



'''simple docstring'''





import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  torch.exp(_lowercase)
				lowerCAmelCase__  :       List[str]				=  torch.sum(_lowercase ,dim=1)  # sum of exp(x_i)
				lowerCAmelCase__  :       str				=  torch.sum(x * exp_x ,dim=1)  # sum of x_i * exp(x_i)
				return torch.log(_lowercase) - B / A



class  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       int				=  config.output_attentions
						lowerCAmelCase__  :       int				=  config.output_hidden_states
						lowerCAmelCase__  :       Union[str, Any]				=  nn.ModuleList([BertLayer(UpperCamelCase_				) for _ in range(config.num_hidden_layers				)]				)
						lowerCAmelCase__  :       List[Any]				=  nn.ModuleList([BertHighway(UpperCamelCase_				) for _ in range(config.num_hidden_layers				)]				)

						lowerCAmelCase__  :       Tuple				=  [-1 for _ in range(config.num_hidden_layers				)]



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						if (type(UpperCamelCase_				) is float) or (type(UpperCamelCase_				) is int):
										for i in range(len(self.early_exit_entropy				)				):
														lowerCAmelCase__  :       Optional[Any]				=  x
						else:
										lowerCAmelCase__  :       Optional[int]				=  x



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  pooler.state_dict()
						for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
														param.copy_(loaded_model[name]				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  ()
						lowerCAmelCase__  :       List[Any]				=  ()
						lowerCAmelCase__  :       Tuple				=  ()
						for i, layer_module in enumerate(self.layer				):
										if self.output_hidden_states:
														lowerCAmelCase__  :       Optional[int]				=  all_hidden_states + (hidden_states,)

										lowerCAmelCase__  :       str				=  layer_module(
										    UpperCamelCase_	,UpperCamelCase_	,head_mask[i]	,UpperCamelCase_	,UpperCamelCase_				)
										lowerCAmelCase__  :       List[str]				=  layer_outputs[0]

										if self.output_attentions:
														lowerCAmelCase__  :       Tuple				=  all_attentions + (layer_outputs[1],)

										lowerCAmelCase__  :       Optional[int]				=  (hidden_states,)
										if self.output_hidden_states:
														lowerCAmelCase__  :       str				=  current_outputs + (all_hidden_states,)
										if self.output_attentions:
														lowerCAmelCase__  :       Optional[int]				=  current_outputs + (all_attentions,)

										lowerCAmelCase__  :       List[Any]				=  self.highway[i](UpperCamelCase_				)
										# logits, pooled_output

										if not self.training:
														lowerCAmelCase__  :       Dict				=  highway_exit[0]
														lowerCAmelCase__  :       Tuple				=  entropy(UpperCamelCase_				)
														lowerCAmelCase__  :       Dict				=  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
														lowerCAmelCase__  :       str				=  all_highway_exits + (highway_exit,)

														if highway_entropy < self.early_exit_entropy[i]:
																		lowerCAmelCase__  :       Tuple				=  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																		raise HighwayException(UpperCamelCase_	,i + 1				)
										else:
														lowerCAmelCase__  :       Optional[int]				=  all_highway_exits + (highway_exit,)

        # Add last layer
						if self.output_hidden_states:
										lowerCAmelCase__  :       str				=  all_hidden_states + (hidden_states,)

						lowerCAmelCase__  :       Optional[Any]				=  (hidden_states,)
						if self.output_hidden_states:
										lowerCAmelCase__  :       Dict				=  outputs + (all_hidden_states,)
						if self.output_attentions:
										lowerCAmelCase__  :       Optional[Any]				=  outputs + (all_attentions,)

						lowerCAmelCase__  :       Optional[int]				=  outputs + (all_highway_exits,)
						return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits



@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """   ,       lowerCamelCase__   ,       )
class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						super().__init__(UpperCamelCase_				)
						lowerCAmelCase__  :       int				=  config

						lowerCAmelCase__  :       int				=  BertEmbeddings(UpperCamelCase_				)
						lowerCAmelCase__  :       List[Any]				=  DeeBertEncoder(UpperCamelCase_				)
						lowerCAmelCase__  :       Any				=  BertPooler(UpperCamelCase_				)

						self.init_weights()



