





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 UpperCAmelCase_				:


  def __init__(							self ,__snake_case ,__snake_case=2 ,__snake_case=3 ,__snake_case=4 ,__snake_case=2 ,__snake_case=7 ,__snake_case=True ,__snake_case=True ,__snake_case=True ,__snake_case=True ,__snake_case=9_9 ,__snake_case=3_6 ,__snake_case=2 ,__snake_case=4 ,__snake_case=3_7 ,__snake_case="gelu" ,__snake_case=0.1 ,__snake_case=0.1 ,__snake_case=5_1_2 ,__snake_case=1_6 ,__snake_case=2 ,__snake_case=0.02 ,__snake_case=6 ,__snake_case=6 ,__snake_case=3 ,__snake_case=4 ,__snake_case=None ,__snake_case=1_0_0_0 ,):



       """simple docstring"""





       A_          =       parent
       A_          =       batch_size
       A_          =       num_channels
       A_          =       image_size
       A_          =       patch_size
       A_          =       is_training
       A_          =       use_input_mask
       A_          =       use_token_type_ids
       A_          =       use_labels
       A_          =       vocab_size
       A_          =       hidden_size
       A_          =       num_hidden_layers
       A_          =       num_attention_heads
       A_          =       intermediate_size
       A_          =       hidden_act
       A_          =       hidden_dropout_prob
       A_          =       attention_probs_dropout_prob
       A_          =       max_position_embeddings
       A_          =       type_vocab_size
       A_          =       type_sequence_label_size
       A_          =       initializer_range
       A_          =       coordinate_size
       A_          =       shape_size
       A_          =       num_labels
       A_          =       num_choices
       A_          =       scope
       A_          =       range_bbox

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ids_tensor([self.batch_size, self.text_seq_length] ,self.vocab_size )

       A_          =       ids_tensor([self.batch_size, self.text_seq_length, 4] ,self.range_bbox )
       A_          =       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]:
                      A_          =       bbox[i, j, 3]
                      A_          =       bbox[i, j, 1]
                      A_          =       tmp_coordinate
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                      A_          =       bbox[i, j, 2]
                      A_          =       bbox[i, j, 0]
                      A_          =       tmp_coordinate
       A_          =       tf.constant(snake_case_ )

       A_          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       A_          =       None
       if self.use_input_mask:
            A_          =       random_attention_mask([self.batch_size, self.text_seq_length] )

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

       A_          =       None
       A_          =       None
       if self.use_labels:
            A_          =       ids_tensor([self.batch_size] ,self.type_sequence_label_size )
            A_          =       ids_tensor([self.batch_size, self.text_seq_length] ,self.num_labels )

       A_          =       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 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       TFLayoutLMvaModel(config=snake_case_ )

       # text + image
       A_          =       model(snake_case_ ,pixel_values=snake_case_ ,training=snake_case_ )
       A_          =       model(
           snake_case_ ,bbox=snake_case_ ,pixel_values=snake_case_ ,attention_mask=snake_case_ ,token_type_ids=snake_case_ ,training=snake_case_ ,)
       A_          =       model(snake_case_ ,bbox=snake_case_ ,pixel_values=snake_case_ ,training=snake_case_ )

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

       # text only
       A_          =       model(snake_case_ ,training=snake_case_ )
       self.parent.assertEqual(
           result.last_hidden_state.shape ,(self.batch_size, self.text_seq_length, self.hidden_size) )

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


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       self.num_labels
       A_          =       TFLayoutLMvaForSequenceClassification(config=snake_case_ )
       A_          =       model(
           snake_case_ ,bbox=snake_case_ ,pixel_values=snake_case_ ,attention_mask=snake_case_ ,token_type_ids=snake_case_ ,labels=snake_case_ ,training=snake_case_ ,)
       self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       self.num_labels
       A_          =       TFLayoutLMvaForTokenClassification(config=snake_case_ )
       A_          =       model(
           snake_case_ ,bbox=snake_case_ ,pixel_values=snake_case_ ,attention_mask=snake_case_ ,token_type_ids=snake_case_ ,labels=snake_case_ ,training=snake_case_ ,)
       self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.text_seq_length, self.num_labels) )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       2
       A_          =       TFLayoutLMvaForQuestionAnswering(config=snake_case_ )
       A_          =       model(
           snake_case_ ,bbox=snake_case_ ,pixel_values=snake_case_ ,attention_mask=snake_case_ ,token_type_ids=snake_case_ ,start_positions=snake_case_ ,end_positions=snake_case_ ,training=snake_case_ ,)
       self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.prepare_config_and_inputs()
       ((A_)   ,    (A_)   ,    (A_)   ,    (A_)   ,    (A_)   ,    (A_)   ,    (A_)   ,    (A_))          =       config_and_inputs
       A_          =       {
           '''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 UpperCAmelCase_				( _SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE     ,		unittest.TestCase ):
  __lowerCAmelCase   :						Optional[int]							=   (
      (
          TFLayoutLMvaModel,
          TFLayoutLMvaForQuestionAnswering,
          TFLayoutLMvaForSequenceClassification,
          TFLayoutLMvaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  __lowerCAmelCase   :						Any							=   (
      {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
      if is_tf_available()
      else {}
  )

