'''simple docstring'''





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule




class 	__A				(			__UpperCamelCase   ):
    a__	:						BigBirdConfig
    a__	:						jnp.dtype	       =  jnp.floataa
    a__	:						bool	       =  True



    def  _lowercase						(self   :   str			):
         super().setup()
         UpperCAmelCase_	   =       nn.Dense(5       ,      dtype=self.dtype			)






    def __call__(self   :   str       ,      *__a   :   int       ,      **__a   :   List[str]			):
         UpperCAmelCase_	   =       super().__call__(*_lowerCAmelCase       ,      **_lowerCAmelCase			)
         UpperCAmelCase_	   =       self.cls(outputs[2]			)
         return outputs[:2] + (cls_out,)




class 	__A				(			__UpperCamelCase   ):
    a__	:						List[str]	       =  FlaxBigBirdForNaturalQuestionsModule





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Tuple ,   snake_case_	:			Optional[Any] ,   snake_case_	:			Optional[Any] ,   snake_case_	:			List[Any] ,   snake_case_	:			Dict      )   ->				Any:


     '''simple docstring'''




     def cross_entropy(snake_case_	:			List[str] ,   snake_case_	:			Dict ,   snake_case_	:			Optional[int]=None      ):
          UpperCAmelCase_	   =       logits.shape[-1]
          UpperCAmelCase_	   =       (labels[..., None] == jnp.arange(__SCREAMING_SNAKE_CASE      )[None]).astype("f4"      )
          UpperCAmelCase_	   =       jax.nn.log_softmax(__SCREAMING_SNAKE_CASE ,   axis=-1      )
          UpperCAmelCase_	   =       -jnp.sum(labels * logits ,   axis=-1      )
          if reduction is not None:
               UpperCAmelCase_	   =       reduction(__SCREAMING_SNAKE_CASE      )
          return loss

     UpperCAmelCase_	   =       partial(__SCREAMING_SNAKE_CASE ,   reduction=jnp.mean      )
     UpperCAmelCase_	   =       cross_entropy(__SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       cross_entropy(__SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       cross_entropy(__SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE      )
     return (start_loss + end_loss + pooled_loss) / 3




@dataclass
class 	__A				:
    a__	:						str	       =  "google/bigbird-roberta-base"
    a__	:						int	       =  3_000
    a__	:						int	       =  10_500

    a__	:						int	       =  128
    a__	:						int	       =  3

    a__	:						int	       =  1
    a__	:						int	       =  5

    # tx_args
    a__	:						float	       =  3e-5
    a__	:						float	       =  0.0
    a__	:						int	       =  20_000
    a__	:						float	       =  0.0_0_9_5

    a__	:						str	       =  "bigbird-roberta-natural-questions"
    a__	:						str	       =  "training-expt"
    a__	:						str	       =  "data/nq-training.jsonl"
    a__	:						str	       =  "data/nq-validation.jsonl"



    def  _lowercase						(self   :   Tuple			):
         os.makedirs(self.base_dir       ,      exist_ok=_lowerCAmelCase			)
         UpperCAmelCase_	   =       os.path.join(self.base_dir       ,      self.save_dir			)
         UpperCAmelCase_	   =       self.batch_size_per_device * jax.device_count()




@dataclass
class 	__A				:
    a__	:						int
    a__	:						int	       =  4_096  # no dynamic padding on TPUs



    def __call__(self   :   Tuple       ,      __a   :   int			):
         UpperCAmelCase_	   =       self.collate_fn(_lowerCAmelCase			)
         UpperCAmelCase_	   =       jax.tree_util.tree_map(_lowerCAmelCase       ,      _lowerCAmelCase			)
         return batch



    def  _lowercase						(self   :   List[str]       ,      __a   :   Tuple			):
         UpperCAmelCase_	   =       self.fetch_inputs(features["input_ids"]			)
         UpperCAmelCase_	   =       {
             """input_ids""": jnp.array(_lowerCAmelCase       ,      dtype=jnp.intaa			),
             """attention_mask""": jnp.array(_lowerCAmelCase       ,      dtype=jnp.intaa			),
             """start_labels""": jnp.array(features["start_token"]       ,      dtype=jnp.intaa			),
             """end_labels""": jnp.array(features["end_token"]       ,      dtype=jnp.intaa			),
             """pooled_labels""": jnp.array(features["category"]       ,      dtype=jnp.intaa			),
         }
         return batch



    def  _lowercase						(self   :   Any       ,      __a   :   list			):
         UpperCAmelCase_	   =       [self._fetch_inputs(_lowerCAmelCase			) for ids in input_ids]
         return zip(*_lowerCAmelCase			)






    def  _lowercase						(self   :   Optional[int]       ,      __a   :   list			):
         UpperCAmelCase_	   =       [1 for _ in range(len(_lowerCAmelCase			)			)]
         while len(_lowerCAmelCase			) < self.max_length:
              input_ids.append(self.pad_id			)
              attention_mask.append(0			)
         return input_ids, attention_mask





