

"""simple docstring"""
import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp







def       UpperCamelCase_					(     lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Tuple     , lowerCamelCase							:					int     , lowerCamelCase							:					Dict     , lowerCamelCase							:					int = 4096   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__       ,							__magic_name__			:		Optional[Any]        =     key.shape[-3:]
       __magic_name__			:		Optional[Any]        =     value.shape[-1]
       __magic_name__			:		Optional[int]        =     min(lowerCamelCase     , lowerCamelCase   )
       __magic_name__			:		int        =     query / jnp.sqrt(lowerCamelCase   )

       @functools.partial(jax.checkpoint     , prevent_cse=lowerCamelCase   )
       def summarize_chunk(lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[int]   ):
              __magic_name__			:		Tuple        =     jnp.einsum('''...qhd,...khd->...qhk'''     , lowerCamelCase     , lowerCamelCase     , precision=lowerCamelCase   )

              __magic_name__			:		Optional[int]        =     jnp.max(lowerCamelCase     , axis=-1     , keepdims=lowerCamelCase   )
              __magic_name__			:		Optional[Any]        =     jax.lax.stop_gradient(lowerCamelCase   )
              __magic_name__			:		Optional[Any]        =     jnp.exp(attn_weights - max_score   )

              __magic_name__			:		Any        =     jnp.einsum('''...vhf,...qhv->...qhf'''     , lowerCamelCase     , lowerCamelCase     , precision=lowerCamelCase   )
              __magic_name__			:		str        =     jnp.einsum('''...qhk->...qh'''     , lowerCamelCase   )

              return (exp_values, exp_weights.sum(axis=-1   ), max_score)

       def chunk_scanner(lowerCamelCase							:					Optional[Any]   ):
              # julienne key array
              __magic_name__			:		Union[str, Any]        =     jax.lax.dynamic_slice(
                  operand=lowerCamelCase     , start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]     , slice_sizes=list(key.shape[:-3]   ) + [key_chunk_size, num_heads, k_features]     , )

              # julienne value array
              __magic_name__			:		Dict        =     jax.lax.dynamic_slice(
                  operand=lowerCamelCase     , start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]     , slice_sizes=list(value.shape[:-3]   ) + [key_chunk_size, num_heads, v_features]     , )

              return summarize_chunk(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       __magic_name__       ,							__magic_name__       ,							__magic_name__			:		str        =     jax.lax.map(f=lowerCamelCase     , xs=jnp.arange(0     , lowerCamelCase     , lowerCamelCase   )   )

       __magic_name__			:		int        =     jnp.max(lowerCamelCase     , axis=0     , keepdims=lowerCamelCase   )
       __magic_name__			:		Optional[Any]        =     jnp.exp(chunk_max - global_max   )

       chunk_values *= jnp.expand_dims(lowerCamelCase     , axis=-1   )
       chunk_weights *= max_diffs

       __magic_name__			:		Union[str, Any]        =     chunk_values.sum(axis=0   )
       __magic_name__			:		Optional[int]        =     jnp.expand_dims(lowerCamelCase     , -1   ).sum(axis=0   )

       return all_values / all_weights







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					int     , lowerCamelCase							:					List[Any]     , lowerCamelCase							:					Optional[Any]=jax.lax.Precision.HIGHEST     , lowerCamelCase							:					int = 1024     , lowerCamelCase							:					int = 4096   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__       ,							__magic_name__			:		List[Any]        =     query.shape[-3:]

       def chunk_scanner(lowerCamelCase							:					Dict     , lowerCamelCase							:					Tuple   ):
              # julienne query array
              __magic_name__			:		Any        =     jax.lax.dynamic_slice(
                  operand=lowerCamelCase     , start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]     , slice_sizes=list(query.shape[:-3]   ) + [min(lowerCamelCase     , lowerCamelCase   ), num_heads, q_features]     , )

              return (
                  chunk_idx + query_chunk_size,  # unused ignore it
                  _query_chunk_attention(
                      query=lowerCamelCase     , key=lowerCamelCase     , value=lowerCamelCase     , precision=lowerCamelCase     , key_chunk_size=lowerCamelCase   ),
              )

