




"""simple docstring"""
import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import (
        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
    )

if is_tf_available() and is_tensorflow_probability_available():
    import tensorflow as tf
    import tensorflow_probability as tfp

    from ..models.auto.modeling_tf_auto import (
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
    )







class  _SCREAMING_SNAKE_CASE       (     _lowercase				):



 '''simple docstring'''





 def __call__(self					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  requires_backends(self					,     "pandas"   )
  import pandas as pd

  if table is None:
   raise ValueError("Keyword argument `table` cannot be None."   )
  elif query is None:
   if isinstance(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE   ) and table.get("query"   ) is not None and table.get("table"   ) is not None:
    _UpperCamelCase      :	str					      =  [table]
   elif isinstance(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE   ) and len(__SCREAMING_SNAKE_CASE   ) > 0:
    if not all(isinstance(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE   ) for d in table   ):
     raise ValueError(
         F"Keyword argument `table` should be a list of dict, but is {(type(__SCREAMING_SNAKE_CASE   ) for d in table)}"   )

    if table[0].get("query"   ) is not None and table[0].get("table"   ) is not None:
     _UpperCamelCase      :	Dict					      =  table
    else:
     raise ValueError(
         "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
         F" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."   )
   elif Dataset is not None and isinstance(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE   ) or isinstance(__SCREAMING_SNAKE_CASE					,     types.GeneratorType   ):
    return table
   else:
    raise ValueError(
        "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
        F"is {type(__SCREAMING_SNAKE_CASE   )})"   )
  else:
   _UpperCamelCase      :	Optional[Any]					      =  [{"table": table, "query": query}]

  for tqa_pipeline_input in tqa_pipeline_inputs:
   if not isinstance(tqa_pipeline_input["table"]					,     pd.DataFrame   ):
    if tqa_pipeline_input["table"] is None:
     raise ValueError("Table cannot be None."   )

    _UpperCamelCase      :	int					      =  pd.DataFrame(tqa_pipeline_input["table"]   )

  return tqa_pipeline_inputs







@add_end_docstrings(_lowercase				)
class  _SCREAMING_SNAKE_CASE       (     _lowercase				):



 '''simple docstring'''





 __UpperCAmelCase		   =							"""table,query"""






 def __init__(self					,     lowerCAmelCase__=TableQuestionAnsweringArgumentHandler()					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  super().__init__(*__SCREAMING_SNAKE_CASE					,     **__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase      :	Tuple					      =  args_parser

  self.check_model_type(
      dict(
          TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
          + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()   )
      if self.framework == "tf"
      else dict(
          MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()   )   )

  _UpperCamelCase      :	Dict					      =  bool(getattr(self.model.config					,     "aggregation_labels"					,     __SCREAMING_SNAKE_CASE   )   ) and bool(
      getattr(self.model.config					,     "num_aggregation_labels"					,     __SCREAMING_SNAKE_CASE   )   )
  _UpperCamelCase      :	Optional[int]					      =  "tapas" if hasattr(self.model.config					,     "aggregation_labels"   ) else None






 def lowercase_      (self					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  return self.model(**__SCREAMING_SNAKE_CASE   )






 def lowercase_      (self					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  if self.framework == "pt":
   _UpperCamelCase      :	Any					      =  []
   _UpperCamelCase      :	Dict					      =  []
   _UpperCamelCase      :	Optional[int]					      =  None
   _UpperCamelCase      :	str					      =  inputs["input_ids"].shape[0]

   _UpperCamelCase      :	List[str]					      =  inputs["input_ids"].to(self.device   )
   _UpperCamelCase      :	Optional[Any]					      =  inputs["attention_mask"].to(self.device   )
   _UpperCamelCase      :	Tuple					      =  inputs["token_type_ids"].to(self.device   )
   _UpperCamelCase      :	Union[str, Any]					      =  None

   for index in range(__SCREAMING_SNAKE_CASE   ):
    # If sequences have already been processed, the token type IDs will be created according to the previous
    # answer.
    if prev_answers is not None:
     _UpperCamelCase      :	Optional[int]					      =  token_type_ids_example[:, 3]  # shape (seq_len,)
     _UpperCamelCase      :	List[Any]					      =  np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

