
"""simple docstring"""



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


UpperCamelCase__          =	logging.get_logger(__name__)
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case=False ):
    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
            ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
            ' instructions.' )
        raise

    if not is_sharded:
        UpperCAmelCase__	:      List[Any]     							=					os.path.abspath(_snake_case )
        logger.info(F'''Loading PyTorch weights from {pt_path}''' )

        UpperCAmelCase__	:      Optional[int]     							=					torch.load(_snake_case    ,map_location='cpu' )
        logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.''' )

        UpperCAmelCase__	:      int     							=					convert_pytorch_state_dict_to_flax(_snake_case    ,_snake_case )
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        UpperCAmelCase__	:      Optional[Any]     							=					convert_pytorch_sharded_state_dict_to_flax(_snake_case    ,_snake_case )
    return flax_state_dict
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,):

    def is_key_or_prefix_key_in_dict(_snake_case ) -> bool:
        return len(set(_snake_case ) & {key, (model_prefix,) + key} ) > 0

    # layer norm
    UpperCAmelCase__	:      List[Any]     							=					pt_tuple_key[:-1] + ("scale",)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_snake_case ):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    UpperCAmelCase__	:      Optional[int]     							=					pt_tuple_key[:-1] + ("mean",)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_snake_case ):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    UpperCAmelCase__	:      Tuple     							=					pt_tuple_key[:-1] + ("var",)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_snake_case ):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    UpperCAmelCase__	:      Union[str, Any]     							=					pt_tuple_key[:-1] + ("embedding",)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_snake_case ):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__	:      Union[str, Any]     							=					pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_snake_case ):
        UpperCAmelCase__	:      Dict     							=					pt_tensor.transpose(2    ,3    ,1    ,0 )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__	:      List[str]     							=					pt_tuple_key[:-1] + ("kernel",)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_snake_case ):
        UpperCAmelCase__	:      Any     							=					pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__	:      List[Any]     							=					pt_tuple_key[:-1] + ("weight",)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__	:      Tuple     							=					pt_tuple_key[:-1] + ("bias",)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    UpperCAmelCase__	:      Dict     							=					None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        UpperCAmelCase__	:      Any     							=					pt_tuple_key[-2] + "_g"
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        UpperCAmelCase__	:      Tuple     							=					pt_tuple_key[-2] + "_v"
    if name is not None:
        UpperCAmelCase__	:      str     							=					pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    # convert pytorch tensor to numpy
    UpperCAmelCase__	:      Tuple     							=					{k: v.numpy() for k, v in pt_state_dict.items()}

    UpperCAmelCase__	:      List[Any]     							=					flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        UpperCAmelCase__	:      Any     							=					flax_model.params["params"]
    else:
        UpperCAmelCase__	:      Union[str, Any]     							=					flax_model.params
    UpperCAmelCase__	:      Tuple     							=					flatten_dict(_snake_case )

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        UpperCAmelCase__	:      List[str]     							=					flatten_dict(flax_model.params['batch_stats'] )
        random_flax_state_dict.update(_snake_case )

    UpperCAmelCase__	:      Union[str, Any]     							=					{}

    UpperCAmelCase__	:      Any     							=					(model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.' )[0] for k in pt_state_dict.keys()}
    )
    UpperCAmelCase__	:      Optional[Any]     							=					(model_prefix in flax_model_params) and (
        model_prefix not in {k.split('.' )[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__	:      Union[str, Any]     							=					tuple(pt_key.split('.' ) )

        # remove base model prefix if necessary
        UpperCAmelCase__	:      int     							=					pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            UpperCAmelCase__	:      List[str]     							=					pt_tuple_key[1:]

        # Correctly rename weight parameters
        UpperCAmelCase__	:      str     							=					rename_key_and_reshape_tensor(
            _snake_case    ,_snake_case    ,_snake_case    ,_snake_case )

