






'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.nn.Linear(2,		4							)
      SCREAMING_SNAKE_CASE__       :			Dict													=torch.optim.AdamW(model.parameters(),		lr=1.0							)
      SCREAMING_SNAKE_CASE__       :			Dict													=torch.optim.lr_scheduler.OneCycleLR(UpperCamelCase__,		max_lr=0.0_1,		steps_per_epoch=2,		epochs=1							)
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=DataLoader(TensorDataset(torch.tensor([1, 2, 3]							)							)							)
      SCREAMING_SNAKE_CASE__       :			Any													=DataLoader(TensorDataset(torch.tensor([4, 5, 6]							)							)							)

      return model, optimizer, scheduler, train_dl, valid_dl





def 	_a(					UpperCamelCase__			:  Union[str, Any]							):




      '''simple docstring'''






      return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=torch.nn.Linear(*tuple(model.weight.T.shape							)							).state_dict()
      model.load_state_dict(UpperCamelCase__							)







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 @require_cuda
 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=Accelerator()
       assert PartialState._shared_state["_cpu"] is False
       assert PartialState._shared_state["device"].type == "cuda"
       with self.assertRaises(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			Any													=Accelerator(cpu=__lowercase )




 def        __magic_name__		( self		:							List[Any] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=Accelerator()
       SCREAMING_SNAKE_CASE__       :			List[Any]													=GradientState()
       assert state.num_steps == 1
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=4
       assert state.num_steps == 4

       assert state.sync_gradients is True
       SCREAMING_SNAKE_CASE__       :			List[str]													=False
       assert state.sync_gradients is False
       GradientState._reset_state()




 def        __magic_name__		( self		:							Optional[int] )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=Accelerator()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=create_components()

       (
           (
           SCREAMING_SNAKE_CASE__
       )						,		(
           SCREAMING_SNAKE_CASE__
       )						,		(
           SCREAMING_SNAKE_CASE__
       )						,		(
           SCREAMING_SNAKE_CASE__
       )						,		(
           SCREAMING_SNAKE_CASE__
       )						,		
       )       :			int													=accelerator.prepare(__lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )

       self.assertTrue(prepared_model in accelerator._models )
       self.assertTrue(prepared_optimizer in accelerator._optimizers )
       self.assertTrue(prepared_scheduler in accelerator._schedulers )
       self.assertTrue(prepared_train_dl in accelerator._dataloaders )
       self.assertTrue(prepared_valid_dl in accelerator._dataloaders )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Dict													=Accelerator()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			int													=create_components()
       accelerator.prepare(__lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )
       accelerator.free_memory()

       self.assertTrue(len(accelerator._models ) == 0 )
       self.assertTrue(len(accelerator._optimizers ) == 0 )
       self.assertTrue(len(accelerator._schedulers ) == 0 )
       self.assertTrue(len(accelerator._dataloaders ) == 0 )




 def        __magic_name__		( self		:							Any )		->  Any:
       PartialState._reset_state()

       # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
       def noop(*__lowercase		:							List[str]   , **__lowercase		:							List[str] ):
             pass

       with patch('''torch.cuda.set_device'''   , __lowercase ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64''' ):
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=Accelerator()
             self.assertEqual(str(accelerator.state.device )   , '''cuda:64''' )




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=Accelerator()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=create_components()
       accelerator.prepare(__lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=get_signature(__lowercase )

       with tempfile.TemporaryDirectory() as tmpdirname:
             accelerator.save_state(__lowercase )

             # make sure random weights don't match
             load_random_weights(__lowercase )
             self.assertTrue(abs(model_signature - get_signature(__lowercase ) ) > 1e-3 )

             # make sure loaded weights match
             accelerator.load_state(__lowercase )
             self.assertTrue(abs(model_signature - get_signature(__lowercase ) ) < 1e-3 )




 def        __magic_name__		( self		:							Optional[Any] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=Accelerator()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=create_components()
       accelerator.prepare(__lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_signature(__lowercase )

       # saving hook
       def save_config(__lowercase		:							Tuple   , __lowercase		:							str   , __lowercase		:							Dict ):
             SCREAMING_SNAKE_CASE__       :			Dict													={'''class_name''': models[0].__class__.__name__}

             with open(os.path.join(__lowercase   , '''data.json''' )   , '''w''' ) as f:
                   json.dump(__lowercase   , __lowercase )