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			Optional[Any] ,   snake_case_	:			Union[str, Any]=None      )   ->				int:


     '''simple docstring'''




     if seed is not None:
          UpperCAmelCase_	   =       dataset.shuffle(seed=__SCREAMING_SNAKE_CASE      )
     for i in range(len(__SCREAMING_SNAKE_CASE      ) // batch_size      ):
          UpperCAmelCase_	   =       dataset[i * batch_size : (i + 1) * batch_size]
          yield dict(__SCREAMING_SNAKE_CASE      )





@partial(jax.pmap ,   axis_name="batch"      )
def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			List[str] ,   **snake_case_	:			Optional[Any]      )   ->				List[Any]:


     '''simple docstring'''




     def loss_fn(snake_case_	:			str      ):
          UpperCAmelCase_	   =       model_inputs.pop("start_labels"      )
          UpperCAmelCase_	   =       model_inputs.pop("end_labels"      )
          UpperCAmelCase_	   =       model_inputs.pop("pooled_labels"      )

          UpperCAmelCase_	   =       state.apply_fn(**__SCREAMING_SNAKE_CASE ,   params=__SCREAMING_SNAKE_CASE ,   dropout_rng=__SCREAMING_SNAKE_CASE ,   train=__SCREAMING_SNAKE_CASE      )
          UpperCAmelCase_	   =       outputs

          return state.loss_fn(
              __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   )

     UpperCAmelCase_	   =       jax.random.split(__SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       jax.value_and_grad(__SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       grad_fn(state.params      )
     UpperCAmelCase_	   =       jax.lax.pmean({"loss": loss} ,   axis_name="batch"      )
     UpperCAmelCase_	   =       jax.lax.pmean(__SCREAMING_SNAKE_CASE ,   "batch"      )

     UpperCAmelCase_	   =       state.apply_gradients(grads=__SCREAMING_SNAKE_CASE      )
     return state, metrics, new_drp_rng





@partial(jax.pmap ,   axis_name="batch"      )
def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   **snake_case_	:			Any      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       model_inputs.pop("start_labels"      )
     UpperCAmelCase_	   =       model_inputs.pop("end_labels"      )
     UpperCAmelCase_	   =       model_inputs.pop("pooled_labels"      )

     UpperCAmelCase_	   =       state.apply_fn(**__SCREAMING_SNAKE_CASE ,   params=state.params ,   train=__SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       outputs

     UpperCAmelCase_	   =       state.loss_fn(__SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       jax.lax.pmean({"loss": loss} ,   axis_name="batch"      )
     return metrics




class 	__A				(			train_state.TrainState   ):
    a__	:						Callable	       =  struct.field(pytree_node=__UpperCamelCase   )




@dataclass
class 	__A				:
    a__	:						Args
    a__	:						Callable
    a__	:						Callable
    a__	:						Callable
    a__	:						Callable
    a__	:						wandb
    a__	:						Callable	       =  None



    def  _lowercase						(self   :   str       ,      __a   :   str       ,      __a   :   int       ,      __a   :   Optional[int]       ,      __a   :   Dict=None			):
         UpperCAmelCase_	   =       model.params
         UpperCAmelCase_	   =       TrainState.create(
             apply_fn=model.__call__       ,      params=_lowerCAmelCase       ,      tx=_lowerCAmelCase       ,      loss_fn=_lowerCAmelCase       ,      )
         if ckpt_dir is not None:
              UpperCAmelCase_	   =       restore_checkpoint(_lowerCAmelCase       ,      _lowerCAmelCase			)
              UpperCAmelCase_	   =       {
                  """lr""": args.lr,
                  """init_lr""": args.init_lr,
                  """warmup_steps""": args.warmup_steps,
                  """num_train_steps""": num_train_steps,
                  """weight_decay""": args.weight_decay,
              }
              UpperCAmelCase_	   =       build_tx(**_lowerCAmelCase			)
              UpperCAmelCase_	   =       train_state.TrainState(
                  step=_lowerCAmelCase       ,      apply_fn=model.__call__       ,      params=_lowerCAmelCase       ,      tx=_lowerCAmelCase       ,      opt_state=_lowerCAmelCase       ,      )
              UpperCAmelCase_	   =       args
              UpperCAmelCase_	   =       data_collator
              UpperCAmelCase_	   =       lr
              UpperCAmelCase_	   =       params
         UpperCAmelCase_	   =       jax_utils.replicate(_lowerCAmelCase			)
         return state



    def  _lowercase						(self   :   Tuple       ,      __a   :   Union[str, Any]       ,      __a   :   Dict       ,      __a   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.args
         UpperCAmelCase_	   =       len(_lowerCAmelCase			) // args.batch_size