        # add model prefix if necessary
        UpperCAmelCase__	:      Optional[int]     							=					(model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            UpperCAmelCase__	:      Tuple     							=					(model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                UpperCAmelCase__	:      List[str]     							=					jnp.asarray(_snake_case )
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(_snake_case    ,_snake_case )
                continue

            # also add unexpected weight so that warning is thrown
            UpperCAmelCase__	:      int     							=					jnp.asarray(_snake_case )

        else:
            # also add unexpected weight so that warning is thrown
            UpperCAmelCase__	:      Optional[Any]     							=					jnp.asarray(_snake_case )

    return unflatten_dict(_snake_case )
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    import torch

    # Load the index
    UpperCAmelCase__	:      Union[str, Any]     							=					{}
    for shard_file in shard_filenames:
        # load using msgpack utils
        UpperCAmelCase__	:      Dict     							=					torch.load(_snake_case )
        UpperCAmelCase__	:      List[Any]     							=					{k: v.numpy() for k, v in pt_state_dict.items()}

        UpperCAmelCase__	:      Union[str, Any]     							=					flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            UpperCAmelCase__	:      str     							=					flax_model.params["params"]

            UpperCAmelCase__	:      List[Any]     							=					flatten_dict(_snake_case )
            random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats'] ) )
        else:
            UpperCAmelCase__	:      List[Any]     							=					flax_model.params
            UpperCAmelCase__	:      Optional[int]     							=					flatten_dict(_snake_case )

        UpperCAmelCase__	:      str     							=					(model_prefix not in flax_model_params) and (
            model_prefix in {k.split('.' )[0] for k in pt_state_dict.keys()}
        )
        UpperCAmelCase__	:      Union[str, Any]     							=					(model_prefix in flax_model_params) and (
            model_prefix not in {k.split('.' )[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            UpperCAmelCase__	:      Optional[Any]     							=					tuple(pt_key.split('.' ) )

            # remove base model prefix if necessary
            UpperCAmelCase__	:      Union[str, Any]     							=					pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                UpperCAmelCase__	:      Union[str, Any]     							=					pt_tuple_key[1:]

            # Correctly rename weight parameters
            UpperCAmelCase__	:      int     							=					rename_key_and_reshape_tensor(
                _snake_case    ,_snake_case    ,_snake_case    ,_snake_case )
            # add model prefix if necessary
            UpperCAmelCase__	:      Union[str, Any]     							=					(model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                UpperCAmelCase__	:      Dict     							=					(model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )

                # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    UpperCAmelCase__	:      str     							=					jnp.asarray(_snake_case )
                    continue
                if "var" in flax_key[-1]:
                    UpperCAmelCase__	:      List[Any]     							=					jnp.asarray(_snake_case )
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(_snake_case    ,_snake_case )
                    continue

                # also add unexpected weight so that warning is thrown
                UpperCAmelCase__	:      Dict     							=					jnp.asarray(_snake_case )

            else:
                # also add unexpected weight so that warning is thrown
                UpperCAmelCase__	:      Union[str, Any]     							=					jnp.asarray(_snake_case )
    return unflatten_dict(_snake_case )
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					os.path.abspath(_snake_case )
    logger.info(F'''Loading Flax weights from {flax_checkpoint_path}''' )

    # import correct flax class
    UpperCAmelCase__	:      int     							=					getattr(_snake_case    ,'Flax' + model.__class__.__name__ )

    # load flax weight dict
    with open(_snake_case    ,'rb' ) as state_f:
        try:
            UpperCAmelCase__	:      Any     							=					from_bytes(_snake_case    ,state_f.read() )
        except UnpicklingError:
            raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. ''' )

    return load_flax_weights_in_pytorch_model(_snake_case    ,_snake_case )

def 					lowerCamelCase   (     _snake_case    ,_snake_case ):

    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
            ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
            ' instructions.' )
        raise