        # loading hook
       def load_config(__lowercase		:							Tuple   , __lowercase		:							int ):
             with open(os.path.join(__lowercase   , '''data.json''' )   , '''r''' ) as f:
                   SCREAMING_SNAKE_CASE__       :			str													=json.load(__lowercase )

             SCREAMING_SNAKE_CASE__       :			List[Any]													=config['''class_name''']

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=accelerator.register_save_state_pre_hook(__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[str]													=accelerator.register_load_state_pre_hook(__lowercase )

       with tempfile.TemporaryDirectory() as tmpdirname:
             accelerator.save_state(__lowercase )

             # make sure random weights don't match with hooks
             load_random_weights(__lowercase )
             self.assertTrue(abs(model_signature - get_signature(__lowercase ) ) > 1e-3 )

             # random class name to verify correct one is loaded
             SCREAMING_SNAKE_CASE__       :			List[Any]													='''random'''

             # make sure loaded weights match with hooks
             accelerator.load_state(__lowercase )
             self.assertTrue(abs(model_signature - get_signature(__lowercase ) ) < 1e-3 )

             # mode.class_name is loaded from config
             self.assertTrue(model.class_name == model.__class__.__name__ )

       # remove hooks
       save_hook.remove()
       load_hook.remove()

       with tempfile.TemporaryDirectory() as tmpdirname:
             accelerator.save_state(__lowercase )

             # make sure random weights don't match with hooks removed
             load_random_weights(__lowercase )
             self.assertTrue(abs(model_signature - get_signature(__lowercase ) ) > 1e-3 )

             # random class name to verify correct one is loaded
             SCREAMING_SNAKE_CASE__       :			List[Any]													='''random'''

             # make sure loaded weights match with hooks removed
             accelerator.load_state(__lowercase )
             self.assertTrue(abs(model_signature - get_signature(__lowercase ) ) < 1e-3 )

             # mode.class_name is NOT loaded from config
             self.assertTrue(model.class_name != model.__class__.__name__ )




 def        __magic_name__		( self		:							str )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=Accelerator()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=create_components()
       SCREAMING_SNAKE_CASE__       :			int													=None

       # This should work
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=accelerator.prepare(
           __lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )
       self.assertTrue(dummy_obj is None )




 def        __magic_name__		( self		:							Any )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			int													=Accelerator()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			int													=create_components()
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[1, 2, 3]

       # This should work
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[Any]													=accelerator.prepare(
           __lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase   , __lowercase )
       self.assertEqual(
           getattr(__lowercase   , '''_is_accelerate_prepared'''   , __lowercase )   , __lowercase   , '''Dummy object should have `_is_accelerate_prepared` set to `True`'''   , )
       self.assertEqual(
           getattr(__lowercase   , '''_is_accelerate_prepared'''   , __lowercase )   , __lowercase   , '''Model is missing `_is_accelerator_prepared` or is set to `False`'''   , )
       self.assertEqual(
           getattr(__lowercase   , '''_is_accelerate_prepared'''   , __lowercase )   , __lowercase   , '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''   , )
       self.assertEqual(
           getattr(__lowercase   , '''_is_accelerate_prepared'''   , __lowercase )   , __lowercase   , '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''   , )
       self.assertEqual(
           getattr(__lowercase   , '''_is_accelerate_prepared'''   , __lowercase )   , __lowercase   , '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''   , )
       self.assertEqual(
           getattr(__lowercase   , '''_is_accelerate_prepared'''   , __lowercase )   , __lowercase   , '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''   , )




 @slow
 @require_bnb
 def        __magic_name__		( self		:							str )		->  Any:
       from transformers import AutoModelForCausalLM

       SCREAMING_SNAKE_CASE__       :			Any													=AutoModelForCausalLM.from_pretrained(
           '''EleutherAI/gpt-neo-125m'''   , load_in_abit=__lowercase   , device_map={'''''': 0}   , )
       SCREAMING_SNAKE_CASE__       :			Any													=Accelerator()

       # This should work
       SCREAMING_SNAKE_CASE__       :			str													=accelerator.prepare(__lowercase )




 @slow
 @require_bnb
 def        __magic_name__		( self		:							Any )		->  Any:
       from transformers import AutoModelForCausalLM

