"""simple docstring"""







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         LayoutLMForMaskedLM,
         LayoutLMForQuestionAnswering,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMModel,
     )






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=99 ,				_SCREAMING_SNAKE_CASE=32 ,				_SCREAMING_SNAKE_CASE=5 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=37 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=512 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=1000 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					seq_length
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_input_mask
      __lowerCamelCase    		=					use_token_type_ids
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					intermediate_size
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					max_position_embeddings
      __lowerCamelCase    		=					type_vocab_size
      __lowerCamelCase    		=					type_sequence_label_size
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					num_choices
      __lowerCamelCase    		=					scope
      __lowerCamelCase    		=					range_bbox







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length, 4] ,				self.range_bbox	)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]	):
         for j in range(bbox.shape[1]	):
            if bbox[i, j, 3] < bbox[i, j, 1]:
               __lowerCamelCase    		=					bbox[i, j, 3]
               __lowerCamelCase    		=					bbox[i, j, 1]
               __lowerCamelCase    		=					t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               __lowerCamelCase    		=					bbox[i, j, 2]
               __lowerCamelCase    		=					bbox[i, j, 0]
               __lowerCamelCase    		=					t

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					self.get_config()

      return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def    _A				(	self	):


      '''simple docstring'''

      return LayoutLMConfig(
          vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				initializer_range=self.initializer_range ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)
      self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				start_positions=_SCREAMING_SNAKE_CASE ,				end_positions=_SCREAMING_SNAKE_CASE ,				)
      self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)	)
      self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[str]    =    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   __UpperCAmelCase	:							List[Any]    =    (
       {
           "document-question-answering": LayoutLMForQuestionAnswering,
           "feature-extraction": LayoutLMModel,
           "fill-mask": LayoutLMForMaskedLM,
           "text-classification": LayoutLMForSequenceClassification,
           "token-classification": LayoutLMForTokenClassification,
           "zero-shot": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase	:							int    =    True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				hidden_size=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __lowerCamelCase    		=					type
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE	)







   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass



def       A__    (	)  ->							Optional[int]:
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   __lowerCamelCase    		=					torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]      , device=lowercase_						)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   __lowerCamelCase    		=					torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]      , device=lowercase_						)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the sequence output on [0, :3, :3]
      __lowerCamelCase    		=					torch.tensor(
          [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] ,				device=_SCREAMING_SNAKE_CASE ,				)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-3	)	)

      # test the pooled output on [1, :3]
      __lowerCamelCase    		=					torch.tensor([-0.6580, -0.0214, 0.8552] ,				device=_SCREAMING_SNAKE_CASE	)

      self.assertTrue(torch.allclose(outputs.pooler_output[1, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-3	)	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=2	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=torch.tensor([1, 1] ,				device=_SCREAMING_SNAKE_CASE	) ,				)

      # test whether we get a loss as a scalar
      __lowerCamelCase    		=					outputs.loss
      __lowerCamelCase    		=					torch.Size([]	)
      self.assertEqual(loss.shape ,				_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 2)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=13	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)

      # test the loss calculation to be around 2.65
      # expected_loss = torch.tensor(2.65, device=torch_device)

      # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
      # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 25, 13)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					torch.Size((2, 25)	)
      self.assertEqual(outputs.start_logits.shape ,				_SCREAMING_SNAKE_CASE	)
      self.assertEqual(outputs.end_logits.shape ,				_SCREAMING_SNAKE_CASE	)