         UpperCAmelCase_	   =       jax.random.PRNGKey(0			)
         UpperCAmelCase_	   =       jax.random.split(_lowerCAmelCase       ,      jax.device_count()			)
         for epoch in range(args.max_epochs			):
              UpperCAmelCase_	   =       jnp.array(0       ,      dtype=jnp.floataa			)
              UpperCAmelCase_	   =       get_batched_dataset(_lowerCAmelCase       ,      args.batch_size       ,      seed=_lowerCAmelCase			)
              UpperCAmelCase_	   =       0
              for batch in tqdm(_lowerCAmelCase       ,      total=_lowerCAmelCase       ,      desc=f"""Running EPOCH-{epoch}"""			):
                   UpperCAmelCase_	   =       self.data_collator(_lowerCAmelCase			)
                   UpperCAmelCase_	   =       self.train_step_fn(_lowerCAmelCase       ,      _lowerCAmelCase       ,      **_lowerCAmelCase			)
                   running_loss += jax_utils.unreplicate(metrics["loss"]			)
                   i += 1
                   if i % args.logging_steps == 0:
                        UpperCAmelCase_	   =       jax_utils.unreplicate(state.step			)
                        UpperCAmelCase_	   =       running_loss.item() / i
                        UpperCAmelCase_	   =       self.scheduler_fn(state_step - 1			)

                        UpperCAmelCase_	   =       self.evaluate(_lowerCAmelCase       ,      _lowerCAmelCase			)
                        UpperCAmelCase_	   =       {
                            """step""": state_step.item(),
                            """eval_loss""": eval_loss.item(),
                            """tr_loss""": tr_loss,
                            """lr""": lr.item(),
                        }
                        tqdm.write(str(_lowerCAmelCase			)			)
                        self.logger.log(_lowerCAmelCase       ,      commit=_lowerCAmelCase			)

                   if i % args.save_steps == 0:
                        self.save_checkpoint(args.save_dir + f"""-e{epoch}-s{i}"""       ,      state=_lowerCAmelCase			)



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Optional[Any]       ,      __a   :   int			):
         UpperCAmelCase_	   =       get_batched_dataset(_lowerCAmelCase       ,      self.args.batch_size			)
         UpperCAmelCase_	   =       len(_lowerCAmelCase			) // self.args.batch_size
         UpperCAmelCase_	   =       jnp.array(0       ,      dtype=jnp.floataa			)
         UpperCAmelCase_	   =       0
         for batch in tqdm(_lowerCAmelCase       ,      total=_lowerCAmelCase       ,      desc="Evaluating ... "			):
              UpperCAmelCase_	   =       self.data_collator(_lowerCAmelCase			)
              UpperCAmelCase_	   =       self.val_step_fn(_lowerCAmelCase       ,      **_lowerCAmelCase			)
              running_loss += jax_utils.unreplicate(metrics["loss"]			)
              i += 1
         return running_loss / i






    def  _lowercase						(self   :   str       ,      __a   :   str       ,      __a   :   str			):
         UpperCAmelCase_	   =       jax_utils.unreplicate(_lowerCAmelCase			)
         print(f"""SAVING CHECKPOINT IN {save_dir}"""       ,      end=" ... "			)
         self.model_save_fn(_lowerCAmelCase       ,      params=state.params			)
         with open(os.path.join(_lowerCAmelCase       ,      "opt_state.msgpack"			)       ,      "wb"			) as f:
              f.write(to_bytes(state.opt_state			)			)
         joblib.dump(self.args       ,      os.path.join(_lowerCAmelCase       ,      "args.joblib"			)			)
         joblib.dump(self.data_collator       ,      os.path.join(_lowerCAmelCase       ,      "data_collator.joblib"			)			)
         with open(os.path.join(_lowerCAmelCase       ,      "training_state.json"			)       ,      "w"			) as f:
              json.dump({"step": state.step.item()}       ,      _lowerCAmelCase			)
         print("DONE"			)





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


     '''simple docstring'''




     print(f"""RESTORING CHECKPOINT FROM {save_dir}""" ,   end=" ... "      )
     with open(os.path.join(__SCREAMING_SNAKE_CASE ,   "flax_model.msgpack"      ) ,   "rb"      ) as f:
          UpperCAmelCase_	   =       from_bytes(state.params ,   f.read()      )

     with open(os.path.join(__SCREAMING_SNAKE_CASE ,   "opt_state.msgpack"      ) ,   "rb"      ) as f:
          UpperCAmelCase_	   =       from_bytes(state.opt_state ,   f.read()      )

