






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       TFLayoutLMvaForQuestionAnswering,
       TFLayoutLMvaForSequenceClassification,
       TFLayoutLMvaForTokenClassification,
       TFLayoutLMvaModel,
   )

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=4  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=9_9  ,      _lowerCamelCase=3_6  ,      _lowerCamelCase=2  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=6  ,      _lowerCamelCase=6  ,      _lowerCamelCase=3  ,      _lowerCamelCase=4  ,      _lowerCamelCase=None  ,      _lowerCamelCase=1_0_0_0  ,      ):
         UpperCamelCase_: Any   				=    parent
         UpperCamelCase_: Tuple   				=    batch_size
         UpperCamelCase_: Tuple   				=    num_channels
         UpperCamelCase_: Tuple   				=    image_size
         UpperCamelCase_: List[Any]   				=    patch_size
         UpperCamelCase_: List[str]   				=    is_training
         UpperCamelCase_: Optional[Any]   				=    use_input_mask
         UpperCamelCase_: Optional[int]   				=    use_token_type_ids
         UpperCamelCase_: List[str]   				=    use_labels
         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: List[str]   				=    hidden_size
         UpperCamelCase_: Union[str, Any]   				=    num_hidden_layers
         UpperCamelCase_: Dict   				=    num_attention_heads
         UpperCamelCase_: str   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: int   				=    hidden_dropout_prob
         UpperCamelCase_: List[str]   				=    attention_probs_dropout_prob
         UpperCamelCase_: str   				=    max_position_embeddings
         UpperCamelCase_: Dict   				=    type_vocab_size
         UpperCamelCase_: Dict   				=    type_sequence_label_size
         UpperCamelCase_: str   				=    initializer_range
         UpperCamelCase_: Any   				=    coordinate_size
         UpperCamelCase_: List[str]   				=    shape_size
         UpperCamelCase_: List[str]   				=    num_labels
         UpperCamelCase_: Any   				=    num_choices
         UpperCamelCase_: Any   				=    scope
         UpperCamelCase_: List[Any]   				=    range_bbox

         # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
         UpperCamelCase_: Any   				=    text_seq_length
         UpperCamelCase_: Tuple   				=    (image_size // patch_size) ** 2 + 1
         UpperCamelCase_: int   				=    self.text_seq_length + self.image_seq_length





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    ids_tensor([self.batch_size, self.text_seq_length]  ,      self.vocab_size							)

         UpperCamelCase_: List[str]   				=    ids_tensor([self.batch_size, self.text_seq_length, 4]  ,      self.range_bbox							)
         UpperCamelCase_: List[str]   				=    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_: Dict   				=    bbox[i, j, 3]
               UpperCamelCase_: Union[str, Any]   				=    bbox[i, j, 1]
               UpperCamelCase_: Optional[Any]   				=    tmp_coordinate
             if bbox[i, j, 2] < bbox[i, j, 0]:
               UpperCamelCase_: Union[str, Any]   				=    bbox[i, j, 2]
               UpperCamelCase_: Optional[Any]   				=    bbox[i, j, 0]
               UpperCamelCase_: Tuple   				=    tmp_coordinate
         UpperCamelCase_: Tuple   				=    tf.constant(_lowerCamelCase							)

         UpperCamelCase_: Optional[Any]   				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCamelCase_: str   				=    None
         if self.use_input_mask:
           UpperCamelCase_: List[str]   				=    random_attention_mask([self.batch_size, self.text_seq_length]							)

         UpperCamelCase_: str   				=    None
         if self.use_token_type_ids:
           UpperCamelCase_: Tuple   				=    ids_tensor([self.batch_size, self.text_seq_length]  ,      self.type_vocab_size							)

         UpperCamelCase_: int   				=    None
         UpperCamelCase_: Optional[Any]   				=    None
         if self.use_labels:
           UpperCamelCase_: int   				=    ids_tensor([self.batch_size]  ,      self.type_sequence_label_size							)
           UpperCamelCase_: Any   				=    ids_tensor([self.batch_size, self.text_seq_length]  ,      self.num_labels							)