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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCAmelCase						:    Optional[Any]      =  logging.get_logger(__name__)


if is_vision_available():
     import PIL




class  __lowerCAmelCase							(				lowerCAmelCase):
       _a			       =    ['''pixel_values''']






       def __init__( self:       Optional[int]							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       Dict[str, int] = None							,       _lowerCAmelCase:       PILImageResampling = PILImageResampling.BILINEAR							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       bool = False							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       Union[int, float] = 1 / 2_55							,       _lowerCAmelCase:       bool = True							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       **_lowerCAmelCase:       Dict							,       ):
         super().__init__(**_lowerCAmelCase					)

         lowercase     :Tuple   						=    size if size is not None else {"height": 25_60, "width": 19_20}
         if isinstance(_lowerCAmelCase							,       (tuple, list)					):
           # The previous feature extractor size parameter was in (width, height) format
           lowercase     :Union[str, Any]   						=    size[::-1]
         lowercase     :int   						=    get_size_dict(_lowerCAmelCase					)

         lowercase     :Optional[Any]   						=    do_resize
         lowercase     :int   						=    size
         lowercase     :List[str]   						=    resample
         lowercase     :Any   						=    do_thumbnail
         lowercase     :Optional[int]   						=    do_align_long_axis
         lowercase     :Tuple   						=    do_pad
         lowercase     :Union[str, Any]   						=    do_rescale
         lowercase     :Union[str, Any]   						=    rescale_factor
         lowercase     :Optional[int]   						=    do_normalize
         lowercase     :Dict   						=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowercase     :Union[str, Any]   						=    image_std if image_std is not None else IMAGENET_STANDARD_STD






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None					):
         lowercase						,     lowercase     :int   						=    get_image_size(_lowerCAmelCase					)
         lowercase						,     lowercase     :str   						=    size["height"], size["width"]

         if (output_width < output_height and input_width > input_height) or (
             output_width > output_height and input_width < input_height
         ):
           lowercase     :Any   						=    np.rotaa(_lowerCAmelCase							,       3					)

         if data_format is not None:
           lowercase     :Optional[int]   						=    to_channel_dimension_format(_lowerCAmelCase							,       _lowerCAmelCase					)

         return image






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       bool = False							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       ):
         lowercase						,     lowercase     :Tuple   						=    size["height"], size["width"]
         lowercase						,     lowercase     :Optional[int]   						=    get_image_size(_lowerCAmelCase					)

         lowercase     :List[Any]   						=    output_width - input_width
         lowercase     :Dict   						=    output_height - input_height

         if random_padding:
           lowercase     :List[str]   						=    np.random.randint(low=0							,       high=delta_height + 1					)
           lowercase     :List[Any]   						=    np.random.randint(low=0							,       high=delta_width + 1					)
         else:
           lowercase     :Dict   						=    delta_height // 2
           lowercase     :int   						=    delta_width // 2

         lowercase     :Optional[int]   						=    delta_height - pad_top
         lowercase     :Dict   						=    delta_width - pad_left

         lowercase     :Optional[Any]   						=    ((pad_top, pad_bottom), (pad_left, pad_right))
         return pad(_lowerCAmelCase							,       _lowerCAmelCase							,       data_format=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str							,       *_lowerCAmelCase:       List[str]							,       **_lowerCAmelCase:       List[str]					):
         logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."					)
         return self.pad_image(*_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       PILImageResampling = PILImageResampling.BICUBIC							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       int							,       ):
         lowercase						,     lowercase     :Any   						=    get_image_size(_lowerCAmelCase					)
         lowercase						,     lowercase     :List[str]   						=    size["height"], size["width"]

         # We always resize to the smallest of either the input or output size.
         lowercase     :Union[str, Any]   						=    min(_lowerCAmelCase							,       _lowerCAmelCase					)
         lowercase     :Any   						=    min(_lowerCAmelCase							,       _lowerCAmelCase					)

         if height == input_height and width == input_width:
           return image

         if input_height > input_width:
           lowercase     :Union[str, Any]   						=    int(input_width * height / input_height					)
         elif input_width > input_height:
           lowercase     :int   						=    int(input_height * width / input_width					)

         return resize(
             _lowerCAmelCase							,       size=(height, width)							,       resample=_lowerCAmelCase							,       reducing_gap=2.0							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Dict[str, int]							,       _lowerCAmelCase:       PILImageResampling = PILImageResampling.BICUBIC							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       str							,       ):
         lowercase     :Optional[Any]   						=    get_size_dict(_lowerCAmelCase					)
         lowercase     :Optional[Any]   						=    min(size["height"]							,       size["width"]					)
         lowercase     :Tuple   						=    get_resize_output_image_size(_lowerCAmelCase							,       size=_lowerCAmelCase							,       default_to_square=_lowerCAmelCase					)
         lowercase     :Optional[int]   						=    resize(_lowerCAmelCase							,       size=_lowerCAmelCase							,       resample=_lowerCAmelCase							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)
         return resized_image






