "__main__":
   A_				:					Optional[Any]							=      get_args()
   main(args)









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCAmelCase						:    Optional[Any]      =  logging.get_logger(__name__)


if is_vision_available():
     import PIL




class  __lowerCAmelCase							(				lowerCAmelCase):
       _a			       =    ['''pixel_values''']






       def __init__( self:       Optional[int]							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       Dict[str, int] = None							,       _lowerCAmelCase:       PILImageResampling = PILImageResampling.BILINEAR							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       bool = False							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       Union[int, float] = 1 / 2_55							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       **_lowerCAmelCase:       Dict							,       ):
         super().__init__(**_lowerCAmelCase					)

         lowercase     :Tuple   						=    size if size is not None else {"height": 25_60, "width": 19_20}
         if isinstance(_lowerCAmelCase							,       (tuple, list)					):
           # The previous feature extractor size parameter was in (width, height) format
           lowercase     :Union[str, Any]   						=    size[::-1]
         lowercase     :int   						=    get_size_dict(_lowerCAmelCase					)

         lowercase     :Optional[Any]   						=    do_resize
         lowercase     :int   						=    size
         lowercase     :List[str]   						=    resample
         lowercase     :Any   						=    do_thumbnail
         lowercase     :Optional[int]   						=    do_align_long_axis
         lowercase     :Tuple   						=    do_pad
         lowercase     :Union[str, Any]   						=    do_rescale
         lowercase     :Union[str, Any]   						=    rescale_factor
         lowercase     :Optional[int]   						=    do_normalize
         lowercase     :Dict   						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase     :Union[str, Any]   						=    image_std if image_std is not None else IMAGENET_STANDARD_STD






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None					):
         lowercase						,     lowercase     :int   						=    get_image_size(_lowerCAmelCase					)
         lowercase						,     lowercase     :str   						=    size["height"], size["width"]

         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
           lowercase     :Any   						=    np.rotaa(_lowerCAmelCase							,       3					)

         if data_format is not None:
           lowercase     :Optional[int]   						=    to_channel_dimension_format(_lowerCAmelCase							,       _lowerCAmelCase					)

         return image






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       bool = False							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       ):
         lowercase						,     lowercase     :Tuple   						=    size["height"], size["width"]
         lowercase						,     lowercase     :Optional[int]   						=    get_image_size(_lowerCAmelCase					)

         lowercase     :List[Any]   						=    output_width - input_width
         lowercase     :Dict   						=    output_height - input_height

         if random_padding:
           lowercase     :List[str]   						=    np.random.randint(low=0							,       high=delta_height + 1					)
           lowercase     :List[Any]   						=    np.random.randint(low=0							,       high=delta_width + 1					)
         else:
           lowercase     :Dict   						=    delta_height // 2
           lowercase     :int   						=    delta_width // 2

         lowercase     :Optional[int]   						=    delta_height - pad_top
         lowercase     :Dict   						=    delta_width - pad_left

         lowercase     :Optional[Any]   						=    ((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(_lowerCAmelCase							,       _lowerCAmelCase							,       data_format=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str							,       *_lowerCAmelCase:       List[str]							,       **_lowerCAmelCase:       List[str]					):
         logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
         return self.pad_image(*_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       PILImageResampling = PILImageResampling.BICUBIC							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       int							,       ):
         lowercase						,     lowercase     :Any   						=    get_image_size(_lowerCAmelCase					)
         lowercase						,     lowercase     :List[str]   						=    size["height"], size["width"]

         # We always resize to the smallest of either the input or output size.
         lowercase     :Union[str, Any]   						=    min(_lowerCAmelCase							,       _lowerCAmelCase					)
         lowercase     :Any   						=    min(_lowerCAmelCase							,       _lowerCAmelCase					)

         if height == input_height and width == input_width:
           return image

         if input_height > input_width:
           lowercase     :Union[str, Any]   						=    int(input_width * height / input_height					)
         elif input_width > input_height:
           lowercase     :int   						=    int(input_height * width / input_width					)

         return resize(
             _lowerCAmelCase							,       size=(height, width)							,       resample=_lowerCAmelCase							,       reducing_gap=2.0							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       PILImageResampling = PILImageResampling.BICUBIC							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       str							,       ):
         lowercase     :Optional[Any]   						=    get_size_dict(_lowerCAmelCase					)
         lowercase     :Optional[Any]   						=    min(size["height"]							,       size["width"]					)
         lowercase     :Tuple   						=    get_resize_output_image_size(_lowerCAmelCase							,       size=_lowerCAmelCase							,       default_to_square=_lowerCAmelCase					)
         lowercase     :Optional[int]   						=    resize(_lowerCAmelCase							,       size=_lowerCAmelCase							,       resample=_lowerCAmelCase							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)
         return resized_image






       def        SCREAMING_SNAKE_CASE     ( self:       Dict							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Union[int, float]							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       List[Any]							,       ):
         return rescale(_lowerCAmelCase							,       scale=_lowerCAmelCase							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Any							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Union[float, List[float]]							,       _lowerCAmelCase:       Union[float, List[float]]							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       Dict							,       ):
         return normalize(_lowerCAmelCase							,       mean=_lowerCAmelCase							,       std=_lowerCAmelCase							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple							,       _lowerCAmelCase:       ImageInput							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       Dict[str, int] = None							,       _lowerCAmelCase:       PILImageResampling = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       bool = False							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       float = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       _lowerCAmelCase:       Optional[Union[str, TensorType]] = None							,       _lowerCAmelCase:       Optional[ChannelDimension] = ChannelDimension.FIRST							,       **_lowerCAmelCase:       Optional[Any]							,       ):
         lowercase     :Union[str, Any]   						=    do_resize if do_resize is not None else self.do_resize
         lowercase     :Optional[int]   						=    size if size is not None else self.size
         if isinstance(_lowerCAmelCase							,       (tuple, list)					):
           # Previous feature extractor had size in (width, height) format
           lowercase     :List[str]   						=    size[::-1]
         lowercase     :List[str]   						=    get_size_dict(_lowerCAmelCase					)
         lowercase     :Optional[int]   						=    resample if resample is not None else self.resample
         lowercase     :Union[str, Any]   						=    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         lowercase     :str   						=    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         lowercase     :int   						=    do_pad if do_pad is not None else self.do_pad
         lowercase     :Optional[Any]   						=    do_rescale if do_rescale is not None else self.do_rescale
         lowercase     :str   						=    rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase     :int   						=    do_normalize if do_normalize is not None else self.do_normalize
         lowercase     :Dict   						=    image_mean if image_mean is not None else self.image_mean
         lowercase     :str   						=    image_std if image_std is not None else self.image_std

         lowercase     :Any   						=    make_list_of_images(_lowerCAmelCase					)

         if not valid_images(_lowerCAmelCase					):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."					)

         if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."					)

         if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."					)

         if do_pad and size is None:
           raise ValueError("Size must be specified if do_pad is True."					)

         if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."					)

         # All transformations expect numpy arrays.
         lowercase     :Union[str, Any]   						=    [to_numpy_array(_lowerCAmelCase					) for image in images]

         if do_align_long_axis:
           lowercase     :Union[str, Any]   						=    [self.align_long_axis(_lowerCAmelCase							,       size=_lowerCAmelCase					) for image in images]

         if do_resize:
           lowercase     :Optional[Any]   						=    [self.resize(image=_lowerCAmelCase							,       size=_lowerCAmelCase							,       resample=_lowerCAmelCase					) for image in images]

         if do_thumbnail:
           lowercase     :str   						=    [self.thumbnail(image=_lowerCAmelCase							,       size=_lowerCAmelCase					) for image in images]

         if do_pad:
           lowercase     :List[Any]   						=    [self.pad_image(image=_lowerCAmelCase							,       size=_lowerCAmelCase							,       random_padding=_lowerCAmelCase					) for image in images]

         if do_rescale:
           lowercase     :Optional[Any]   						=    [self.rescale(image=_lowerCAmelCase							,       scale=_lowerCAmelCase					) for image in images]

         if do_normalize:
           lowercase     :Tuple   						=    [self.normalize(image=_lowerCAmelCase							,       mean=_lowerCAmelCase							,       std=_lowerCAmelCase					) for image in images]

         lowercase     :List[str]   						=    [to_channel_dimension_format(_lowerCAmelCase							,       _lowerCAmelCase					) for image in images]

         lowercase     :List[str]   						=    {"pixel_values": images}
         return BatchFeature(data=_lowerCAmelCase							,       tensor_type=_lowerCAmelCase					)