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

						"""simple docstring"""






						self.encoder.init_highway_pooler(self.pooler				)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						return self.embeddings.word_embeddings



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  value



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(UpperCamelCase_				)



		@add_start_docstrings_to_model_forward(UpperCamelCase_				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,)  ->    Union[str, Any]:

						"""simple docstring"""






						if input_ids is not None and inputs_embeds is not None:
										raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''				)
						elif input_ids is not None:
										lowerCAmelCase__  :       Any				=  input_ids.size()
						elif inputs_embeds is not None:
										lowerCAmelCase__  :       Any				=  inputs_embeds.size()[:-1]
						else:
										raise ValueError('''You have to specify either input_ids or inputs_embeds'''				)

						lowerCAmelCase__  :       str				=  input_ids.device if input_ids is not None else inputs_embeds.device

						if attention_mask is None:
										lowerCAmelCase__  :       Tuple				=  torch.ones(UpperCamelCase_	,device=UpperCamelCase_				)
						if encoder_attention_mask is None:
										lowerCAmelCase__  :       Dict				=  torch.ones(UpperCamelCase_	,device=UpperCamelCase_				)
						if token_type_ids is None:
										lowerCAmelCase__  :       int				=  torch.zeros(UpperCamelCase_	,dtype=torch.long	,device=UpperCamelCase_				)

						# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
						# ourselves in which case we just need to make it broadcastable to all heads.
						lowerCAmelCase__  :       torch.Tensor				=  self.get_extended_attention_mask(UpperCamelCase_	,UpperCamelCase_	,UpperCamelCase_				)

						# If a 2D ou 3D attention mask is provided for the cross-attention
						# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
						if encoder_attention_mask.dim() == 3:
										lowerCAmelCase__  :       int				=  encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.dim() == 2:
										lowerCAmelCase__  :       int				=  encoder_attention_mask[:, None, None, :]

						lowerCAmelCase__  :       str				=  encoder_extended_attention_mask.to(
						    dtype=next(self.parameters()				).dtype				)  # fp16 compatibility
						lowerCAmelCase__  :       Optional[int]				=  (1.0 - encoder_extended_attention_mask) * -1_00_00.0

						# Prepare head mask if needed
						# 1.0 in head_mask indicate we keep the head
						# attention_probs has shape bsz x n_heads x N x N
						# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
						# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
						lowerCAmelCase__  :       Optional[int]				=  self.get_head_mask(UpperCamelCase_	,self.config.num_hidden_layers				)

						lowerCAmelCase__  :       Dict				=  self.embeddings(
						    input_ids=UpperCamelCase_	,position_ids=UpperCamelCase_	,token_type_ids=UpperCamelCase_	,inputs_embeds=UpperCamelCase_				)
						lowerCAmelCase__  :       List[Any]				=  self.encoder(
						    UpperCamelCase_	,attention_mask=UpperCamelCase_	,head_mask=UpperCamelCase_	,encoder_hidden_states=UpperCamelCase_	,encoder_attention_mask=UpperCamelCase_	,)
						lowerCAmelCase__  :       int				=  encoder_outputs[0]
						lowerCAmelCase__  :       str				=  self.pooler(UpperCamelCase_				)

						lowerCAmelCase__  :       List[Any]				=  (
						    sequence_output,
						    pooled_output,
						) + encoder_outputs[
						    1:
						]  # add hidden_states and attentions if they are here
						return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  message
						lowerCAmelCase__  :       Dict				=  exit_layer  # start from 1!



class  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       Optional[Any]				=  BertPooler(UpperCamelCase_				)
						lowerCAmelCase__  :       List[Any]				=  nn.Dropout(config.hidden_dropout_prob				)
						lowerCAmelCase__  :       int				=  nn.Linear(config.hidden_size	,config.num_labels				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  encoder_outputs[0]
						lowerCAmelCase__  :       int				=  self.pooler(UpperCamelCase_				)
						# "return" pooler_output