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=Accelerator()

       with init_empty_weights():
             SCREAMING_SNAKE_CASE__       :			str													=AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''   , )
             model.tie_weights()
             SCREAMING_SNAKE_CASE__       :			List[Any]													=infer_auto_device_map(__lowercase )
             SCREAMING_SNAKE_CASE__       :			Any													='''cpu'''

       SCREAMING_SNAKE_CASE__       :			List[Any]													=AutoModelForCausalLM.from_pretrained(
           '''EleutherAI/gpt-neo-125m'''   , device_map=__lowercase   , load_in_abit=__lowercase   , llm_inta_enable_fpaa_cpu_offload=__lowercase )

       # This should not work and get value error
       with self.assertRaises(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			List[str]													=accelerator.prepare(__lowercase )




 @slow
 @require_bnb
 @require_multi_gpu
 def        __magic_name__		( self		:							List[Any] )		->  Any:
       from transformers import AutoModelForCausalLM

       SCREAMING_SNAKE_CASE__       :			Optional[int]													={'''distributed_type''': DistributedType.MULTI_GPU}

       with init_empty_weights():
             SCREAMING_SNAKE_CASE__       :			Any													=AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''   , )
             model.tie_weights()
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=infer_auto_device_map(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=1

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=AutoModelForCausalLM.from_pretrained(
           '''EleutherAI/gpt-neo-125m'''   , load_in_abit=__lowercase   , device_map=__lowercase   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=Accelerator()

       # This should not work and get value error
       with self.assertRaises(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			Dict													=accelerator.prepare(__lowercase )

       PartialState._reset_state()




 @slow
 @require_bnb
 @require_multi_gpu
 def        __magic_name__		( self		:							List[str] )		->  int:
       from transformers import AutoModelForCausalLM

       with init_empty_weights():
             SCREAMING_SNAKE_CASE__       :			Tuple													=AutoModelForCausalLM.from_pretrained(
                 '''EleutherAI/gpt-neo-125m'''   , )
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=infer_auto_device_map(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[str]													=1

       SCREAMING_SNAKE_CASE__       :			str													=AutoModelForCausalLM.from_pretrained(
           '''EleutherAI/gpt-neo-125m'''   , load_in_abit=__lowercase   , device_map=__lowercase   , )
       SCREAMING_SNAKE_CASE__       :			int													=Accelerator()

       # This should work
       SCREAMING_SNAKE_CASE__       :			int													=accelerator.prepare(__lowercase )




 @require_cuda
 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.nn.Linear(10   , 10 )
       SCREAMING_SNAKE_CASE__       :			str													=torch.optim.SGD(model.parameters()   , lr=0.01 )
       SCREAMING_SNAKE_CASE__       :			Any													=Accelerator(cpu=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=accelerator.prepare(__lowercase )







'''simple docstring'''
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


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""pixel_values"""]




 def __init__( self		:							List[str]   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							bool = True   , __lowercase		:							Union[int, float] = 1 / 2_55   , __lowercase		:							bool = True   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , **__lowercase		:							List[Any]   , )		->  None:
       super().__init__(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=size if size is not None else {'''shortest_edge''': 2_56}
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			int													=do_resize
       SCREAMING_SNAKE_CASE__       :			Dict													=size
       SCREAMING_SNAKE_CASE__       :			List[str]													=resample
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_center_crop
       SCREAMING_SNAKE_CASE__       :			str													=crop_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_rescale
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__       :			Any													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[Any]   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
       return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							int   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Any   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
       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(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Tuple )		->  np.ndarray:
       return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							np.ndarray   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							str   , )		->  np.ndarray:
       return normalize(__lowercase   , mean=__lowercase   , std=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__       :			int													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__       :			int													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__       :			Any													=image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__       :			str													=make_list_of_images(__lowercase )

       if not valid_images(__lowercase ):
             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.
       SCREAMING_SNAKE_CASE__       :			Dict													=[to_numpy_array(__lowercase ) for image in images]

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

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

       if do_rescale:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.rescale(image=__lowercase   , scale=__lowercase ) for image in images]

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

       SCREAMING_SNAKE_CASE__       :			str													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			Any													={'''pixel_values''': images}
       return BatchFeature(data=__lowercase   , tensor_type=__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Tuple   , __lowercase		:							List[Tuple] = None )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=outputs.logits

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

             if is_torch_tensor(__lowercase ):
                   SCREAMING_SNAKE_CASE__       :			int													=target_sizes.numpy()

             SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

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

       return semantic_segmentation