         UpperCamelCase_: List[Any]   				=    LayoutLMvaConfig(
             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  ,      coordinate_size=self.coordinate_size  ,      shape_size=self.shape_size  ,      input_size=self.image_size  ,      patch_size=self.patch_size  ,      )

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Dict   				=    TFLayoutLMvaModel(config=_lowerCamelCase							)

         # text + image
         UpperCamelCase_: Tuple   				=    model(_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      training=_lowerCamelCase							)
         UpperCamelCase_: Any   				=    model(
             _lowerCamelCase  ,      bbox=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase  ,      training=_lowerCamelCase  ,      )
         UpperCamelCase_: Union[str, Any]   				=    model(_lowerCamelCase  ,      bbox=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      training=_lowerCamelCase							)

         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)							)

         # text only
         UpperCamelCase_: Tuple   				=    model(_lowerCamelCase  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(
             result.last_hidden_state.shape  ,      (self.batch_size, self.text_seq_length, self.hidden_size)							)

         # image only
         UpperCamelCase_: Dict   				=    model({'pixel_values': pixel_values}  ,      training=_lowerCamelCase							)
         self.parent.assertEqual(
             result.last_hidden_state.shape  ,      (self.batch_size, self.image_seq_length, self.hidden_size)							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[int]   				=    self.num_labels
         UpperCamelCase_: Optional[Any]   				=    TFLayoutLMvaForSequenceClassification(config=_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    model(
             _lowerCamelCase  ,      bbox=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase  ,      labels=_lowerCamelCase  ,      training=_lowerCamelCase  ,      )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    self.num_labels
         UpperCamelCase_: int   				=    TFLayoutLMvaForTokenClassification(config=_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    model(
             _lowerCamelCase  ,      bbox=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase  ,      labels=_lowerCamelCase  ,      training=_lowerCamelCase  ,      )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.text_seq_length, self.num_labels)							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    2
         UpperCamelCase_: Optional[Any]   				=    TFLayoutLMvaForQuestionAnswering(config=_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    model(
             _lowerCamelCase  ,      bbox=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase  ,      start_positions=_lowerCamelCase  ,      end_positions=_lowerCamelCase  ,      training=_lowerCamelCase  ,      )
         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							):
         UpperCamelCase_: Optional[int]   				=    self.prepare_config_and_inputs()
         ((UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)				,(UpperCamelCase_)): Optional[Any]   				=    config_and_inputs
         UpperCamelCase_: Union[str, Any]   				=    {
             'input_ids': input_ids,
             'bbox': bbox,
             'pixel_values': pixel_values,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict







@require_tf
class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Any									=(
           (
               TFLayoutLMvaModel,
               TFLayoutLMvaForQuestionAnswering,
               TFLayoutLMvaForSequenceClassification,
               TFLayoutLMvaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       a    :		Any									=(
           {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
           if is_tf_available()
           else {}
       )

       a    :		List[Any]									=False
       a    :		Optional[Any]									=False
       a    :		List[Any]									=False





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         return True





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=False							):
         UpperCamelCase_: Tuple   				=    copy.deepcopy(_lowerCamelCase							)

         if model_class in get_values(_lowerCamelCase							):
           UpperCamelCase_: int   				=    {
               k: tf.tile(tf.expand_dims(_lowerCamelCase  ,      1							)  ,      (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
               if isinstance(_lowerCamelCase  ,      tf.Tensor							) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

         if return_labels:
           if model_class in get_values(_lowerCamelCase							):
             UpperCamelCase_: Optional[Any]   				=    tf.ones(self.model_tester.batch_size  ,      dtype=tf.intaa							)
           elif model_class in get_values(_lowerCamelCase							):
             UpperCamelCase_: Tuple   				=    tf.zeros(self.model_tester.batch_size  ,      dtype=tf.intaa							)
             UpperCamelCase_: int   				=    tf.zeros(self.model_tester.batch_size  ,      dtype=tf.intaa							)
           elif model_class in get_values(_lowerCamelCase							):
             UpperCamelCase_: Tuple   				=    tf.zeros(self.model_tester.batch_size  ,      dtype=tf.intaa							)
           elif model_class in get_values(_lowerCamelCase							):
             UpperCamelCase_: List[Any]   				=    tf.zeros(
                 (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,      dtype=tf.intaa							)

