



"""simple docstring"""




from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.layoutlm.modeling_tf_layoutlm import (
     TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFLayoutLMForMaskedLM,
     TFLayoutLMForQuestionAnswering,
     TFLayoutLMForSequenceClassification,
     TFLayoutLMForTokenClassification,
     TFLayoutLMModel,
 )






class      UpperCAmelCase_  :



      def __init__(					self					:     Union[str, Any]					,     A					:     Optional[int]					,     A					:     List[str]=1_3					,     A					:     Optional[Any]=7					,     A					:     str=True					,     A					:     Union[str, Any]=True					,     A					:     Optional[int]=True					,     A					:     List[str]=True					,     A					:     Tuple=9_9					,     A					:     Tuple=3_2					,     A					:     str=2					,     A					:     str=4					,     A					:     List[Any]=3_7					,     A					:     int="gelu"					,     A					:     Dict=0.1					,     A					:     Optional[int]=0.1					,     A					:     List[str]=5_1_2					,     A					:     int=1_6					,     A					:     Optional[int]=2					,     A					:     Optional[Any]=0.02					,     A					:     int=3					,     A					:     int=4					,     A					:     Union[str, Any]=None					,     A					:     Tuple=1_0_0_0					,     ):
           _UpperCAmelCase      :     Union[str, Any]        =   parent
           _UpperCAmelCase      :     Tuple        =   batch_size
           _UpperCAmelCase      :     Dict        =   seq_length
           _UpperCAmelCase      :     Any        =   is_training
           _UpperCAmelCase      :     str        =   use_input_mask
           _UpperCAmelCase      :     Tuple        =   use_token_type_ids
           _UpperCAmelCase      :     Tuple        =   use_labels
           _UpperCAmelCase      :     Optional[Any]        =   vocab_size
           _UpperCAmelCase      :     Optional[int]        =   hidden_size
           _UpperCAmelCase      :     Optional[Any]        =   num_hidden_layers
           _UpperCAmelCase      :     Tuple        =   num_attention_heads
           _UpperCAmelCase      :     Dict        =   intermediate_size
           _UpperCAmelCase      :     Dict        =   hidden_act
           _UpperCAmelCase      :     int        =   hidden_dropout_prob
           _UpperCAmelCase      :     List[Any]        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     Tuple        =   max_position_embeddings
           _UpperCAmelCase      :     Dict        =   type_vocab_size
           _UpperCAmelCase      :     List[str]        =   type_sequence_label_size
           _UpperCAmelCase      :     str        =   initializer_range
           _UpperCAmelCase      :     Any        =   num_labels
           _UpperCAmelCase      :     Dict        =   num_choices
           _UpperCAmelCase      :     str        =   scope
           _UpperCAmelCase      :     Tuple        =   range_bbox



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[Any]        =   ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size						)

           # convert bbox to numpy since TF does not support item assignment
           _UpperCAmelCase      :     List[Any]        =   ids_tensor([self.batch_size, self.seq_length, 4]					,     self.range_bbox						).numpy()
           # 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]:
                          _UpperCAmelCase      :     Optional[Any]        =   bbox[i, j, 3]
                          _UpperCAmelCase      :     Optional[Any]        =   bbox[i, j, 1]
                          _UpperCAmelCase      :     int        =   t
                     if bbox[i, j, 2] < bbox[i, j, 0]:
                          _UpperCAmelCase      :     List[Any]        =   bbox[i, j, 2]
                          _UpperCAmelCase      :     int        =   bbox[i, j, 0]
                          _UpperCAmelCase      :     Any        =   t
           _UpperCAmelCase      :     Optional[Any]        =   tf.convert_to_tensor(A						)

           _UpperCAmelCase      :     Optional[Any]        =   None
           if self.use_input_mask:
                _UpperCAmelCase      :     List[str]        =   random_attention_mask([self.batch_size, self.seq_length]						)