     _UpperCamelCase      :	Dict					      =  token_type_ids[index]  # shape (seq_len, 7)
     for i in range(model_labels.shape[0]   ):
      _UpperCamelCase      :	Optional[int]					      =  token_type_ids_example[:, 0].tolist()[i]
      _UpperCamelCase      :	List[str]					      =  token_type_ids_example[:, 1].tolist()[i] - 1
      _UpperCamelCase      :	List[Any]					      =  token_type_ids_example[:, 2].tolist()[i] - 1

      if row_id >= 0 and col_id >= 0 and segment_id == 1:
       _UpperCamelCase      :	Optional[int]					      =  int(prev_answers[(col_id, row_id)]   )

     _UpperCamelCase      :	List[str]					      =  torch.from_numpy(__SCREAMING_SNAKE_CASE   ).type(torch.long   ).to(self.device   )

    _UpperCamelCase      :	str					      =  input_ids[index]
    _UpperCamelCase      :	List[str]					      =  attention_mask[index]  # shape (seq_len,)
    _UpperCamelCase      :	str					      =  token_type_ids[index]  # shape (seq_len, 7)
    _UpperCamelCase      :	int					      =  self.model(
        input_ids=input_ids_example.unsqueeze(0   )					,     attention_mask=attention_mask_example.unsqueeze(0   )					,     token_type_ids=token_type_ids_example.unsqueeze(0   )					,     )
    _UpperCamelCase      :	Optional[Any]					      =  outputs.logits

    if self.aggregate:
     all_aggregations.append(outputs.logits_aggregation   )

    all_logits.append(__SCREAMING_SNAKE_CASE   )

    _UpperCamelCase      :	str					      =  torch.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase      :	int					      =  dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
        dist_per_token.probs.device   )

    _UpperCamelCase      :	Any					      =  collections.defaultdict(__SCREAMING_SNAKE_CASE   )
    for i, p in enumerate(probabilities.squeeze().tolist()   ):
     _UpperCamelCase      :	List[str]					      =  token_type_ids_example[:, 0].tolist()[i]
     _UpperCamelCase      :	Dict					      =  token_type_ids_example[:, 1].tolist()[i] - 1
     _UpperCamelCase      :	Optional[int]					      =  token_type_ids_example[:, 2].tolist()[i] - 1
     if col >= 0 and row >= 0 and segment_id == 1:
      coords_to_probs[(col, row)].append(__SCREAMING_SNAKE_CASE   )

    _UpperCamelCase      :	List[str]					      =  {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

   _UpperCamelCase      :	int					      =  torch.cat(tuple(__SCREAMING_SNAKE_CASE   )					,     0   )

   return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__SCREAMING_SNAKE_CASE   )					,     0   ))
  else:
   _UpperCamelCase      :	Tuple					      =  []
   _UpperCamelCase      :	List[Any]					      =  []
   _UpperCamelCase      :	List[str]					      =  None
   _UpperCamelCase      :	Optional[int]					      =  inputs["input_ids"].shape[0]

   _UpperCamelCase      :	Union[str, Any]					      =  inputs["input_ids"]
   _UpperCamelCase      :	List[Any]					      =  inputs["attention_mask"]
   _UpperCamelCase      :	Union[str, Any]					      =  inputs["token_type_ids"].numpy()
   _UpperCamelCase      :	List[Any]					      =  None

   for index in range(__SCREAMING_SNAKE_CASE   ):
    # If sequences have already been processed, the token type IDs will be created according to the previous
    # answer.
    if prev_answers is not None:
     _UpperCamelCase      :	List[Any]					      =  token_type_ids_example[:, 3]  # shape (seq_len,)
     _UpperCamelCase      :	Dict					      =  np.zeros_like(__SCREAMING_SNAKE_CASE					,     dtype=np.intaa   )  # shape (seq_len,)

     _UpperCamelCase      :	List[str]					      =  token_type_ids[index]  # shape (seq_len, 7)
     for i in range(model_labels.shape[0]   ):
      _UpperCamelCase      :	Optional[Any]					      =  token_type_ids_example[:, 0].tolist()[i]
      _UpperCamelCase      :	Any					      =  token_type_ids_example[:, 1].tolist()[i] - 1
      _UpperCamelCase      :	int					      =  token_type_ids_example[:, 2].tolist()[i] - 1

      if row_id >= 0 and col_id >= 0 and segment_id == 1:
       _UpperCamelCase      :	Any					      =  int(prev_answers[(col_id, row_id)]   )