       __magic_name__       ,							__magic_name__			:		Optional[int]        =     jax.lax.scan(
           f=lowerCamelCase     , init=0     , xs=lowerCamelCase     , length=math.ceil(num_q / query_chunk_size   )   )

       return jnp.concatenate(lowerCamelCase     , axis=-3   )  # fuse the chunked result back

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  snake_case_        =    42
  snake_case_        =    8
  snake_case_        =    6_4
  snake_case_        =    0.0
  snake_case_        =    False
  snake_case_        =    jnp.floataa



  def 							_UpperCAmelCase      ( self					: Union[str, Any]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     self.dim_head * self.heads
         __magic_name__			:		Any        =     self.dim_head**-0.5

         # Weights were exported with old names {to_q, to_k, to_v, to_out}
         __magic_name__			:		List[str]        =     nn.Dense(snake_case       ,       use_bias=snake_case       ,       dtype=self.dtype       ,       name='''to_q'''      )
         __magic_name__			:		Tuple        =     nn.Dense(snake_case       ,       use_bias=snake_case       ,       dtype=self.dtype       ,       name='''to_k'''      )
         __magic_name__			:		Optional[int]        =     nn.Dense(snake_case       ,       use_bias=snake_case       ,       dtype=self.dtype       ,       name='''to_v'''      )

         __magic_name__			:		str        =     nn.Dense(self.query_dim       ,       dtype=self.dtype       ,       name='''to_out_0'''      )
         __magic_name__			:		Any        =     nn.Dropout(rate=self.dropout      )



  def 							_UpperCAmelCase      ( self					: Tuple       ,       snake_case					: Dict      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__       ,							__magic_name__			:		List[str]        =     tensor.shape
         __magic_name__			:		int        =     self.heads
         __magic_name__			:		Dict        =     tensor.reshape(snake_case       ,       snake_case       ,       snake_case       ,       dim // head_size      )
         __magic_name__			:		Optional[Any]        =     jnp.transpose(snake_case       ,       (0, 2, 1, 3)      )
         __magic_name__			:		List[Any]        =     tensor.reshape(batch_size * head_size       ,       snake_case       ,       dim // head_size      )
         return tensor



  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: Tuple      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__       ,							__magic_name__			:		str        =     tensor.shape
         __magic_name__			:		Optional[Any]        =     self.heads
         __magic_name__			:		Optional[int]        =     tensor.reshape(batch_size // head_size       ,       snake_case       ,       snake_case       ,       snake_case      )
         __magic_name__			:		int        =     jnp.transpose(snake_case       ,       (0, 2, 1, 3)      )
         __magic_name__			:		Optional[int]        =     tensor.reshape(batch_size // head_size       ,       snake_case       ,       dim * head_size      )
         return tensor




  def __call__( self					: str       ,       snake_case					: Dict       ,       snake_case					: List[str]=None       ,       snake_case					: int=True      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     hidden_states if context is None else context

         __magic_name__			:		Optional[int]        =     self.query(snake_case      )
         __magic_name__			:		int        =     self.key(snake_case      )
         __magic_name__			:		int        =     self.value(snake_case      )

         __magic_name__			:		List[str]        =     self.reshape_heads_to_batch_dim(snake_case      )
         __magic_name__			:		Optional[int]        =     self.reshape_heads_to_batch_dim(snake_case      )
         __magic_name__			:		Optional[Any]        =     self.reshape_heads_to_batch_dim(snake_case      )

         if self.use_memory_efficient_attention:
                __magic_name__			:		str        =     query_states.transpose(1       ,       0       ,       2      )
                __magic_name__			:		List[Any]        =     key_states.transpose(1       ,       0       ,       2      )
                __magic_name__			:		Optional[Any]        =     value_states.transpose(1       ,       0       ,       2      )

                # this if statement create a chunk size for each layer of the unet
                # the chunk size is equal to the query_length dimension of the deepest layer of the unet

                __magic_name__			:		List[Any]        =     query_states.shape[-3]
                if flatten_latent_dim % 64 == 0:
                       __magic_name__			:		Optional[int]        =     int(flatten_latent_dim / 64      )
                elif flatten_latent_dim % 16 == 0:
                       __magic_name__			:		Union[str, Any]        =     int(flatten_latent_dim / 16      )
                elif flatten_latent_dim % 4 == 0:
                       __magic_name__			:		Tuple        =     int(flatten_latent_dim / 4      )
                else:
                       __magic_name__			:		int        =     int(snake_case      )