           _UpperCAmelCase      :     int        =   None
           if self.use_token_type_ids:
                _UpperCAmelCase      :     Optional[Any]        =   ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size						)

           _UpperCAmelCase      :     Any        =   None
           _UpperCAmelCase      :     Optional[Any]        =   None
           _UpperCAmelCase      :     Tuple        =   None
           if self.use_labels:
                _UpperCAmelCase      :     List[str]        =   ids_tensor([self.batch_size]					,     self.type_sequence_label_size						)
                _UpperCAmelCase      :     List[Any]        =   ids_tensor([self.batch_size, self.seq_length]					,     self.num_labels						)
                _UpperCAmelCase      :     Dict        =   ids_tensor([self.batch_size]					,     self.num_choices						)

           _UpperCAmelCase      :     Optional[Any]        =   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					,     )

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



      def 						snake_case_ (					self					:     List[Any]					,     A					:     Optional[Any]					,     A					:     int					,     A					:     int					,     A					:     Optional[int]					,     A					:     int					,     A					:     Dict					,     A					:     Dict					,     A					:     List[Any]						):
           _UpperCAmelCase      :     Tuple        =   TFLayoutLMModel(config=A						)

           _UpperCAmelCase      :     str        =   model(A					,     A					,     attention_mask=A					,     token_type_ids=A						)
           _UpperCAmelCase      :     List[Any]        =   model(A					,     A					,     token_type_ids=A						)
           _UpperCAmelCase      :     Optional[Any]        =   model(A					,     A						)
           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 						snake_case_ (					self					:     Any					,     A					:     Any					,     A					:     Optional[int]					,     A					:     Optional[Any]					,     A					:     Optional[Any]					,     A					:     Tuple					,     A					:     Optional[Any]					,     A					:     List[str]					,     A					:     Tuple						):
           _UpperCAmelCase      :     Any        =   TFLayoutLMForMaskedLM(config=A						)

           _UpperCAmelCase      :     Dict        =   model(A					,     A					,     attention_mask=A					,     token_type_ids=A					,     labels=A						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.vocab_size)						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     Union[str, Any]					,     A					:     List[Any]					,     A					:     int					,     A					:     Optional[Any]					,     A					:     Any					,     A					:     str					,     A					:     Union[str, Any]					,     A					:     Optional[Any]						):
           _UpperCAmelCase      :     List[str]        =   self.num_labels
           _UpperCAmelCase      :     Optional[Any]        =   TFLayoutLMForSequenceClassification(config=A						)

           _UpperCAmelCase      :     Union[str, Any]        =   model(A					,     A					,     attention_mask=A					,     token_type_ids=A						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)						)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     Union[str, Any]					,     A					:     Optional[Any]					,     A					:     List[str]					,     A					:     List[str]					,     A					:     Union[str, Any]					,     A					:     List[str]					,     A					:     List[Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.num_labels
           _UpperCAmelCase      :     Union[str, Any]        =   TFLayoutLMForTokenClassification(config=A						)

           _UpperCAmelCase      :     Optional[int]        =   model(A					,     A					,     attention_mask=A					,     token_type_ids=A					,     labels=A						)
           self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.seq_length, self.num_labels)						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     Dict					,     A					:     str					,     A					:     str					,     A					:     List[Any]					,     A					:     Tuple					,     A					:     Tuple					,     A					:     int					,     A					:     List[Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   TFLayoutLMForQuestionAnswering(config=A						)

           _UpperCAmelCase      :     Optional[Any]        =   model(A					,     A					,     attention_mask=A					,     token_type_ids=A						)
           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 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Optional[int]        =   self.prepare_config_and_inputs()
           (
               (
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	(
               _UpperCAmelCase
           )    ,	
           )      :     Tuple        =   config_and_inputs
           _UpperCAmelCase      :     List[Any]        =   {
               "input_ids": input_ids,
               "bbox": bbox,
               "token_type_ids": token_type_ids,
               "attention_mask": input_mask,
           }
           return config, inputs_dict