     UpperCAmelCase_	   =       joblib.load(os.path.join(__SCREAMING_SNAKE_CASE ,   "args.joblib"      )      )
     UpperCAmelCase_	   =       joblib.load(os.path.join(__SCREAMING_SNAKE_CASE ,   "data_collator.joblib"      )      )

     with open(os.path.join(__SCREAMING_SNAKE_CASE ,   "training_state.json"      ) ,   "r"      ) as f:
          UpperCAmelCase_	   =       json.load(__SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       training_state["""step"""]

     print("DONE"      )
     return params, opt_state, step, args, data_collator





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Optional[Any] ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       num_train_steps - warmup_steps
     UpperCAmelCase_	   =       optax.linear_schedule(init_value=__SCREAMING_SNAKE_CASE ,   end_value=__SCREAMING_SNAKE_CASE ,   transition_steps=__SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       optax.linear_schedule(init_value=__SCREAMING_SNAKE_CASE ,   end_value=1E-7 ,   transition_steps=__SCREAMING_SNAKE_CASE      )
     UpperCAmelCase_	   =       optax.join_schedules(schedules=[warmup_fn, decay_fn] ,   boundaries=[warmup_steps]      )
     return lr






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


     '''simple docstring'''




     def weight_decay_mask(snake_case_	:			Dict      ):
          UpperCAmelCase_	   =       traverse_util.flatten_dict(__SCREAMING_SNAKE_CASE      )
          UpperCAmelCase_	   =       {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
          return traverse_util.unflatten_dict(__SCREAMING_SNAKE_CASE      )

     UpperCAmelCase_	   =       scheduler_fn(__SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE ,   __SCREAMING_SNAKE_CASE      )

     UpperCAmelCase_	   =       optax.adamw(learning_rate=__SCREAMING_SNAKE_CASE ,   weight_decay=__SCREAMING_SNAKE_CASE ,   mask=__SCREAMING_SNAKE_CASE      )
     return tx, lr





'''simple docstring'''





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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  ["""pixel_values"""]



    def __init__(self   :   Any       ,      __a   :   bool = True       ,      __a   :   Optional[Dict[str, int]] = None       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   bool = True       ,      __a   :   bool = True       ,      __a   :   Union[int, float] = 1 / 255       ,      __a   :   Dict[str, int] = None       ,      __a   :   bool = True       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         UpperCAmelCase_	   =       size if size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a			)
         UpperCAmelCase_	   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a       ,      param_name="crop_size"			)

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def  _lowercase						(self   :   Tuple       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BILINEAR       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if "shortest_edge" in size:
              UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
              UpperCAmelCase_	   =       (size["height"], size["width"])
         else:
              raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)
         if "height" not in size or "width" not in size:
              raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""			)
         return center_crop(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   str       ,      __a   :   np.ndarray       ,      __a   :   float       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   List[str]			):
         return rescale(__a       ,      scale=__a       ,      data_format=__a       ,      **__a			)



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






    def  _lowercase						(self   :   Dict       ,      __a   :   ImageInput       ,      __a   :   Optional[bool] = None       ,      __a   :   Dict[str, int] = None       ,      __a   :   PILImageResampling = None       ,      __a   :   bool = None       ,      __a   :   int = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[float] = None       ,      __a   :   Optional[bool] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[str, TensorType]] = None       ,      __a   :   Union[str, ChannelDimension] = ChannelDimension.FIRST       ,      **__a   :   Dict       ,      ):
         UpperCAmelCase_	   =       do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_	   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_	   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_	   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_	   =       crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_	   =       get_size_dict(__a       ,      param_name="crop_size"       ,      default_to_square=__a			)
         UpperCAmelCase_	   =       resample if resample is not None else self.resample
         UpperCAmelCase_	   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_	   =       image_std if image_std is not None else self.image_std

         UpperCAmelCase_	   =       size if size is not None else self.size
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if not is_batched(__a			):
              UpperCAmelCase_	   =       [images]

         if not valid_images(__a			):
              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_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop 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.
         UpperCAmelCase_	   =       [to_numpy_array(__a			) for image in images]

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

         if do_center_crop:
              UpperCAmelCase_	   =       [self.center_crop(image=__a       ,      size=__a			) for image in images]

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

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

         UpperCAmelCase_	   =       [to_channel_dimension_format(__a       ,      __a			) for image in images]

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)