     _UpperCamelCase      :	Dict					      =  model_labels

    _UpperCamelCase      :	Optional[Any]					      =  input_ids[index]
    _UpperCamelCase      :	List[str]					      =  attention_mask[index]  # shape (seq_len,)
    _UpperCamelCase      :	Optional[Any]					      =  token_type_ids[index]  # shape (seq_len, 7)
    _UpperCamelCase      :	List[str]					      =  self.model(
        input_ids=np.expand_dims(__SCREAMING_SNAKE_CASE					,     axis=0   )					,     attention_mask=np.expand_dims(__SCREAMING_SNAKE_CASE					,     axis=0   )					,     token_type_ids=np.expand_dims(__SCREAMING_SNAKE_CASE					,     axis=0   )					,     )
    _UpperCamelCase      :	Tuple					      =  outputs.logits

    if self.aggregate:
     all_aggregations.append(outputs.logits_aggregation   )

    all_logits.append(__SCREAMING_SNAKE_CASE   )

    _UpperCamelCase      :	Tuple					      =  tfp.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase      :	Any					      =  dist_per_token.probs_parameter() * tf.cast(__SCREAMING_SNAKE_CASE					,     tf.floataa   )

    _UpperCamelCase      :	str					      =  collections.defaultdict(__SCREAMING_SNAKE_CASE   )
    _UpperCamelCase      :	Dict					      =  token_type_ids_example
    for i, p in enumerate(tf.squeeze(__SCREAMING_SNAKE_CASE   ).numpy().tolist()   ):
     _UpperCamelCase      :	Optional[Any]					      =  token_type_ids_example[:, 0].tolist()[i]
     _UpperCamelCase      :	str					      =  token_type_ids_example[:, 1].tolist()[i] - 1
     _UpperCamelCase      :	Dict					      =  token_type_ids_example[:, 2].tolist()[i] - 1
     if col >= 0 and row >= 0 and segment_id == 1:
      coords_to_probs[(col, row)].append(__SCREAMING_SNAKE_CASE   )

    _UpperCamelCase      :	str					      =  {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

   _UpperCamelCase      :	Any					      =  tf.concat(tuple(__SCREAMING_SNAKE_CASE   )					,     0   )

   return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__SCREAMING_SNAKE_CASE   )					,     0   ))






 def __call__(self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  self._args_parser(*__SCREAMING_SNAKE_CASE					,     **__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase      :	Any					      =  super().__call__(__SCREAMING_SNAKE_CASE					,     **__SCREAMING_SNAKE_CASE   )
  if len(__SCREAMING_SNAKE_CASE   ) == 1:
   return results[0]
  return results






 def lowercase_      (self					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  {}
  if padding is not None:
   _UpperCamelCase      :	List[str]					      =  padding
  if truncation is not None:
   _UpperCamelCase      :	str					      =  truncation

  _UpperCamelCase      :	List[str]					      =  {}
  if sequential is not None:
   _UpperCamelCase      :	Dict					      =  sequential
  return preprocess_params, forward_params, {}






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=True					,     lowerCAmelCase__=None   ):

  '''simple docstring'''







  if truncation is None:
   if self.type == "tapas":
    _UpperCamelCase      :	Tuple					      =  "drop_rows_to_fit"
   else:
    _UpperCamelCase      :	Tuple					      =  "do_not_truncate"

  _UpperCamelCase      ,	_UpperCamelCase      :	List[Any]					      =  pipeline_input["table"], pipeline_input["query"]
  if table.empty:
   raise ValueError("table is empty"   )
  if query is None or query == "":
   raise ValueError("query is empty"   )
  _UpperCamelCase      :	Dict					      =  self.tokenizer(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     return_tensors=self.framework					,     truncation=__SCREAMING_SNAKE_CASE					,     padding=__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase      :	Optional[Any]					      =  table
  return inputs






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__=False   ):

  '''simple docstring'''