@require_tf
class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			(
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE						:       int								=			(
          {
              'feature-extraction': TFLayoutLMModel,
              'fill-mask': TFLayoutLMForMaskedLM,
              'text-classification': TFLayoutLMForSequenceClassification,
              'token-classification': TFLayoutLMForTokenClassification,
              'zero-shot': TFLayoutLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			False
      __SCREAMING_SNAKE_CASE						:       str								=			True
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			1_0



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Any        =   TFLayoutLMModelTester(self						)
           _UpperCAmelCase      :     List[Any]        =   ConfigTester(self					,     config_class=A					,     hidden_size=3_7						)



      def 						snake_case_ (					self					:     Union[str, Any]						):
           self.config_tester.run_common_tests()



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*A						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     int        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Any        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*A						)



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*A						)



      @slow
      def 						snake_case_ (					self					:     Tuple						):
           for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase      :     Optional[Any]        =   TFLayoutLMModel.from_pretrained(A						)
                self.assertIsNotNone(A						)



      @unittest.skip("Onnx compliancy broke with TF 2.10"						)
      def 						snake_case_ (					self					:     Dict						):
           pass







def 						__snake_case						(				)  ->       Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]    )  # noqa: E231
     _UpperCAmelCase      :     List[Any]        =   tf.convert_to_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],]    )  # noqa: E231
     _UpperCAmelCase      :     Tuple        =   tf.convert_to_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],[1_000,1_000,1_000,1_000]],[[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],[1_000,1_000,1_000,1_000]]]    )  # noqa: E231
     _UpperCAmelCase      :     int        =   tf.convert_to_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]]    )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     _UpperCAmelCase      :     Tuple        =   tf.convert_to_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]]    )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class      UpperCAmelCase_  (    unittest.TestCase						):



      @slow
      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     str        =   TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"						)

           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   prepare_layoutlm_batch_inputs()

           # forward pass
           _UpperCAmelCase      :     Optional[Any]        =   model(input_ids=A					,     bbox=A					,     attention_mask=A					,     token_type_ids=A						)

           # test the sequence output on [0, :3, :3]
           _UpperCAmelCase      :     Dict        =   tf.convert_to_tensor(
               [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]					,     )

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]					,     A					,     atol=1e-3						)						)

           # test the pooled output on [1, :3]
           _UpperCAmelCase      :     Optional[int]        =   tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]						)

           self.assertTrue(np.allclose(outputs.pooler_output[1, :3]					,     A					,     atol=1e-3						)						)



      @slow
      def 						snake_case_ (					self					:     Any						):
           # initialize model with randomly initialized sequence classification head
           _UpperCAmelCase      :     Any        =   TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"					,     num_labels=2						)

           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   prepare_layoutlm_batch_inputs()

           # forward pass
           _UpperCAmelCase      :     List[Any]        =   model(
               input_ids=A					,     bbox=A					,     attention_mask=A					,     token_type_ids=A					,     labels=tf.convert_to_tensor([1, 1]						)					,     )

           # test whether we get a loss as a scalar
           _UpperCAmelCase      :     Optional[int]        =   outputs.loss
           _UpperCAmelCase      :     Dict        =   (2,)
           self.assertEqual(loss.shape					,     A						)

           # test the shape of the logits
           _UpperCAmelCase      :     Dict        =   outputs.logits
           _UpperCAmelCase      :     Optional[int]        =   (2, 2)
           self.assertEqual(logits.shape					,     A						)



      @slow
      def 						snake_case_ (					self					:     str						):
           # initialize model with randomly initialized token classification head
           _UpperCAmelCase      :     int        =   TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"					,     num_labels=1_3						)

           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   prepare_layoutlm_batch_inputs()

           # forward pass
           _UpperCAmelCase      :     List[str]        =   model(
               input_ids=A					,     bbox=A					,     attention_mask=A					,     token_type_ids=A					,     labels=A						)