						# BertModel
						lowerCAmelCase__  :       Optional[int]				=  (pooler_input, pooler_output) + encoder_outputs[1:]
						# "return" bmodel_output

						# Dropout and classification
						lowerCAmelCase__  :       Dict				=  bmodel_output[1]

						lowerCAmelCase__  :       Union[str, Any]				=  self.dropout(UpperCamelCase_				)
						lowerCAmelCase__  :       str				=  self.classifier(UpperCamelCase_				)

						return logits, pooled_output



@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """   ,       lowerCamelCase__   ,       )
class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						super().__init__(UpperCamelCase_				)
						lowerCAmelCase__  :       Dict				=  config.num_labels
						lowerCAmelCase__  :       Any				=  config.num_hidden_layers

						lowerCAmelCase__  :       Optional[int]				=  DeeBertModel(UpperCamelCase_				)
						lowerCAmelCase__  :       Any				=  nn.Dropout(config.hidden_dropout_prob				)
						lowerCAmelCase__  :       Optional[Any]				=  nn.Linear(config.hidden_size	,self.config.num_labels				)

						self.init_weights()



		@add_start_docstrings_to_model_forward(UpperCamelCase_				)
		def 	lowerCAmelCase__   (self	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=-1	,__lowerCamelCase=False	,)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  self.num_layers
						try:
										lowerCAmelCase__  :       Tuple				=  self.bert(
										    UpperCamelCase_	,attention_mask=UpperCamelCase_	,token_type_ids=UpperCamelCase_	,position_ids=UpperCamelCase_	,head_mask=UpperCamelCase_	,inputs_embeds=UpperCamelCase_	,)
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										lowerCAmelCase__  :       List[Any]				=  outputs[1]

										lowerCAmelCase__  :       int				=  self.dropout(UpperCamelCase_				)
										lowerCAmelCase__  :       Optional[int]				=  self.classifier(UpperCamelCase_				)
										lowerCAmelCase__  :       Union[str, Any]				=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
						except HighwayException as e:
										lowerCAmelCase__  :       Union[str, Any]				=  e.message
										lowerCAmelCase__  :       Any				=  e.exit_layer
										lowerCAmelCase__  :       Optional[int]				=  outputs[0]

						if not self.training:
										lowerCAmelCase__  :       Union[str, Any]				=  entropy(UpperCamelCase_				)
										lowerCAmelCase__  :       Tuple				=  []
										lowerCAmelCase__  :       Tuple				=  []
						if labels is not None:
										if self.num_labels == 1:
														#  We are doing regression
														lowerCAmelCase__  :       Tuple				=  MSELoss()
														lowerCAmelCase__  :       Tuple				=  loss_fct(logits.view(-1				)	,labels.view(-1				)				)
										else:
														lowerCAmelCase__  :       Union[str, Any]				=  CrossEntropyLoss()
														lowerCAmelCase__  :       Tuple				=  loss_fct(logits.view(-1	,self.num_labels				)	,labels.view(-1				)				)

										# work with highway exits
										lowerCAmelCase__  :       Optional[Any]				=  []
										for highway_exit in outputs[-1]:
														lowerCAmelCase__  :       Optional[Any]				=  highway_exit[0]
														if not self.training:
																		highway_logits_all.append(UpperCamelCase_				)
																		highway_entropy.append(highway_exit[2]				)
														if self.num_labels == 1:
																		#  We are doing regression
																		lowerCAmelCase__  :       Union[str, Any]				=  MSELoss()
																		lowerCAmelCase__  :       Any				=  loss_fct(highway_logits.view(-1				)	,labels.view(-1				)				)
														else:
																		lowerCAmelCase__  :       Dict				=  CrossEntropyLoss()
																		lowerCAmelCase__  :       Optional[int]				=  loss_fct(highway_logits.view(-1	,self.num_labels				)	,labels.view(-1				)				)
														highway_losses.append(UpperCamelCase_				)