                __magic_name__			:		Optional[int]        =     jax_memory_efficient_attention(
                    snake_case       ,       snake_case       ,       snake_case       ,       query_chunk_size=snake_case       ,       key_chunk_size=4096 * 4      )

                __magic_name__			:		int        =     hidden_states.transpose(1       ,       0       ,       2      )
         else:
                # compute attentions
                __magic_name__			:		Dict        =     jnp.einsum('''b i d, b j d->b i j'''       ,       snake_case       ,       snake_case      )
                __magic_name__			:		Dict        =     attention_scores * self.scale
                __magic_name__			:		Optional[Any]        =     nn.softmax(snake_case       ,       axis=2      )

                # attend to values
                __magic_name__			:		Union[str, Any]        =     jnp.einsum('''b i j, b j d -> b i d'''       ,       snake_case       ,       snake_case      )

         __magic_name__			:		Any        =     self.reshape_batch_dim_to_heads(snake_case      )
         __magic_name__			:		str        =     self.proj_attn(snake_case      )
         return self.dropout_layer(snake_case       ,       deterministic=snake_case      )

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  snake_case_        =    42
  snake_case_        =    42
  snake_case_        =    42
  snake_case_        =    0.0
  snake_case_        =    False
  snake_case_        =    jnp.floataa
  snake_case_        =    False



  def 							_UpperCAmelCase      ( self					: List[str]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     FlaxAttention(
             self.dim       ,       self.n_heads       ,       self.d_head       ,       self.dropout       ,       self.use_memory_efficient_attention       ,       dtype=self.dtype      )
         # cross attention
         __magic_name__			:		str        =     FlaxAttention(
             self.dim       ,       self.n_heads       ,       self.d_head       ,       self.dropout       ,       self.use_memory_efficient_attention       ,       dtype=self.dtype      )
         __magic_name__			:		Optional[int]        =     FlaxFeedForward(dim=self.dim       ,       dropout=self.dropout       ,       dtype=self.dtype      )
         __magic_name__			:		Union[str, Any]        =     nn.LayerNorm(epsilon=1e-5       ,       dtype=self.dtype      )
         __magic_name__			:		Optional[int]        =     nn.LayerNorm(epsilon=1e-5       ,       dtype=self.dtype      )
         __magic_name__			:		Optional[int]        =     nn.LayerNorm(epsilon=1e-5       ,       dtype=self.dtype      )
         __magic_name__			:		Tuple        =     nn.Dropout(rate=self.dropout      )




  def __call__( self					: int       ,       snake_case					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: Optional[Any]=True      )	->			Any:


         '''simple docstring'''





         __magic_name__			:		str        =     hidden_states
         if self.only_cross_attention:
                __magic_name__			:		str        =     self.attna(self.norma(snake_case      )       ,       snake_case       ,       deterministic=snake_case      )
         else:
                __magic_name__			:		Any        =     self.attna(self.norma(snake_case      )       ,       deterministic=snake_case      )
         __magic_name__			:		List[str]        =     hidden_states + residual

         # cross attention
         __magic_name__			:		int        =     hidden_states
         __magic_name__			:		int        =     self.attna(self.norma(snake_case      )       ,       snake_case       ,       deterministic=snake_case      )
         __magic_name__			:		int        =     hidden_states + residual

         # feed forward
         __magic_name__			:		List[Any]        =     hidden_states
         __magic_name__			:		List[Any]        =     self.ff(self.norma(snake_case      )       ,       deterministic=snake_case      )
         __magic_name__			:		Dict        =     hidden_states + residual

         return self.dropout_layer(snake_case       ,       deterministic=snake_case      )

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  snake_case_        =    42
  snake_case_        =    42
  snake_case_        =    42
  snake_case_        =    1
  snake_case_        =    0.0
  snake_case_        =    False
  snake_case_        =    False
  snake_case_        =    jnp.floataa
  snake_case_        =    False



  def 							_UpperCAmelCase      ( self					: Optional[int]      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     nn.GroupNorm(num_groups=32       ,       epsilon=1e-5      )