           # test the shape of the logits
           _UpperCAmelCase      :     List[Any]        =   outputs.logits
           _UpperCAmelCase      :     Dict        =   tf.convert_to_tensor((2, 2_5, 1_3)						)
           self.assertEqual(logits.shape					,     A						)



      @slow
      def 						snake_case_ (					self					:     int						):
           # initialize model with randomly initialized token classification head
           _UpperCAmelCase      :     Optional[Any]        =   TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"						)

           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   prepare_layoutlm_batch_inputs()

           # forward pass
           _UpperCAmelCase      :     Optional[Any]        =   model(input_ids=A					,     bbox=A					,     attention_mask=A					,     token_type_ids=A						)

           # test the shape of the logits
           _UpperCAmelCase      :     Any        =   tf.convert_to_tensor((2, 2_5)						)
           self.assertEqual(outputs.start_logits.shape					,     A						)
           self.assertEqual(outputs.end_logits.shape					,     A						)





"""simple docstring"""




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase    : Tuple							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       str								=			['pixel_values']



      def __init__(					self					:     List[Any]					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,     **A					:     List[Any]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     int        =   size if size is not None else {"shortest_edge": 2_2_4}
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     int        =   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase      :     Union[str, Any]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     str        =   do_resize
           _UpperCAmelCase      :     Union[str, Any]        =   size
           _UpperCAmelCase      :     Tuple        =   resample
           _UpperCAmelCase      :     List[Any]        =   do_center_crop
           _UpperCAmelCase      :     List[Any]        =   crop_size
           _UpperCAmelCase      :     Tuple        =   do_rescale
           _UpperCAmelCase      :     List[Any]        =   rescale_factor
           _UpperCAmelCase      :     Dict        =   do_normalize
           _UpperCAmelCase      :     Optional[Any]        =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           _UpperCAmelCase      :     Any        =   image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Union[str, Any]					,     ):
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                _UpperCAmelCase      :     Any        =   int((2_5_6 / 2_2_4) * size["shortest_edge"]						)
                _UpperCAmelCase      :     Union[str, Any]        =   get_resize_output_image_size(A					,     size=A					,     default_to_square=A						)
                _UpperCAmelCase      :     str        =   {"height": output_size[0], "width": output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                raise ValueError(
                    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'						)
           return resize(
               A					,     size=(size_dict["height"], size_dict["width"])					,     resample=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     Dict        =   get_size_dict(A						)
           if "height" not in size or "width" not in size:
                raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'						)
           return center_crop(A					,     size=(size["height"], size["width"])					,     data_format=A					,     **A						)



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



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



      def 						snake_case_ (					self					:     Tuple					,     A					:     ImageInput					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     PILImageResampling = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[float] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[TensorType] = None					,     A					:     ChannelDimension = ChannelDimension.FIRST					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     Dict        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     List[str]        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     str        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     Tuple        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Optional[int]        =   do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase      :     List[str]        =   image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase      :     Tuple        =   image_std if image_std is not None else self.image_std

           _UpperCAmelCase      :     Any        =   size if size is not None else self.size
           _UpperCAmelCase      :     Optional[int]        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     List[str]        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     List[str]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     List[Any]        =   make_list_of_images(A						)

           if not valid_images(A						):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."						)

           if do_resize and size is None:
                raise ValueError("Size must be specified if do_resize is True."						)

           if do_center_crop and crop_size is None:
                raise ValueError("Crop size must be specified if do_center_crop is True."						)

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

           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.
           _UpperCAmelCase      :     Any        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     str        =   [self.resize(A					,     A					,     A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     Dict        =   [self.center_crop(A					,     A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     Dict        =   [self.rescale(A					,     A						) for image in images]

           if do_normalize:
                _UpperCAmelCase      :     Optional[int]        =   [self.normalize(A					,     A					,     A						) for image in images]

           _UpperCAmelCase      :     Union[str, Any]        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     List[Any]        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)