         return inputs_dict





       def       _a   ( self							):
         UpperCamelCase_: str   				=    TFLayoutLMvaModelTester(self							)
         UpperCamelCase_: Optional[int]   				=    ConfigTester(self  ,      config_class=_lowerCamelCase  ,      hidden_size=3_7							)





       def       _a   ( self							):
         self.config_tester.run_common_tests()





       def       _a   ( self							):
         UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           UpperCamelCase_: List[Any]   				=    model_class(_lowerCamelCase							)
           if getattr(_lowerCamelCase  ,      'hf_compute_loss'  ,      _lowerCamelCase							):
             # The number of elements in the loss should be the same as the number of elements in the label
             UpperCamelCase_: List[str]   				=    self._prepare_for_class(inputs_dict.copy()  ,      _lowerCamelCase  ,      return_labels=_lowerCamelCase							)
             UpperCamelCase_: List[Any]   				=    prepared_for_class[
                 sorted(prepared_for_class.keys() - inputs_dict.keys()  ,      reverse=_lowerCamelCase							)[0]
             ]
             UpperCamelCase_: int   				=    added_label.shape.as_list()[:1]

             # Test that model correctly compute the loss with kwargs
             UpperCamelCase_: str   				=    self._prepare_for_class(inputs_dict.copy()  ,      _lowerCamelCase  ,      return_labels=_lowerCamelCase							)
             UpperCamelCase_: Dict   				=    prepared_for_class.pop('input_ids'							)

             UpperCamelCase_: Union[str, Any]   				=    model(_lowerCamelCase  ,      **_lowerCamelCase							)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

             # Test that model correctly compute the loss when we mask some positions
             UpperCamelCase_: Optional[int]   				=    self._prepare_for_class(inputs_dict.copy()  ,      _lowerCamelCase  ,      return_labels=_lowerCamelCase							)
             UpperCamelCase_: Optional[int]   				=    prepared_for_class.pop('input_ids'							)
             if "labels" in prepared_for_class:
               UpperCamelCase_: Any   				=    prepared_for_class['labels'].numpy()
               if len(labels.shape							) > 1 and labels.shape[1] != 1:
                 UpperCamelCase_: List[str]   				=    -1_0_0
                 UpperCamelCase_: List[Any]   				=    tf.convert_to_tensor(_lowerCamelCase							)
                 UpperCamelCase_: List[Any]   				=    model(_lowerCamelCase  ,      **_lowerCamelCase							)[0]
                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)
                 self.assertTrue(not np.any(np.isnan(loss.numpy()							)							)							)

                # Test that model correctly compute the loss with a dict
             UpperCamelCase_: int   				=    self._prepare_for_class(inputs_dict.copy()  ,      _lowerCamelCase  ,      return_labels=_lowerCamelCase							)
             UpperCamelCase_: Union[str, Any]   				=    model(_lowerCamelCase							)[0]
             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)

             # Test that model correctly compute the loss with a tuple
             UpperCamelCase_: List[str]   				=    self._prepare_for_class(inputs_dict.copy()  ,      _lowerCamelCase  ,      return_labels=_lowerCamelCase							)

             # Get keys that were added with the _prepare_for_class function
             UpperCamelCase_: Dict   				=    prepared_for_class.keys() - inputs_dict.keys()
             UpperCamelCase_: Any   				=    inspect.signature(model.call							).parameters
             UpperCamelCase_: Union[str, Any]   				=    list(signature.keys()							)

