


"""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 						snake_case_:






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






     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		str				=      ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size )

           # convert bbox to numpy since TF does not support item assignment
           lowerCAmelCase    :		str				=      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]:
                             lowerCAmelCase    :		str				=      bbox[i, j, 3]
                             lowerCAmelCase    :		Tuple				=      bbox[i, j, 1]
                             lowerCAmelCase    :		Union[str, Any]				=      t
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                             lowerCAmelCase    :		Dict				=      bbox[i, j, 2]
                             lowerCAmelCase    :		int				=      bbox[i, j, 0]
                             lowerCAmelCase    :		str				=      t
           lowerCAmelCase    :		str				=      tf.convert_to_tensor(UpperCamelCase_ )

           lowerCAmelCase    :		Tuple				=      None
           if self.use_input_mask:
                 lowerCAmelCase    :		Optional[int]				=      random_attention_mask([self.batch_size, self.seq_length] )

           lowerCAmelCase    :		List[str]				=      None
           if self.use_token_type_ids:
                 lowerCAmelCase    :		int				=      ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size )

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

           lowerCAmelCase    :		Dict				=      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    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    int ):
           lowerCAmelCase    :		Tuple				=      TFLayoutLMModel(config=UpperCamelCase_ )

           lowerCAmelCase    :		Any				=      model(UpperCamelCase_    , UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      model(UpperCamelCase_    , UpperCamelCase_    , token_type_ids=UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      model(UpperCamelCase_    , UpperCamelCase_ )
           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    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[str] ):
           lowerCAmelCase    :		Any				=      TFLayoutLMForMaskedLM(config=UpperCamelCase_ )

           lowerCAmelCase    :		Tuple				=      model(UpperCamelCase_    , UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size) )






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		Optional[int]				=      self.num_labels
           lowerCAmelCase    :		Any				=      TFLayoutLMForSequenceClassification(config=UpperCamelCase_ )

           lowerCAmelCase    :		Optional[Any]				=      model(UpperCamelCase_    , UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels) )






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Dict ):
           lowerCAmelCase    :		Tuple				=      self.num_labels
           lowerCAmelCase    :		Union[str, Any]				=      TFLayoutLMForTokenClassification(config=UpperCamelCase_ )

           lowerCAmelCase    :		List[str]				=      model(UpperCamelCase_    , UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , labels=UpperCamelCase_ )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels) )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[int] ):
           lowerCAmelCase    :		Dict				=      TFLayoutLMForQuestionAnswering(config=UpperCamelCase_ )

           lowerCAmelCase    :		int				=      model(UpperCamelCase_    , UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_ )
           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    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		int				=      self.prepare_config_and_inputs()
           (
               (
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					
           )    :		Union[str, Any]				=      config_and_inputs
           lowerCAmelCase    :		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 						snake_case_(				a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     __UpperCamelCase     =    (
         {
             '''feature-extraction''': TFLayoutLMModel,
             '''fill-mask''': TFLayoutLMForMaskedLM,
             '''text-classification''': TFLayoutLMForSequenceClassification,
             '''token-classification''': TFLayoutLMForTokenClassification,
             '''zero-shot''': TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __UpperCamelCase     =    False
     __UpperCamelCase     =    True
     __UpperCamelCase     =    10






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		str				=      TFLayoutLMModelTester(self )
           lowerCAmelCase    :		str				=      ConfigTester(self    , config_class=UpperCamelCase_    , hidden_size=3_7 )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           self.config_tester.run_common_tests()






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		List[Any]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		List[str]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		List[Any]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Tuple				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_ )






     @slow
     def    lowerCamelCase__   (		self     :    int ):
           for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase    :		Tuple				=      TFLayoutLMModel.from_pretrained(UpperCamelCase_ )
                 self.assertIsNotNone(UpperCamelCase_ )






     @unittest.skip('''Onnx compliancy broke with TF 2.10''' )
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           pass



def    _snake_case  (  ):
      # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
      # fmt: off
      lowerCAmelCase    :		Dict				=      tf.convert_to_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]]	)  # noqa: E231
      lowerCAmelCase    :		Tuple				=      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
      lowerCAmelCase    :		str				=      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],[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]]]	)  # noqa: E231
      lowerCAmelCase    :		Union[str, Any]				=      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)
      lowerCAmelCase    :		str				=      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 						snake_case_(				unittest.TestCase	):






     @slow
     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		Union[str, Any]				=      TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' )

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase    :		Union[str, Any]				=      model(input_ids=UpperCamelCase_    , bbox=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_ )

           # test the sequence output on [0, :3, :3]
           lowerCAmelCase    :		List[str]				=      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]    , UpperCamelCase_    , atol=1E-3 ) )

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

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






     @slow
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           # initialize model with randomly initialized sequence classification head
           lowerCAmelCase    :		List[str]				=      TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    , num_labels=2 )

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase    :		Optional[Any]				=      model(
               input_ids=UpperCamelCase_    , bbox=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , labels=tf.convert_to_tensor([1, 1] )    , )

           # test whether we get a loss as a scalar
           lowerCAmelCase    :		str				=      outputs.loss
           lowerCAmelCase    :		Tuple				=      (2,)
           self.assertEqual(loss.shape    , UpperCamelCase_ )

           # test the shape of the logits
           lowerCAmelCase    :		Union[str, Any]				=      outputs.logits
           lowerCAmelCase    :		List[Any]				=      (2, 2)
           self.assertEqual(logits.shape    , UpperCamelCase_ )






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           # initialize model with randomly initialized token classification head
           lowerCAmelCase    :		List[Any]				=      TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    , num_labels=1_3 )

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase    :		Any				=      model(
               input_ids=UpperCamelCase_    , bbox=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , labels=UpperCamelCase_ )

           # test the shape of the logits
           lowerCAmelCase    :		Union[str, Any]				=      outputs.logits
           lowerCAmelCase    :		Dict				=      tf.convert_to_tensor((2, 2_5, 1_3) )
           self.assertEqual(logits.shape    , UpperCamelCase_ )






     @slow
     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           # initialize model with randomly initialized token classification head
           lowerCAmelCase    :		str				=      TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' )

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		str				=      prepare_layoutlm_batch_inputs()

           # forward pass
           lowerCAmelCase    :		Dict				=      model(input_ids=UpperCamelCase_    , bbox=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_ )

           # test the shape of the logits
           lowerCAmelCase    :		List[Any]				=      tf.convert_to_tensor((2, 2_5) )
           self.assertEqual(outputs.start_logits.shape    , UpperCamelCase_ )
           self.assertEqual(outputs.end_logits.shape    , UpperCamelCase_ )






"""simple docstring"""





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

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    int = 3_2    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Union[int, float] = 1 / 2_5_5    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[int]=7    , UpperCamelCase_     :    int=3_0    , UpperCamelCase_     :    str=4_0_0    , UpperCamelCase_     :    List[Any]=3    , ):
           lowerCAmelCase    :		Union[str, Any]				=      parent
           lowerCAmelCase    :		Union[str, Any]				=      do_resize
           lowerCAmelCase    :		List[str]				=      size if size is not None else {'''shortest_edge''': 2_8_8}
           lowerCAmelCase    :		int				=      size_divisor
           lowerCAmelCase    :		List[str]				=      do_rescale
           lowerCAmelCase    :		Optional[Any]				=      rescale_factor
           lowerCAmelCase    :		Dict				=      do_normalize
           lowerCAmelCase    :		Any				=      do_center_crop
           lowerCAmelCase    :		Union[str, Any]				=      image_mean
           lowerCAmelCase    :		Optional[Any]				=      image_std
           lowerCAmelCase    :		Union[str, Any]				=      do_pad
           lowerCAmelCase    :		Union[str, Any]				=      batch_size
           lowerCAmelCase    :		Any				=      num_channels
           lowerCAmelCase    :		Union[str, Any]				=      min_resolution
           lowerCAmelCase    :		int				=      max_resolution






     def    lowerCamelCase__   (		self     :    Dict ):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[str]=False ):
           if not batched:
                 lowerCAmelCase    :		Dict				=      self.size['''shortest_edge''']
                 lowerCAmelCase    :		Dict				=      image_inputs[0]
                 if isinstance(UpperCamelCase_    , Image.Image ):
                       lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      image.size
                 else:
                       lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      image.shape[1], image.shape[2]
                 lowerCAmelCase    :		Union[str, Any]				=      size / min(UpperCamelCase_    , UpperCamelCase_ )
                 if h < w:
                       lowerCAmelCase,					lowerCAmelCase    :		Dict				=      size, scale * w
                 else:
                       lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      scale * h, size

                 lowerCAmelCase    :		List[Any]				=      int((1_3_3_3 / 8_0_0) * size )
                 if max(UpperCamelCase_    , UpperCamelCase_ ) > max_size:
                       lowerCAmelCase    :		int				=      max_size / max(UpperCamelCase_    , UpperCamelCase_ )
                       lowerCAmelCase    :		str				=      newh * scale
                       lowerCAmelCase    :		Tuple				=      neww * scale

                 lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      int(newh + 0.5 ), int(neww + 0.5 )
                 lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      (
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 lowerCAmelCase    :		Optional[int]				=      []
                 for image in image_inputs:
                       lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 lowerCAmelCase    :		Union[str, Any]				=      max(UpperCamelCase_    , key=lambda UpperCamelCase_ : item[0] )[0]
                 lowerCAmelCase    :		Union[str, Any]				=      max(UpperCamelCase_    , key=lambda UpperCamelCase_ : item[1] )[1]

           return expected_height, expected_width



@require_torch
@require_vision
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    BridgeTowerImageProcessor if is_vision_available() else None






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Optional[int]				=      BridgeTowerImageProcessingTester(self )






     @property
     def    lowerCamelCase__   (		self     :    List[str] ):
           return self.image_processor_tester.prepare_image_processor_dict()






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Optional[Any]				=      self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(UpperCamelCase_    , '''image_mean''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''image_std''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''do_normalize''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''do_resize''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''size''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''size_divisor''' ) )






     def    lowerCamelCase__   (		self     :    int ):
           pass






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           # Initialize image processor
           lowerCAmelCase    :		str				=      self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           lowerCAmelCase    :		Optional[int]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , Image.Image )

           # Test not batched input
           lowerCAmelCase    :		Optional[int]				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

           # Test batched
           lowerCAmelCase    :		Dict				=      image_processing(UpperCamelCase_    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		int				=      self.image_processor_tester.get_expected_values(UpperCamelCase_    , batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    , )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           # Initialize image processor
           lowerCAmelCase    :		Tuple				=      self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           lowerCAmelCase    :		List[Any]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_    , numpify=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , np.ndarray )

           # Test not batched input
           lowerCAmelCase    :		Any				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

           # Test batched
           lowerCAmelCase    :		Tuple				=      image_processing(UpperCamelCase_    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		str				=      self.image_processor_tester.get_expected_values(UpperCamelCase_    , batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    , )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           # Initialize image processor
           lowerCAmelCase    :		Union[str, Any]				=      self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           lowerCAmelCase    :		List[str]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_    , torchify=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , torch.Tensor )

           # Test not batched input
           lowerCAmelCase    :		Any				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

           # Test batched
           lowerCAmelCase    :		str				=      image_processing(UpperCamelCase_    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		str				=      self.image_processor_tester.get_expected_values(UpperCamelCase_    , batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    , )