  _UpperCamelCase      :	Any					      =  model_inputs.pop("table"   )

  if self.type == "tapas":
   if sequential:
    _UpperCamelCase      :	List[Any]					      =  self.sequential_inference(**__SCREAMING_SNAKE_CASE   )
   else:
    _UpperCamelCase      :	Any					      =  self.batch_inference(**__SCREAMING_SNAKE_CASE   )
  else:
   _UpperCamelCase      :	int					      =  self.model.generate(**__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase      :	int					      =  {"model_inputs": model_inputs, "table": table, "outputs": outputs}
  return model_outputs






 def lowercase_      (self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  model_outputs["model_inputs"]
  _UpperCamelCase      :	Union[str, Any]					      =  model_outputs["table"]
  _UpperCamelCase      :	List[Any]					      =  model_outputs["outputs"]
  if self.type == "tapas":
   if self.aggregate:
    _UpperCamelCase      ,	_UpperCamelCase      :	List[str]					      =  outputs[:2]
    _UpperCamelCase      :	Tuple					      =  self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase      ,	_UpperCamelCase      :	str					      =  predictions
    _UpperCamelCase      :	int					      =  {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__SCREAMING_SNAKE_CASE   )}

    _UpperCamelCase      :	Dict					      =  self.model.config.no_aggregation_label_index
    _UpperCamelCase      :	Union[str, Any]					      =  {
        i: aggregators[i] + " > " for i, pred in enumerate(__SCREAMING_SNAKE_CASE   ) if pred != no_agg_label_index
    }
   else:
    _UpperCamelCase      :	Tuple					      =  outputs[0]
    _UpperCamelCase      :	Any					      =  self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase      :	List[Any]					      =  predictions[0]
    _UpperCamelCase      :	List[str]					      =  {}
    _UpperCamelCase      :	Dict					      =  {}
   _UpperCamelCase      :	Dict					      =  []
   for index, coordinates in enumerate(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase      :	List[str]					      =  [table.iat[coordinate] for coordinate in coordinates]
    _UpperCamelCase      :	int					      =  aggregators.get(__SCREAMING_SNAKE_CASE					,     ""   )
    _UpperCamelCase      :	Union[str, Any]					      =  aggregators_prefix.get(__SCREAMING_SNAKE_CASE					,     ""   )
    _UpperCamelCase      :	int					      =  {
        "answer": aggregator_prefix + ", ".join(__SCREAMING_SNAKE_CASE   ),
        "coordinates": coordinates,
        "cells": [table.iat[coordinate] for coordinate in coordinates],
    }
    if aggregator:
     _UpperCamelCase      :	Optional[int]					      =  aggregator

    answers.append(__SCREAMING_SNAKE_CASE   )
   if len(__SCREAMING_SNAKE_CASE   ) == 0:
    raise PipelineException("Empty answer"   )
  else:
   _UpperCamelCase      :	Optional[int]					      =  [{"answer": answer} for answer in self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE					,     skip_special_tokens=__SCREAMING_SNAKE_CASE   )]

  return answers if len(__SCREAMING_SNAKE_CASE   ) > 1 else answers[0]




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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



		def __init__(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Dict[str, int]] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Union[int, float] = 1 / 255	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       **__SCREAMING_SNAKE_CASE     :					str	,       ):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}' )
					return resize(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Union[float, List[float]]	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Optional[Any]	,       ):






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					ImageInput	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int] = None	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[float] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[bool] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[float, List[float]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, TensorType]] = None	,       __SCREAMING_SNAKE_CASE     :					Union[str, ChannelDimension] = ChannelDimension.FIRST	,       **__SCREAMING_SNAKE_CASE     :					List[Any]	,       ):






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

					_snake_case														=   make_list_of_images(__SCREAMING_SNAKE_CASE )

					if not valid_images(__SCREAMING_SNAKE_CASE ):
								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." )

					# All transformations expect numpy arrays.
					_snake_case														=   [to_numpy_array(__SCREAMING_SNAKE_CASE ) for image in images]

					if do_resize:
								_snake_case														=   [self.resize(image=__SCREAMING_SNAKE_CASE	,       size=__SCREAMING_SNAKE_CASE	,       resample=__SCREAMING_SNAKE_CASE ) for image in images]

					if do_rescale:
								_snake_case														=   [self.rescale(image=__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE ) for image in images]

					if do_normalize:
								_snake_case														=   [self.normalize(image=__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