  __lowerCAmelCase   :						List[str]							=   False
  __lowerCAmelCase   :						int							=   False
  __lowerCAmelCase   :						Union[str, Any]							=   False


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ):



       """simple docstring"""





       return True


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case=False ):



       """simple docstring"""





       A_          =       copy.deepcopy(snake_case_ )

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

       if return_labels:
            if model_class in get_values(snake_case_ ):
                 A_          =       tf.ones(self.model_tester.batch_size ,dtype=tf.intaa )
            elif model_class in get_values(snake_case_ ):
                 A_          =       tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
                 A_          =       tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
            elif model_class in get_values(snake_case_ ):
                 A_          =       tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
            elif model_class in get_values(snake_case_ ):
                 A_          =       tf.zeros(
                     (self.model_tester.batch_size, self.model_tester.text_seq_length) ,dtype=tf.intaa )

       return inputs_dict


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       TFLayoutLMvaModelTester(self )
       A_          =       ConfigTester(self ,config_class=snake_case_ ,hidden_size=3_7 )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       self.config_tester.run_common_tests()


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





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

                 # Test that model correctly compute the loss with kwargs
                 A_          =       self._prepare_for_class(inputs_dict.copy() ,snake_case_ ,return_labels=snake_case_ )
                 A_          =       prepared_for_class.pop('''input_ids''' )

                 A_          =       model(snake_case_ ,**snake_case_ )[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
                 A_          =       self._prepare_for_class(inputs_dict.copy() ,snake_case_ ,return_labels=snake_case_ )
                 A_          =       prepared_for_class.pop('''input_ids''' )
                 if "labels" in prepared_for_class:
                      A_          =       prepared_for_class['''labels'''].numpy()
                      if len(labels.shape ) > 1 and labels.shape[1] != 1:
                           A_          =       -1_0_0
                           A_          =       tf.convert_to_tensor(snake_case_ )
                           A_          =       model(snake_case_ ,**snake_case_ )[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
                 A_          =       self._prepare_for_class(inputs_dict.copy() ,snake_case_ ,return_labels=snake_case_ )
                 A_          =       model(snake_case_ )[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
                 A_          =       self._prepare_for_class(inputs_dict.copy() ,snake_case_ ,return_labels=snake_case_ )

                 # Get keys that were added with the _prepare_for_class function
                 A_          =       prepared_for_class.keys() - inputs_dict.keys()
                 A_          =       inspect.signature(model.call ).parameters
                 A_          =       list(signature.keys() )

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

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

                 for index, value in sorted_tuple_index_mapping:
                      A_          =       prepared_for_class[value]

                 A_          =       tuple(snake_case_ )

                 # Send to model
                 A_          =       model(tuple_input[:-1] )[0]

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       (
           (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    						
       )          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       (
           (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    						
       )          =       self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            A_          =       type
            self.model_tester.create_and_check_model(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       (
           (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    						
       )          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(
           snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       (
           (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    						
       )          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(
           snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       (
           (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    (
           A_						
       )   ,    						
       )          =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(
           snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A_          =       TFLayoutLMvaModel.from_pretrained(snake_case_ )
            self.assertIsNotNone(snake_case_ )




def      UpperCAmelCase_    (    )	->				Any:
     '''simple docstring'''


     A_          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image





@require_tf
class UpperCAmelCase_				( unittest.TestCase ):


  @cached_property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return LayoutLMvaImageProcessor(apply_ocr=snake_case_ ) if is_vision_available() else None




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' )

       A_          =       self.default_image_processor
       A_          =       prepare_img()
       A_          =       image_processor(images=snake_case_ ,return_tensors='''tf''' ).pixel_values

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

       # forward pass
       A_          =       model(input_ids=snake_case_ ,bbox=snake_case_ ,pixel_values=snake_case_ ,training=snake_case_ )

       # verify the logits
       A_          =       (1, 1_9_9, 7_6_8)
       self.assertEqual(outputs.last_hidden_state.shape ,snake_case_ )

       A_          =       tf.constant(
           [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )

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








from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).most_common(1						)[0][0]
					return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))