       def        SCREAMING_SNAKE_CASE     ( self:       Dict							,       _lowerCAmelCase:       np.ndarray							,       _lowerCAmelCase:       Union[int, float]							,       _lowerCAmelCase:       Optional[Union[str, ChannelDimension]] = None							,       **_lowerCAmelCase:       List[Any]							,       ):
         return rescale(_lowerCAmelCase							,       scale=_lowerCAmelCase							,       data_format=_lowerCAmelCase							,       **_lowerCAmelCase					)






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






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple							,       _lowerCAmelCase:       ImageInput							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       Dict[str, int] = None							,       _lowerCAmelCase:       PILImageResampling = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       bool = False							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       float = None							,       _lowerCAmelCase:       bool = None							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       _lowerCAmelCase:       Optional[Union[float, List[float]]] = None							,       _lowerCAmelCase:       Optional[Union[str, TensorType]] = None							,       _lowerCAmelCase:       Optional[ChannelDimension] = ChannelDimension.FIRST							,       **_lowerCAmelCase:       Optional[Any]							,       ):
         lowercase     :Union[str, Any]   						=    do_resize if do_resize is not None else self.do_resize
         lowercase     :Optional[int]   						=    size if size is not None else self.size
         if isinstance(_lowerCAmelCase							,       (tuple, list)					):
           # Previous feature extractor had size in (width, height) format
           lowercase     :List[str]   						=    size[::-1]
         lowercase     :List[str]   						=    get_size_dict(_lowerCAmelCase					)
         lowercase     :Optional[int]   						=    resample if resample is not None else self.resample
         lowercase     :Union[str, Any]   						=    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
         lowercase     :str   						=    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
         lowercase     :int   						=    do_pad if do_pad is not None else self.do_pad
         lowercase     :Optional[Any]   						=    do_rescale if do_rescale is not None else self.do_rescale
         lowercase     :str   						=    rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase     :int   						=    do_normalize if do_normalize is not None else self.do_normalize
         lowercase     :Dict   						=    image_mean if image_mean is not None else self.image_mean
         lowercase     :str   						=    image_std if image_std is not None else self.image_std

         lowercase     :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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."					)

         if do_pad and size is None:
           raise ValueError("Size must be specified if do_pad 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.
         lowercase     :Union[str, Any]   						=    [to_numpy_array(_lowerCAmelCase					) for image in images]

         if do_align_long_axis:
           lowercase     :Union[str, Any]   						=    [self.align_long_axis(_lowerCAmelCase							,       size=_lowerCAmelCase					) for image in images]

         if do_resize:
           lowercase     :Optional[Any]   						=    [self.resize(image=_lowerCAmelCase							,       size=_lowerCAmelCase							,       resample=_lowerCAmelCase					) for image in images]

         if do_thumbnail:
           lowercase     :str   						=    [self.thumbnail(image=_lowerCAmelCase							,       size=_lowerCAmelCase					) for image in images]

         if do_pad:
           lowercase     :List[Any]   						=    [self.pad_image(image=_lowerCAmelCase							,       size=_lowerCAmelCase							,       random_padding=_lowerCAmelCase					) for image in images]

         if do_rescale:
           lowercase     :Optional[Any]   						=    [self.rescale(image=_lowerCAmelCase							,       scale=_lowerCAmelCase					) for image in images]

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

         lowercase     :List[str]   						=    [to_channel_dimension_format(_lowerCAmelCase							,       _lowerCAmelCase					) for image in images]

         lowercase     :List[str]   						=    {"pixel_values": images}
         return BatchFeature(data=_lowerCAmelCase							,       tensor_type=_lowerCAmelCase					)