										if train_highway:
														lowerCAmelCase__  :       List[str]				=  (sum(highway_losses[:-1]				),) + outputs
														# exclude the final highway, of course
										else:
														lowerCAmelCase__  :       Optional[Any]				=  (loss,) + outputs
						if not self.training:
										lowerCAmelCase__  :       List[Any]				=  outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
														lowerCAmelCase__  :       Dict				=  (
														    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
														)  # use the highway of the last layer

						return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)






from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=["""pixel_values"""]



		def __init__(self	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = PILImageResampling.BILINEAR	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = True	,__lowerCamelCase = 1 / 2_55	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  size if size is not None else {'''shortest_edge''': 2_56}
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''				)
						lowerCAmelCase__  :       Dict				=  do_resize
						lowerCAmelCase__  :       str				=  size
						lowerCAmelCase__  :       Tuple				=  resample
						lowerCAmelCase__  :       List[Any]				=  do_center_crop
						lowerCAmelCase__  :       Dict				=  crop_size
						lowerCAmelCase__  :       Optional[int]				=  do_rescale
						lowerCAmelCase__  :       List[str]				=  rescale_factor
						lowerCAmelCase__  :       List[Any]				=  do_normalize
						lowerCAmelCase__  :       Optional[Any]				=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__  :       str				=  image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = PILImageResampling.BICUBIC	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
						lowerCAmelCase__  :       str				=  get_resize_output_image_size(__lowerCamelCase	,size=size['''shortest_edge''']	,default_to_square=__lowerCamelCase				)
						return resize(__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase				)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
						return center_crop(__lowerCamelCase	,size=(size['''height'''], size['''width'''])	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase				)  ->    np.ndarray:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = ChannelDimension.FIRST	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__  :       Optional[int]				=  size if size is not None else self.size
						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  resample if resample is not None else self.resample
						lowerCAmelCase__  :       Union[str, Any]				=  do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__  :       Any				=  crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__  :       List[str]				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''				)
						lowerCAmelCase__  :       List[str]				=  do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__  :       str				=  rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__  :       int				=  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__  :       Optional[Any]				=  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__  :       List[Any]				=  image_std if image_std is not None else self.image_std

						lowerCAmelCase__  :       Any				=  make_list_of_images(__lowerCamelCase				)

						if not valid_images(__lowerCamelCase				):
										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.'''				)

						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.
						lowerCAmelCase__  :       Union[str, Any]				=  [to_numpy_array(__lowerCamelCase				) for image in images]

						if do_resize:
										lowerCAmelCase__  :       str				=  [self.resize(image=__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase				) for image in images]

						if do_center_crop:
										lowerCAmelCase__  :       List[Any]				=  [self.center_crop(image=__lowerCamelCase	,size=__lowerCamelCase				) for image in images]

						if do_rescale:
										lowerCAmelCase__  :       Any				=  [self.rescale(image=__lowerCamelCase	,scale=__lowerCamelCase				) for image in images]

						if do_normalize:
										lowerCAmelCase__  :       Tuple				=  [self.normalize(image=__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       int				=  [to_channel_dimension_format(__lowerCamelCase	,__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       int				=  {'''pixel_values''': images}
						return BatchFeature(data=__lowerCamelCase	,tensor_type=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
										if len(__lowerCamelCase				) != len(__lowerCamelCase				):
														raise ValueError(
														    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

										if is_torch_tensor(__lowerCamelCase				):
														lowerCAmelCase__  :       str				=  target_sizes.numpy()

										lowerCAmelCase__  :       Optional[Any]				=  []

										for idx in range(len(__lowerCamelCase				)				):
														lowerCAmelCase__  :       Optional[int]				=  torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0				)	,size=target_sizes[idx]	,mode='''bilinear'''	,align_corners=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[Any]				=  resized_logits[0].argmax(dim=0				)
														semantic_segmentation.append(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       Optional[Any]				=  logits.argmax(dim=1				)
										lowerCAmelCase__  :       int				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

						return semantic_segmentation