         __magic_name__			:		Any        =     self.n_heads * self.d_head
         if self.use_linear_projection:
                __magic_name__			:		Dict        =     nn.Dense(snake_case       ,       dtype=self.dtype      )
         else:
                __magic_name__			:		Optional[Any]        =     nn.Conv(
                    snake_case       ,       kernel_size=(1, 1)       ,       strides=(1, 1)       ,       padding='''VALID'''       ,       dtype=self.dtype       ,       )

         __magic_name__			:		Dict        =     [
             FlaxBasicTransformerBlock(
                 snake_case       ,       self.n_heads       ,       self.d_head       ,       dropout=self.dropout       ,       only_cross_attention=self.only_cross_attention       ,       dtype=self.dtype       ,       use_memory_efficient_attention=self.use_memory_efficient_attention       ,       )
             for _ in range(self.depth      )
         ]

         if self.use_linear_projection:
                __magic_name__			:		List[Any]        =     nn.Dense(snake_case       ,       dtype=self.dtype      )
         else:
                __magic_name__			:		int        =     nn.Conv(
                    snake_case       ,       kernel_size=(1, 1)       ,       strides=(1, 1)       ,       padding='''VALID'''       ,       dtype=self.dtype       ,       )

         __magic_name__			:		Optional[int]        =     nn.Dropout(rate=self.dropout      )




  def __call__( self					: Optional[Any]       ,       snake_case					: Any       ,       snake_case					: Optional[Any]       ,       snake_case					: List[Any]=True      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__       ,							__magic_name__       ,							__magic_name__       ,							__magic_name__			:		Optional[Any]        =     hidden_states.shape
         __magic_name__			:		Dict        =     hidden_states
         __magic_name__			:		Union[str, Any]        =     self.norm(snake_case      )
         if self.use_linear_projection:
                __magic_name__			:		Any        =     hidden_states.reshape(snake_case       ,       height * width       ,       snake_case      )
                __magic_name__			:		Tuple        =     self.proj_in(snake_case      )
         else:
                __magic_name__			:		Dict        =     self.proj_in(snake_case      )
                __magic_name__			:		Optional[Any]        =     hidden_states.reshape(snake_case       ,       height * width       ,       snake_case      )

         for transformer_block in self.transformer_blocks:
                __magic_name__			:		Any        =     transformer_block(snake_case       ,       snake_case       ,       deterministic=snake_case      )

         if self.use_linear_projection:
                __magic_name__			:		Optional[Any]        =     self.proj_out(snake_case      )
                __magic_name__			:		Any        =     hidden_states.reshape(snake_case       ,       snake_case       ,       snake_case       ,       snake_case      )
         else:
                __magic_name__			:		Tuple        =     hidden_states.reshape(snake_case       ,       snake_case       ,       snake_case       ,       snake_case      )
                __magic_name__			:		Dict        =     self.proj_out(snake_case      )

         __magic_name__			:		List[Any]        =     hidden_states + residual
         return self.dropout_layer(snake_case       ,       deterministic=snake_case      )

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  snake_case_        =    42
  snake_case_        =    0.0
  snake_case_        =    jnp.floataa



  def 							_UpperCAmelCase      ( self					: List[Any]      )	->			int:


         '''simple docstring'''





         __magic_name__			:		str        =     FlaxGEGLU(self.dim       ,       self.dropout       ,       self.dtype      )
         __magic_name__			:		str        =     nn.Dense(self.dim       ,       dtype=self.dtype      )




  def __call__( self					: Any       ,       snake_case					: Dict       ,       snake_case					: Tuple=True      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     self.net_a(snake_case       ,       deterministic=snake_case      )
         __magic_name__			:		List[str]        =     self.net_a(snake_case      )
         return hidden_states

class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  snake_case_        =    42
  snake_case_        =    0.0
  snake_case_        =    jnp.floataa



  def 							_UpperCAmelCase      ( self					: Dict      )	->			str:


         '''simple docstring'''





         __magic_name__			:		Tuple        =     self.dim * 4
         __magic_name__			:		List[str]        =     nn.Dense(inner_dim * 2       ,       dtype=self.dtype      )
         __magic_name__			:		List[str]        =     nn.Dropout(rate=self.dropout      )




  def __call__( self					: str       ,       snake_case					: Union[str, Any]       ,       snake_case					: Any=True      )	->			Optional[Any]:


         '''simple docstring'''





         __magic_name__			:		int        =     self.proj(snake_case      )
         __magic_name__       ,							__magic_name__			:		List[str]        =     jnp.split(snake_case       ,       2       ,       axis=2      )
         return self.dropout_layer(hidden_linear * nn.gelu(snake_case      )       ,       deterministic=snake_case      )



"""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, 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, is_vision_available, logging


if is_vision_available():
 import PIL


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: bool = False       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Union[int, float] = 1 / 255       ,       snake_case					: bool = False       ,       snake_case					: bool = True       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = True       ,       **snake_case					: Optional[Any]       ,       )	->			None:


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Any        =     size if size is not None else {'''height''': 346, '''width''': 346}
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		List[str]        =     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Any        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		List[Any]        =     resample
         __magic_name__			:		Tuple        =     do_center_crop
         __magic_name__			:		str        =     crop_size
         __magic_name__			:		Tuple        =     do_rescale
         __magic_name__			:		Optional[Any]        =     rescale_factor
         __magic_name__			:		Optional[Any]        =     rescale_offset
         __magic_name__			:		int        =     do_normalize
         __magic_name__			:		str        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __magic_name__			:		Optional[int]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD
         __magic_name__			:		Optional[int]        =     include_top



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Union[str, Any]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return resize(
             snake_case       ,       size=(size['''height'''], size['''width'''])       ,       resample=snake_case       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: str       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return center_crop(snake_case       ,       size=(size['''height'''], size['''width'''])       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         if offset:
                __magic_name__			:		List[str]        =     (image - 127.5) * scale
                if data_format is not None:
                       __magic_name__			:		str        =     to_channel_dimension_format(snake_case       ,       snake_case      )
                __magic_name__			:		Optional[int]        =     rescaled_image.astype(np.floataa      )
         else:
                __magic_name__			:		str        =     rescale(snake_case       ,       scale=snake_case       ,       data_format=snake_case       ,       **snake_case      )
         return rescaled_image



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: np.ndarray       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: ImageInput       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Any=None       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: bool = None       ,       snake_case					: float = None       ,       snake_case					: bool = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: List[str]       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Any        =     do_resize if do_resize is not None else self.do_resize
         __magic_name__			:		Optional[Any]        =     resample if resample is not None else self.resample
         __magic_name__			:		Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
         __magic_name__			:		Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
         __magic_name__			:		Tuple        =     rescale_factor if rescale_factor is not None else self.rescale_factor
         __magic_name__			:		Dict        =     rescale_offset if rescale_offset is not None else self.rescale_offset
         __magic_name__			:		Union[str, Any]        =     do_normalize if do_normalize is not None else self.do_normalize
         __magic_name__			:		Tuple        =     image_mean if image_mean is not None else self.image_mean
         __magic_name__			:		Dict        =     image_std if image_std is not None else self.image_std
         __magic_name__			:		Union[str, Any]        =     include_top if include_top is not None else self.include_top

         __magic_name__			:		Any        =     size if size is not None else self.size
         __magic_name__			:		List[str]        =     get_size_dict(snake_case      )
         __magic_name__			:		List[Any]        =     crop_size if crop_size is not None else self.crop_size
         __magic_name__			:		Dict        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Optional[Any]        =     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_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.'''      )

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

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

         if do_resize:
                __magic_name__			:		Optional[int]        =     [self.resize(image=snake_case       ,       size=snake_case       ,       resample=snake_case      ) for image in images]

         if do_center_crop:
                __magic_name__			:		Dict        =     [self.center_crop(image=snake_case       ,       size=snake_case      ) for image in images]

         if do_rescale:
                __magic_name__			:		Any        =     [self.rescale(image=snake_case       ,       scale=snake_case       ,       offset=snake_case      ) for image in images]

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

         if include_top:
                __magic_name__			:		Optional[Any]        =     [self.normalize(image=snake_case       ,       mean=[0, 0, 0]       ,       std=snake_case      ) for image in images]

         __magic_name__			:		Any        =     [to_channel_dimension_format(snake_case       ,       snake_case      ) for image in images]

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )
