


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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  return max(metric_fn(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) for gt in ground_truths       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Union[str, Any]		     =					[line.strip() for line in open(SCREAMING_SNAKE_CASE ,			'''r'''       ).readlines()]
  A_      :				str		     =					[]

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

  A_      :				List[str]		     =					0
  for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
    total += 1
    em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
    fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  A_      :				Any		     =					1_0_0.0 * em / total
  A_      :				Dict		     =					1_0_0.0 * fa / total

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					args.k
  A_      :				Optional[Any]		     =					[line.strip() for line in open(SCREAMING_SNAKE_CASE ,			'''r'''       ).readlines()]
  A_      :				str		     =					[line.strip() for line in open(SCREAMING_SNAKE_CASE ,			'''r'''       ).readlines()]

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

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  def strip_title(SCREAMING_SNAKE_CASE       ):
    if title.startswith('''"'''       ):
      A_      :				str		     =					title[1:]
    if title.endswith('''"'''       ):
      A_      :				Optional[int]		     =					title[:-1]
    return title

  A_      :				Tuple		     =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      SCREAMING_SNAKE_CASE ,			return_tensors='''pt''' ,			padding=SCREAMING_SNAKE_CASE ,			truncation=SCREAMING_SNAKE_CASE ,			)['''input_ids'''].to(args.device       )

  A_      :				Optional[int]		     =					rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					question_enc_outputs[0]

  A_      :				List[Any]		     =					rag_model.retriever(
      SCREAMING_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''' ,			)
  A_      :				Union[str, Any]		     =					rag_model.retriever.index.get_doc_dicts(result.doc_ids       )
  A_      :				List[Any]		     =					[]
  for docs in all_docs:
    A_      :				List[str]		     =					[strip_title(SCREAMING_SNAKE_CASE       ) for title in docs['''title''']]
    provenance_strings.append('''\t'''.join(SCREAMING_SNAKE_CASE       )       )
  return provenance_strings


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  with torch.no_grad():
    A_      :				str		     =					rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
        SCREAMING_SNAKE_CASE ,			return_tensors='''pt''' ,			padding=SCREAMING_SNAKE_CASE ,			truncation=SCREAMING_SNAKE_CASE       )

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

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

    return answers


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Optional[Any]		     =					argparse.ArgumentParser()
  parser.add_argument(
      '''--model_type''' ,			choices=['''rag_sequence''', '''rag_token''', '''bart'''] ,			type=SCREAMING_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=SCREAMING_SNAKE_CASE ,			choices=['''exact''', '''compressed''', '''legacy'''] ,			type=SCREAMING_SNAKE_CASE ,			help='''RAG model retriever type''' ,			)
  parser.add_argument(
      '''--index_path''' ,			default=SCREAMING_SNAKE_CASE ,			type=SCREAMING_SNAKE_CASE ,			help='''Path to the retrieval index''' ,			)
  parser.add_argument('''--n_docs''' ,			default=5 ,			type=SCREAMING_SNAKE_CASE ,			help='''Number of retrieved docs'''       )
  parser.add_argument(
      '''--model_name_or_path''' ,			default=SCREAMING_SNAKE_CASE ,			type=SCREAMING_SNAKE_CASE ,			required=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE ,			help='''k for the precision@k calculation'''       )
  parser.add_argument(
      '''--evaluation_set''' ,			default=SCREAMING_SNAKE_CASE ,			type=SCREAMING_SNAKE_CASE ,			required=SCREAMING_SNAKE_CASE ,			help='''Path to a file containing evaluation samples''' ,			)
  parser.add_argument(
      '''--gold_data_path''' ,			default=SCREAMING_SNAKE_CASE ,			type=SCREAMING_SNAKE_CASE ,			required=SCREAMING_SNAKE_CASE ,			help='''Path to a tab-separated file with gold samples''' ,			)
  parser.add_argument(
      '''--gold_data_mode''' ,			default='''qa''' ,			type=SCREAMING_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=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE ,			help='''Number of beams to be used when generating answers''' ,			)
  parser.add_argument('''--min_length''' ,			default=1 ,			type=SCREAMING_SNAKE_CASE ,			help='''Min length of the generated answers'''       )
  parser.add_argument('''--max_length''' ,			default=50 ,			type=SCREAMING_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.''' ,			)
  A_      :				str		     =					parser.parse_args()
  A_      :				Any		     =					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )
  return args


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Union[str, Any]		     =					{}
  if args.model_type is None:
    A_      :				int		     =					infer_model_type(args.model_name_or_path       )
    assert args.model_type is not None
  if args.model_type.startswith('''rag'''       ):
    A_      :				int		     =					RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
    A_      :				Tuple		     =					args.n_docs
    if args.index_name is not None:
      A_      :				Optional[int]		     =					args.index_name
    if args.index_path is not None:
      A_      :				int		     =					args.index_path
  else:
    A_      :				str		     =					BartForConditionalGeneration

