


from __future__ import annotations

import unittest

import numpy as np

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

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


if is_tf_available():
   import tensorflow as tf

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




class    UpperCAmelCase					:




   def __init__(						self							:		Dict       ,       __magic_name__							:		Dict       ,       __magic_name__							:		List[Any]=1_3       ,       __magic_name__							:		Optional[Any]=7       ,       __magic_name__							:		Union[str, Any]=True       ,       __magic_name__							:		int=True       ,       __magic_name__							:		Any=True       ,       __magic_name__							:		Union[str, Any]=True       ,       __magic_name__							:		str=9_9       ,       __magic_name__							:		Any=3_2       ,       __magic_name__							:		Dict=2       ,       __magic_name__							:		List[Any]=4       ,       __magic_name__							:		List[str]=3_7       ,       __magic_name__							:		str="gelu"       ,       __magic_name__							:		int=0.1       ,       __magic_name__							:		List[str]=0.1       ,       __magic_name__							:		str=5_1_2       ,       __magic_name__							:		Union[str, Any]=1_6       ,       __magic_name__							:		Optional[int]=2       ,       __magic_name__							:		Dict=0.02       ,       __magic_name__							:		Any=3       ,       __magic_name__							:		Dict=4       ,       __magic_name__							:		Any=None       ,       __magic_name__							:		Optional[int]=1_0_0_0       ,       ):







      """simple docstring"""







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




   def   lowerCamelCase_					(						self							:		Dict						):







      """simple docstring"""







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

      # convert bbox to numpy since TF does not support item assignment
      UpperCamelCase         =					ids_tensor([self.batch_size, self.seq_length, 4]       ,       self.range_bbox						).numpy()
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]						):
         for j in range(bbox.shape[1]						):
            if bbox[i, j, 3] < bbox[i, j, 1]:
               UpperCamelCase         =					bbox[i, j, 3]
               UpperCamelCase         =					bbox[i, j, 1]
               UpperCamelCase         =					t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               UpperCamelCase         =					bbox[i, j, 2]
               UpperCamelCase         =					bbox[i, j, 0]
               UpperCamelCase         =					t
      UpperCamelCase         =					tf.convert_to_tensor(__magic_name__						)

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

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

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

      UpperCamelCase         =					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       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Union[str, Any]						):







      """simple docstring"""







      UpperCamelCase         =					TFLayoutLMModel(config=__magic_name__						)

      UpperCamelCase         =					model(__magic_name__       ,       __magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)
      UpperCamelCase         =					model(__magic_name__       ,       __magic_name__       ,       token_type_ids=__magic_name__						)
      UpperCamelCase         =					model(__magic_name__       ,       __magic_name__						)
      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							:		int       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		int       ,       __magic_name__							:		int       ,       __magic_name__							:		Any       ,       __magic_name__							:		Any       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		int						):







      """simple docstring"""







      UpperCamelCase         =					TFLayoutLMForMaskedLM(config=__magic_name__						)

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




   def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		int       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Union[str, Any]						):







      """simple docstring"""







      UpperCamelCase         =					self.num_labels
      UpperCamelCase         =					TFLayoutLMForSequenceClassification(config=__magic_name__						)

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




   def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Any       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		int       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		str						):







      """simple docstring"""







      UpperCamelCase         =					self.num_labels
      UpperCamelCase         =					TFLayoutLMForTokenClassification(config=__magic_name__						)

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




   def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		Any       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		Optional[Any]						):







      """simple docstring"""







      UpperCamelCase         =					TFLayoutLMForQuestionAnswering(config=__magic_name__						)

      UpperCamelCase         =					model(__magic_name__       ,       __magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)
      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							:		Dict						):







      """simple docstring"""







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




@require_tf
class    UpperCAmelCase					(       __snake_case      , __snake_case      , unittest.TestCase    ):
   lowercase						= (
       (
           TFLayoutLMModel,
           TFLayoutLMForMaskedLM,
           TFLayoutLMForTokenClassification,
           TFLayoutLMForSequenceClassification,
           TFLayoutLMForQuestionAnswering,
       )
       if is_tf_available()
       else ()
   )
   lowercase						= (
       {
           """feature-extraction""": TFLayoutLMModel,
           """fill-mask""": TFLayoutLMForMaskedLM,
           """text-classification""": TFLayoutLMForSequenceClassification,
           """token-classification""": TFLayoutLMForTokenClassification,
           """zero-shot""": TFLayoutLMForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   lowercase						= False
   lowercase						= True
   lowercase						= 1_0




   def   lowerCamelCase_					(						self							:		Tuple						):







      """simple docstring"""







      UpperCamelCase         =					TFLayoutLMModelTester(self						)
      UpperCamelCase         =					ConfigTester(self       ,       config_class=__magic_name__       ,       hidden_size=3_7						)




   def   lowerCamelCase_					(						self							:		int						):







      """simple docstring"""







      self.config_tester.run_common_tests()




   def   lowerCamelCase_					(						self							:		Optional[Any]						):







      """simple docstring"""







      UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__						)




   def   lowerCamelCase_					(						self							:		str						):







      """simple docstring"""







      UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__magic_name__						)




   def   lowerCamelCase_					(						self							:		Union[str, Any]						):







      """simple docstring"""







      UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__magic_name__						)




   def   lowerCamelCase_					(						self							:		Optional[Any]						):







      """simple docstring"""







      UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__magic_name__						)




   def   lowerCamelCase_					(						self							:		List[str]						):







      """simple docstring"""







      UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__magic_name__						)




   @slow
   def   lowerCamelCase_					(						self							:		str						):







      """simple docstring"""







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





   @unittest.skip("""Onnx compliancy broke with TF 2.10"""						)
   def   lowerCamelCase_					(						self							:		List[Any]						):







      """simple docstring"""







      pass






def 							_lowercase    (		):





   """simple docstring"""




   UpperCamelCase         =					tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]	)  # noqa: E231
   UpperCamelCase         =					tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
   UpperCamelCase         =					tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]	)  # noqa: E231
   UpperCamelCase         =					tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   UpperCamelCase         =					tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class    UpperCAmelCase					(       unittest.TestCase    ):




   @slow
   def   lowerCamelCase_					(						self							:		int						):







      """simple docstring"""







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

      UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCamelCase         =					model(input_ids=__magic_name__       ,       bbox=__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)

      # test the sequence output on [0, :3, :3]
      UpperCamelCase         =					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]       ,       __magic_name__       ,       atol=1e-3						)						)

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

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




   @slow
   def   lowerCamelCase_					(						self							:		Dict						):







      """simple docstring"""







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

      UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

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

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

      # test the shape of the logits
      UpperCamelCase         =					outputs.logits
      UpperCamelCase         =					(2, 2)
      self.assertEqual(logits.shape       ,       __magic_name__						)




   @slow
   def   lowerCamelCase_					(						self							:		int						):







      """simple docstring"""







      UpperCamelCase         =					TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""       ,       num_labels=1_3						)

      UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCamelCase         =					model(
          input_ids=__magic_name__       ,       bbox=__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__       ,       labels=__magic_name__						)

      # test the shape of the logits
      UpperCamelCase         =					outputs.logits
      UpperCamelCase         =					tf.convert_to_tensor((2, 2_5, 1_3)						)
      self.assertEqual(logits.shape       ,       __magic_name__						)





   @slow
   def   lowerCamelCase_					(						self							:		Union[str, Any]						):







      """simple docstring"""







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

      UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					prepare_layoutlm_batch_inputs()

      # forward pass
      UpperCamelCase         =					model(input_ids=__magic_name__       ,       bbox=__magic_name__       ,       attention_mask=__magic_name__       ,       token_type_ids=__magic_name__						)

      # test the shape of the logits
      UpperCamelCase         =					tf.convert_to_tensor((2, 2_5)						)
      self.assertEqual(outputs.start_logits.shape       ,       __magic_name__						)
      self.assertEqual(outputs.end_logits.shape       ,       __magic_name__						)








def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					int(SCREAMING_SNAKE_CASE_	)
			if decimal in (0, 1):  # Exit cases for the recursion
						return str(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase  ,  UpperCamelCase         =					divmod(SCREAMING_SNAKE_CASE_       ,  2	)
			return binary_recursive(SCREAMING_SNAKE_CASE_	) + str(SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			UpperCamelCase         =					str(SCREAMING_SNAKE_CASE_	).strip()
			if not number:
						raise ValueError("""No input value was provided"""	)
			UpperCamelCase         =					"""-""" if number.startswith("""-"""	) else """"""
			UpperCamelCase         =					number.lstrip("""-"""	)
			if not number.isnumeric():
						raise ValueError("""Input value is not an integer"""	)
			return f'{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_	)	)}'


if __name__ == "__main__":
			from doctest import testmod

			testmod()