             # Create a dictionary holding the location of the tensors in the tuple
             UpperCamelCase_: List[Any]   				=    {0: 'input_ids'}
             for label_key in label_keys:
               UpperCamelCase_: Optional[Any]   				=    signature_names.index(_lowerCamelCase							)
               UpperCamelCase_: List[Any]   				=    label_key
             UpperCamelCase_: Union[str, Any]   				=    sorted(tuple_index_mapping.items()							)
             # Initialize a list with their default values, update the values and convert to a tuple
             UpperCamelCase_: Union[str, Any]   				=    []

             for name in signature_names:
               if name != "kwargs":
                 list_input.append(signature[name].default							)

             for index, value in sorted_tuple_index_mapping:
               UpperCamelCase_: List[Any]   				=    prepared_for_class[value]

             UpperCamelCase_: Union[str, Any]   				=    tuple(_lowerCamelCase							)

             # Send to model
             UpperCamelCase_: List[str]   				=    model(tuple_input[:-1]							)[0]

             self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]							)





       def       _a   ( self							):
         (
             (
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,
         ): List[str]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         (
             (
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,
         ): Dict   				=    self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
           UpperCamelCase_: Dict   				=    type
           self.model_tester.create_and_check_model(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         (
             (
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,
         ): Any   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(
             _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         (
             (
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,
         ): Dict   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(
             _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       def       _a   ( self							):
         (
             (
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,(
             UpperCamelCase_
         )				,
         ): Optional[int]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(
             _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							)





       @slow
       def       _a   ( self							):
         for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase_: str   				=    TFLayoutLMvaModel.from_pretrained(_lowerCamelCase							)
           self.assertIsNotNone(_lowerCamelCase							)







def   snake_case ()	->    Any:
  UpperCamelCase_: str   				=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image




@require_tf
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @cached_property
       def       _a   ( self							):
         return LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase							) if is_vision_available() else None





       @slow
       def       _a   ( self							):
         UpperCamelCase_: str   				=    TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'							)

         UpperCamelCase_: Optional[Any]   				=    self.default_image_processor
         UpperCamelCase_: List[Any]   				=    prepare_img()
         UpperCamelCase_: Union[str, Any]   				=    image_processor(images=_lowerCamelCase  ,      return_tensors='tf'							).pixel_values

         UpperCamelCase_: List[Any]   				=    tf.constant([[1, 2]]							)
         UpperCamelCase_: Any   				=    tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]							)  ,      axis=0							)

         # forward pass
         UpperCamelCase_: str   				=    model(input_ids=_lowerCamelCase  ,      bbox=_lowerCamelCase  ,      pixel_values=_lowerCamelCase  ,      training=_lowerCamelCase							)

         # verify the logits
         UpperCamelCase_: Dict   				=    (1, 1_9_9, 7_6_8)
         self.assertEqual(outputs.last_hidden_state.shape  ,      _lowerCamelCase							)

         UpperCamelCase_: Optional[int]   				=    tf.constant(
             [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]							)

         self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]  ,      _lowerCamelCase  ,      atol=1e-4							)							)







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 (
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_		:		int		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Any   				=    get_size_dict(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: Optional[int]   				=    do_resize
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Dict   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    do_center_crop
         UpperCamelCase_: Tuple   				=    crop_size
         UpperCamelCase_: Optional[int]   				=    size
         UpperCamelCase_: Dict   				=    resample
         UpperCamelCase_: Tuple   				=    rescale_factor
         UpperCamelCase_: Tuple   				=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if "shortest_edge" in size:
           UpperCamelCase_: str   				=    get_resize_output_image_size(_lowerCamelCase  ,      size=size['shortest_edge']  ,      default_to_square=_lowerCamelCase							)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
           UpperCamelCase_: Any   				=    (size['height'], size['width'])
         else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''							)
         return resize(_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase							)
         if "height" not in size or "width" not in size:
           raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
         return center_crop(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase							):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: str   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Dict   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: List[str]   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    resample if resample is not None else self.resample
         UpperCamelCase_: str   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Dict   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: Dict   				=    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[Any]   				=    get_size_dict(_lowerCamelCase							)

         if not is_batched(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    [images]

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

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

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

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

         # All transformations expect numpy arrays.
         UpperCamelCase_: List[str]   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: List[str]   				=    [self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: List[str]   				=    [self.center_crop(image=_lowerCamelCase  ,      size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: int   				=    [self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    [self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: Optional[int]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)