    # check if we have bf16 weights
    UpperCAmelCase__	:      int     							=					flatten_dict(jax.tree_util.tree_map(lambda _snake_case : x.dtype == jnp.bfloataa    ,_snake_case ) ).values()
    if any(_snake_case ):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
            'before loading those in PyTorch model.' )
        UpperCAmelCase__	:      Optional[Any]     							=					jax.tree_util.tree_map(
            lambda _snake_case : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params    ,_snake_case )

    UpperCAmelCase__	:      List[str]     							=					flatten_dict(_snake_case )
    UpperCAmelCase__	:      Union[str, Any]     							=					pt_model.state_dict()

    UpperCAmelCase__	:      str     							=					(pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split('.' )[0] for k in pt_model_dict.keys()}
    )
    UpperCAmelCase__	:      Union[str, Any]     							=					(pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split('.' )[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    UpperCAmelCase__	:      Tuple     							=					[]
    UpperCAmelCase__	:      List[Any]     							=					set(pt_model_dict.keys() )

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        UpperCAmelCase__	:      List[str]     							=					flax_key_tuple[0] == pt_model.base_model_prefix
        UpperCAmelCase__	:      int     							=					".".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            UpperCAmelCase__	:      Union[str, Any]     							=					flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            UpperCAmelCase__	:      Optional[int]     							=					(pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_snake_case ) not in pt_model_dict:
            # conv layer
            UpperCAmelCase__	:      Optional[int]     							=					flax_key_tuple[:-1] + ("weight",)
            UpperCAmelCase__	:      Optional[int]     							=					jnp.transpose(_snake_case    ,(3, 2, 0, 1) )
        elif flax_key_tuple[-1] == "kernel" and ".".join(_snake_case ) not in pt_model_dict:
            # linear layer
            UpperCAmelCase__	:      Any     							=					flax_key_tuple[:-1] + ("weight",)
            UpperCAmelCase__	:      str     							=					flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            UpperCAmelCase__	:      Any     							=					flax_key_tuple[:-1] + ("weight",)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            UpperCAmelCase__	:      str     							=					flax_key_tuple[:-1] + ("running_mean",)
        elif "var" in flax_key_tuple[-1]:
            UpperCAmelCase__	:      int     							=					flax_key_tuple[:-1] + ("running_var",)

        if "batch_stats" in flax_state:
            UpperCAmelCase__	:      List[Any]     							=					".".join(flax_key_tuple[1:] )  # Remove the params/batch_stats header
        else:
            UpperCAmelCase__	:      List[Any]     							=					".".join(_snake_case )

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        UpperCAmelCase__	:      int     							=					{}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            UpperCAmelCase__	:      Optional[Any]     							=					key.split('.' )
            UpperCAmelCase__	:      Any     							=					None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                UpperCAmelCase__	:      Tuple     							=					key_components[-2] + "_g"
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                UpperCAmelCase__	:      Dict     							=					key_components[-2] + "_v"
            if name is not None:
                UpperCAmelCase__	:      Any     							=					key_components[:-3] + [name]
                UpperCAmelCase__	:      Union[str, Any]     							=					".".join(_snake_case )
                UpperCAmelCase__	:      int     							=					key

        if flax_key in special_pt_names:
            UpperCAmelCase__	:      List[Any]     							=					special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                    F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
            else:
                # add weight to pytorch dict
                UpperCAmelCase__	:      Dict     							=					np.asarray(_snake_case ) if not isinstance(_snake_case    ,np.ndarray ) else flax_tensor
                UpperCAmelCase__	:      Optional[int]     							=					torch.from_numpy(_snake_case )
                # remove from missing keys
                missing_keys.remove(_snake_case )
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(_snake_case )

    pt_model.load_state_dict(_snake_case )