  A_      :				Union[str, 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''' ,			SCREAMING_SNAKE_CASE       )

  A_      :				Union[str, Any]		     =					get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
  A_      :				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(SCREAMING_SNAKE_CASE ,			args.predictions_path ,			args.gold_data_path       )
      continue

    logger.info('''***** Running evaluation for {} *****'''.format(SCREAMING_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'''       ):
      A_      :				List[str]		     =					RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
      A_      :				Dict		     =					model_class.from_pretrained(SCREAMING_SNAKE_CASE ,			retriever=SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
      model.retriever.init_retrieval()
    else:
      A_      :				List[Any]		     =					model_class.from_pretrained(SCREAMING_SNAKE_CASE ,			**SCREAMING_SNAKE_CASE       )
    model.to(args.device       )

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

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


if __name__ == "__main__":
     UpperCamelCase								=	get_args()
     main(args)








import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


UpperCamelCase								=	None
UpperCamelCase								=	"""<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
UpperCamelCase								=	[
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]






@dataclass
class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    True
       snake_case      =    None
       # Automatically constructed
       snake_case      =    "PIL.Image.Image"
       snake_case      =    pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
       snake_case      =    field(default="Image"					,							init=UpperCamelCase					,							repr=UpperCamelCase			)



       def __call__(      self		)->int:




         '''simple docstring'''





         return self.pa_type



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->dict:




         '''simple docstring'''





         if config.PIL_AVAILABLE:
           import PIL.Image
         else:
           raise ImportError('''To support encoding images, please install \'Pillow\'.'''		)

         if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				Optional[int]		     =					np.array(_SCREAMING_SNAKE_CASE		)

         if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           return {"path": value, "bytes": None}
         elif isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           return {"path": None, "bytes": value}
         elif isinstance(_SCREAMING_SNAKE_CASE				,       np.ndarray		):
           # convert the image array to PNG/TIFF bytes
           return encode_np_array(_SCREAMING_SNAKE_CASE		)
         elif isinstance(_SCREAMING_SNAKE_CASE				,       PIL.Image.Image		):
           # convert the PIL image to bytes (default format is PNG/TIFF)
           return encode_pil_image(_SCREAMING_SNAKE_CASE		)
         elif value.get('''path'''		) is not None and os.path.isfile(value['''path''']		):
           # we set "bytes": None to not duplicate the data if they're already available locally
           return {"bytes": None, "path": value.get('''path'''		)}
         elif value.get('''bytes'''		) is not None or value.get('''path'''		) is not None:
           # store the image bytes, and path is used to infer the image format using the file extension
           return {"bytes": value.get('''bytes'''		), "path": value.get('''path'''		)}
         else:
           raise ValueError(
               F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None		)->"PIL.Image.Image":




         '''simple docstring'''





         if not self.decode:
           raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''		)

         if config.PIL_AVAILABLE:
           import PIL.Image
         else:
           raise ImportError('''To support decoding images, please install \'Pillow\'.'''		)

         if token_per_repo_id is None:
           A_      :				List[str]		     =					{}

         A_     ,       A_      :				str		     =					value['''path'''], value['''bytes''']
         if bytes_ is None:
           if path is None:
             raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''		)
           else:
             if is_local_path(_SCREAMING_SNAKE_CASE		):
               A_      :				List[str]		     =					PIL.Image.open(_SCREAMING_SNAKE_CASE		)
             else:
               A_      :				List[str]		     =					path.split('''::'''		)[-1]
               try:
                 A_      :				int		     =					string_to_dict(_SCREAMING_SNAKE_CASE				,       config.HUB_DATASETS_URL		)['''repo_id''']
                 A_      :				Optional[int]		     =					token_per_repo_id.get(_SCREAMING_SNAKE_CASE		)
               except ValueError:
                 A_      :				Any		     =					None
               with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''				,       use_auth_token=_SCREAMING_SNAKE_CASE		) as f:
                 A_      :				Optional[Any]		     =					BytesIO(f.read()		)
               A_      :				Dict		     =					PIL.Image.open(bytes_		)
         else:
           A_      :				Optional[int]		     =					PIL.Image.open(BytesIO(bytes_		)		)
         image.load()  # to avoid "Too many open files" errors
         return image



       def        _snake_case       (      self		)->Union["FeatureType", Dict[str, "FeatureType"]]:




         '''simple docstring'''





         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value('''binary'''		),
                 "path": Value('''string'''		),
             }
         )



