"""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  _A						:

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase=13		,       __lowerCAmelCase=7		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=99		,       __lowerCAmelCase=32		,       __lowerCAmelCase=2		,       __lowerCAmelCase=4		,       __lowerCAmelCase=37		,       __lowerCAmelCase="gelu"		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=512		,       __lowerCAmelCase=16		,       __lowerCAmelCase=2		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=3		,       __lowerCAmelCase=4		,       __lowerCAmelCase=None		,       __lowerCAmelCase=1000		,       ):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



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

            # convert bbox to numpy since TF does not support item assignment
            lowercase														=	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]:
                              lowercase														=	bbox[i, j, 3]
                              lowercase														=	bbox[i, j, 1]
                              lowercase														=	t
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                              lowercase														=	bbox[i, j, 2]
                              lowercase														=	bbox[i, j, 0]
                              lowercase														=	t
            lowercase														=	tf.convert_to_tensor(__lowerCAmelCase						)

            lowercase														=	None
            if self.use_input_mask:
                  lowercase														=	random_attention_mask([self.batch_size, self.seq_length]						)

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

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

            lowercase														=	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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	TFLayoutLMModel(config=__lowerCAmelCase						)

            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase						)
            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase		,       token_type_ids=__lowerCAmelCase						)
            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase						)
            self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)						)
            self.parent.assertEqual(result.pooler_output.shape		,       (self.batch_size, self.hidden_size)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	TFLayoutLMForMaskedLM(config=__lowerCAmelCase						)

            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase		,       labels=__lowerCAmelCase						)
            self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.vocab_size)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.num_labels
            lowercase														=	TFLayoutLMForSequenceClassification(config=__lowerCAmelCase						)

            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase						)
            self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_labels)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.num_labels
            lowercase														=	TFLayoutLMForTokenClassification(config=__lowerCAmelCase						)

            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase		,       labels=__lowerCAmelCase						)
            self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.num_labels)						)

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	TFLayoutLMForQuestionAnswering(config=__lowerCAmelCase						)

            lowercase														=	model(__lowerCAmelCase		,       __lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__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						):







            """simple docstring"""



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






@require_tf
class  _A						(       _A  ,    _A  ,    unittest.TestCase	):
      snake_case__			:   Dict							  =	(
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      snake_case__			:   List[str]							  =	(
          {
              "feature-extraction": TFLayoutLMModel,
              "fill-mask": TFLayoutLMForMaskedLM,
              "text-classification": TFLayoutLMForSequenceClassification,
              "token-classification": TFLayoutLMForTokenClassification,
              "zero-shot": TFLayoutLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      snake_case__			:   int							  =	False
      snake_case__			:   List[Any]							  =	True
      snake_case__			:   Any							  =	10

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFLayoutLMModelTester(self						)
            lowercase														=	ConfigTester(self		,       config_class=__lowerCAmelCase		,       hidden_size=37						)

      def 				A__       (						self						):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase						)

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase														=	TFLayoutLMModel.from_pretrained(__lowerCAmelCase						)
                  self.assertIsNotNone(__lowerCAmelCase						)







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







            """simple docstring"""



            pass






def 			UpperCAmelCase__       (				) ->    int:
      '''simple docstring'''


      lowercase														=	tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]		)  # noqa: E231
      lowercase														=	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
      lowercase														=	tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]		)  # noqa: E231
      lowercase														=	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)
      lowercase														=	tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]		)  # noqa: E231
      # fmt: on

      return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class  _A						(       unittest.TestCase	):

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""						)

            lowercase														=	prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=	model(input_ids=__lowerCAmelCase		,       bbox=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase						)

            # test the sequence output on [0, :3, :3]
            lowercase														=	tf.convert_to_tensor(
                [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]		,       )

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

            # test the pooled output on [1, :3]
            lowercase														=	tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]						)

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

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""		,       num_labels=2						)

            lowercase														=	prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=	model(
                input_ids=__lowerCAmelCase		,       bbox=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase		,       labels=tf.convert_to_tensor([1, 1]						)		,       )

            # test whether we get a loss as a scalar
            lowercase														=	outputs.loss
            lowercase														=	(2,)
            self.assertEqual(loss.shape		,       __lowerCAmelCase						)

            # test the shape of the logits
            lowercase														=	outputs.logits
            lowercase														=	(2, 2)
            self.assertEqual(logits.shape		,       __lowerCAmelCase						)

      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""		,       num_labels=13						)

            lowercase														=	prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=	model(
                input_ids=__lowerCAmelCase		,       bbox=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase		,       labels=__lowerCAmelCase						)

            # test the shape of the logits
            lowercase														=	outputs.logits
            lowercase														=	tf.convert_to_tensor((2, 25, 13)						)
            self.assertEqual(logits.shape		,       __lowerCAmelCase						)







      @slow
      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""						)

            lowercase														=	prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=	model(input_ids=__lowerCAmelCase		,       bbox=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       token_type_ids=__lowerCAmelCase						)

            # test the shape of the logits
            lowercase														=	tf.convert_to_tensor((2, 25)						)
            self.assertEqual(outputs.start_logits.shape		,       __lowerCAmelCase						)
            self.assertEqual(outputs.end_logits.shape		,       __lowerCAmelCase						)











"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