    # re-transform missing_keys to list
    UpperCAmelCase__	:      Tuple     							=					list(_snake_case )

    if len(_snake_case ) > 0:
        logger.warning(
            'Some weights of the Flax model were not used when initializing the PyTorch model'
            F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
            F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
            ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
            F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
            ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
            ' FlaxBertForSequenceClassification model).' )
    else:
        logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n''' )
    if len(_snake_case ) > 0:
        logger.warning(
            F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
            F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
            ' use it for predictions and inference.' )
    else:
        logger.warning(
            F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
            'If your task is similar to the task the model of the checkpoint was trained on, '
            F'''you can already use {pt_model.__class__.__name__} for predictions without further training.''' )

    return pt_model








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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE__							:      List[str]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    int  		=					["""pixel_values"""]





   def __init__(			self       ,      snake_case = True       ,      snake_case = None       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = True       ,      snake_case = 1 / 255       ,      snake_case = True       ,      snake_case = None       ,      snake_case = None       ,      snake_case = True       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      super().__init__(**snake_case   )
      a__						:    List[str]   		=	size if size is not None else {"height": 384, "width": 384}
      a__						:    List[str]   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    Any   		=	do_resize
      a__						:    Optional[int]   		=	size
      a__						:    int   		=	resample
      a__						:    Optional[Any]   		=	do_rescale
      a__						:    Dict   		=	rescale_factor
      a__						:    Dict   		=	do_normalize
      a__						:    Any   		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      a__						:    List[str]   		=	image_std if image_std is not None else OPENAI_CLIP_STD
      a__						:    str   		=	do_convert_rgb





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = PILImageResampling.BICUBIC       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    Optional[int]   		=	get_size_dict(snake_case       ,      default_to_square=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()}"""   )
      a__						:    Optional[int]   		=	(size["height"], size["width"])
      return resize(snake_case       ,      size=snake_case       ,      resample=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	List[str]:
      """simple docstring"""





      return rescale(snake_case       ,      scale=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      **snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      return normalize(snake_case       ,      mean=snake_case       ,      std=snake_case       ,      data_format=snake_case       ,      **snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = ChannelDimension.FIRST       ,      **snake_case       ,      )					->	PIL.Image.Image:
      """simple docstring"""





      a__						:    List[str]   		=	do_resize if do_resize is not None else self.do_resize
      a__						:    Optional[Any]   		=	resample if resample is not None else self.resample
      a__						:    int   		=	do_rescale if do_rescale is not None else self.do_rescale
      a__						:    int   		=	rescale_factor if rescale_factor is not None else self.rescale_factor
      a__						:    Union[str, Any]   		=	do_normalize if do_normalize is not None else self.do_normalize
      a__						:    List[Any]   		=	image_mean if image_mean is not None else self.image_mean
      a__						:    int   		=	image_std if image_std is not None else self.image_std
      a__						:    Tuple   		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      a__						:    Tuple   		=	size if size is not None else self.size
      a__						:    Any   		=	get_size_dict(snake_case       ,      default_to_square=snake_case   )

      a__						:    str   		=	make_list_of_images(snake_case   )

      if not valid_images(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 or resample is None:
         raise ValueError("Size and resample 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_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."   )

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
         a__						:    List[str]   		=	[convert_to_rgb(snake_case   ) for image in images]

      # All transformations expect numpy arrays.
      a__						:    Dict   		=	[to_numpy_array(snake_case   ) for image in images]

      if do_resize:
         a__						:    Union[str, Any]   		=	[self.resize(image=snake_case       ,      size=snake_case       ,      resample=snake_case   ) for image in images]

      if do_rescale:
         a__						:    Tuple   		=	[self.rescale(image=snake_case       ,      scale=snake_case   ) for image in images]

      if do_normalize:
         a__						:    List[str]   		=	[self.normalize(image=snake_case       ,      mean=snake_case       ,      std=snake_case   ) for image in images]

      a__						:    Union[str, Any]   		=	[to_channel_dimension_format(snake_case       ,      snake_case   ) for image in images]

      a__						:    List[Any]   		=	BatchFeature(data={"pixel_values": images}       ,      tensor_type=snake_case   )

      return encoded_outputs