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





         if pa.types.is_string(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           A_      :				List[Any]		     =					pa.StructArray.from_arrays([bytes_array, storage]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[str]		     =					pa.StructArray.from_arrays([storage, path_array]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_struct(storage.type		):
           if storage.type.get_field_index('''bytes'''		) >= 0:
             A_      :				Tuple		     =					storage.field('''bytes'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           if storage.type.get_field_index('''path'''		) >= 0:
             A_      :				Optional[Any]		     =					storage.field('''path'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				Union[str, Any]		     =					pa.StructArray.from_arrays([bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
           A_      :				Optional[Any]		     =					pa.array(
               [encode_np_array(np.array(_SCREAMING_SNAKE_CASE		)		)['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]				,       type=pa.binary()				,       )
           A_      :				str		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[Any]		     =					pa.StructArray.from_arrays(
               [bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=bytes_array.is_null()		)
         return array_cast(_SCREAMING_SNAKE_CASE				,       self.pa_type		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





         @no_op_if_value_is_null
         def path_to_bytes(_SCREAMING_SNAKE_CASE		):
           with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''		) as f:
             A_      :				Any		     =					f.read()
           return bytes_

         A_      :				Dict		     =					pa.array(
             [
                 (path_to_bytes(x['''path''']		) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                 for x in storage.to_pylist()
             ]				,       type=pa.binary()				,       )
         A_      :				List[Any]		     =					pa.array(
             [os.path.basename(_SCREAMING_SNAKE_CASE		) if path is not None else None for path in storage.field('''path'''		).to_pylist()]				,       type=pa.string()				,       )
         A_      :				str		     =					pa.StructArray.from_arrays([bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=bytes_array.is_null()		)
         return array_cast(_SCREAMING_SNAKE_CASE				,       self.pa_type		)




def   _SCREAMING_SNAKE_CASE	( ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  global _IMAGE_COMPRESSION_FORMATS
  if _IMAGE_COMPRESSION_FORMATS is None:
    PIL.Image.init()
    A_      :				Dict		     =					list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
  return _IMAGE_COMPRESSION_FORMATS


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Dict		     =					BytesIO()
  if image.format in list_image_compression_formats():
    A_      :				Tuple		     =					image.format
  else:
    A_      :				List[str]		     =					'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
  image.save(SCREAMING_SNAKE_CASE ,			format=SCREAMING_SNAKE_CASE       )
  return buffer.getvalue()


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if hasattr(SCREAMING_SNAKE_CASE ,			'''filename'''       ) and image.filename != "":
    return {"path": image.filename, "bytes": None}
  else:
    return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE       )}


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  A_      :				Union[str, Any]		     =					array.dtype
  A_      :				Dict		     =					dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
  A_      :				Any		     =					dtype.kind
  A_      :				Any		     =					dtype.itemsize

  A_      :				Dict		     =					None

  # Multi-channel array case (only np.dtype("|u1") is allowed)
  if array.shape[2:]:
    A_      :				List[Any]		     =					np.dtype('''|u1'''       )
    if dtype_kind not in ["u", "i"]:
      raise TypeError(
          f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''       )
    if dtype is not dest_dtype:
      warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''       )
    # Exact match
  elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
    A_      :				int		     =					dtype
  else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
    while dtype_itemsize >= 1:
      A_      :				Any		     =					dtype_byteorder + dtype_kind + str(SCREAMING_SNAKE_CASE       )
      A_      :				Optional[int]		     =					np.dtype(SCREAMING_SNAKE_CASE       )
      if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
        warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''       )
        break
      else:
        dtype_itemsize //= 2

  if dest_dtype is None:
    raise TypeError(
        f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''       )
  A_      :				Tuple		     =					PIL.Image.fromarray(array.astype(SCREAMING_SNAKE_CASE       )       )
  return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE       )}







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError('''To support encoding images, please install \'Pillow\'.'''       )

  if objs:
    A_     ,       A_      :				Union[str, Any]		     =					first_non_null_value(SCREAMING_SNAKE_CASE       )
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
    if isinstance(SCREAMING_SNAKE_CASE ,			np.ndarray       ):
      A_      :				Tuple		     =					no_op_if_value_is_null(SCREAMING_SNAKE_CASE       )
      return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE       ) for obj in objs]
    elif isinstance(SCREAMING_SNAKE_CASE ,			PIL.Image.Image       ):
      A_      :				List[str]		     =					no_op_if_value_is_null(SCREAMING_SNAKE_CASE       )
      return [obj_to_image_dict_func(SCREAMING_SNAKE_CASE       ) for obj in objs]
    else:
      return objs
  else:
    return objs




