'''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 :



       """simple docstring"""







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

         '''simple docstring'''







         __UpperCAmelCase:					List[str]		= parent
         __UpperCAmelCase:					Optional[int]		= batch_size
         __UpperCAmelCase:					List[Any]		= seq_length
         __UpperCAmelCase:					Dict		= is_training
         __UpperCAmelCase:					Optional[int]		= use_input_mask
         __UpperCAmelCase:					Optional[int]		= use_token_type_ids
         __UpperCAmelCase:					Tuple		= use_labels
         __UpperCAmelCase:					Dict		= vocab_size
         __UpperCAmelCase:					List[Any]		= hidden_size
         __UpperCAmelCase:					Optional[Any]		= num_hidden_layers
         __UpperCAmelCase:					Optional[Any]		= num_attention_heads
         __UpperCAmelCase:					str		= intermediate_size
         __UpperCAmelCase:					str		= hidden_act
         __UpperCAmelCase:					Tuple		= hidden_dropout_prob
         __UpperCAmelCase:					Optional[int]		= attention_probs_dropout_prob
         __UpperCAmelCase:					Optional[int]		= max_position_embeddings
         __UpperCAmelCase:					Tuple		= type_vocab_size
         __UpperCAmelCase:					str		= type_sequence_label_size
         __UpperCAmelCase:					int		= initializer_range
         __UpperCAmelCase:					int		= num_labels
         __UpperCAmelCase:					Union[str, Any]		= num_choices
         __UpperCAmelCase:					Union[str, Any]		= scope
         __UpperCAmelCase:					Union[str, Any]		= range_bbox







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Tuple		= ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size					)

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

         __UpperCAmelCase:					Any		= None
         if self.use_input_mask:
           __UpperCAmelCase:					List[str]		= random_attention_mask([self.batch_size, self.seq_length]					)

         __UpperCAmelCase:					Any		= None
         if self.use_token_type_ids:
           __UpperCAmelCase:					Optional[int]		= ids_tensor([self.batch_size, self.seq_length]   ,    self.type_vocab_size					)

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

         __UpperCAmelCase:					List[Any]		= 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    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

         '''simple docstring'''







         __UpperCAmelCase:					Any		= TFLayoutLMModel(config=snake_case_					)

         __UpperCAmelCase:					List[Any]		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)
         __UpperCAmelCase:					Tuple		= model(snake_case_   ,    snake_case_   ,    token_type_ids=snake_case_					)
         __UpperCAmelCase:					Optional[Any]		= model(snake_case_   ,    snake_case_					)
         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    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

         '''simple docstring'''







         __UpperCAmelCase:					List[str]		= TFLayoutLMForMaskedLM(config=snake_case_					)

         __UpperCAmelCase:					List[str]		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=snake_case_					)
         self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.vocab_size)					)







       def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

         '''simple docstring'''







         __UpperCAmelCase:					str		= self.num_labels
         __UpperCAmelCase:					Tuple		= TFLayoutLMForSequenceClassification(config=snake_case_					)

         __UpperCAmelCase:					int		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)
         self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)					)







       def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

         '''simple docstring'''







         __UpperCAmelCase:					Dict		= self.num_labels
         __UpperCAmelCase:					int		= TFLayoutLMForTokenClassification(config=snake_case_					)

         __UpperCAmelCase:					Any		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=snake_case_					)
         self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.num_labels)					)







       def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

         '''simple docstring'''







         __UpperCAmelCase:					List[Any]		= TFLayoutLMForQuestionAnswering(config=snake_case_					)

         __UpperCAmelCase:					Optional[Any]		= model(snake_case_   ,    snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=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    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Tuple		= self.prepare_config_and_inputs()
         (
             __UpperCAmelCase 
         ):					Union[str, Any]		= config_and_inputs
         __UpperCAmelCase:					Dict		= {
             """input_ids""": input_ids,
             """bbox""": bbox,
             """token_type_ids""": token_type_ids,
             """attention_mask""": input_mask,
         }
         return config, inputs_dict




@require_tf
class    a (       __lowerCAmelCase						,  __lowerCAmelCase						,  unittest.TestCase				):



       """simple docstring"""
       __lowerCAmelCase				      =					(
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       __lowerCAmelCase				      =					(
           {
               """feature-extraction""": TFLayoutLMModel,
               """fill-mask""": TFLayoutLMForMaskedLM,
               """text-classification""": TFLayoutLMForSequenceClassification,
               """token-classification""": TFLayoutLMForTokenClassification,
               """zero-shot""": TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __lowerCAmelCase				      =					False
       __lowerCAmelCase				      =					True
       __lowerCAmelCase				      =					1_0







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Tuple		= TFLayoutLMModelTester(self					)
         __UpperCAmelCase:					Optional[int]		= ConfigTester(self   ,    config_class=snake_case_   ,    hidden_size=37					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         self.config_tester.run_common_tests()







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Any		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Tuple		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*snake_case_					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Union[str, Any]		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*snake_case_					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					int		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*snake_case_					)







       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Optional[Any]		= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __UpperCAmelCase:					Tuple		= TFLayoutLMModel.from_pretrained(snake_case_					)
           self.assertIsNotNone(snake_case_					)







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

         '''simple docstring'''







         pass






def  UpperCamelCase__					(					)							->	Tuple:
  # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  # fmt: off
  __UpperCAmelCase:					Union[str, Any]		= 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
  __UpperCAmelCase:					Any		= 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:					Optional[Any]		= 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
  __UpperCAmelCase:					List[Any]		= tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]     )  # noqa: E231
  # these are sequence labels (i.e. at the token level)
  __UpperCAmelCase:					List[Any]		= 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				):



       """simple docstring"""







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					int		= TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""					)

         __UpperCAmelCase:					List[str]		= prepare_layoutlm_batch_inputs()

         # forward pass
         __UpperCAmelCase:					Dict		= model(input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)

         # test the sequence output on [0, :3, :3]
         __UpperCAmelCase:					Tuple		= 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]   ,    snake_case_   ,    atol=1e-3					)					)

         # test the pooled output on [1, :3]
         __UpperCAmelCase:					Union[str, Any]		= 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]   ,    snake_case_   ,    atol=1e-3					)					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







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

         __UpperCAmelCase:					List[Any]		= prepare_layoutlm_batch_inputs()

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

         # test whether we get a loss as a scalar
         __UpperCAmelCase:					int		= outputs.loss
         __UpperCAmelCase:					Dict		= (2,)
         self.assertEqual(loss.shape   ,    snake_case_					)

         # test the shape of the logits
         __UpperCAmelCase:					Tuple		= outputs.logits
         __UpperCAmelCase:					Tuple		= (2, 2)
         self.assertEqual(logits.shape   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







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

         __UpperCAmelCase:					List[str]		= prepare_layoutlm_batch_inputs()

         # forward pass
         __UpperCAmelCase:					int		= model(
             input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_   ,    labels=snake_case_					)

         # test the shape of the logits
         __UpperCAmelCase:					Optional[int]		= outputs.logits
         __UpperCAmelCase:					Optional[int]		= tf.convert_to_tensor((2, 25, 13)					)
         self.assertEqual(logits.shape   ,    snake_case_					)







       @slow
       def    lowercase_  (							self					):

         '''simple docstring'''







         __UpperCAmelCase:					Union[str, Any]		= TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""					)

         __UpperCAmelCase:					Any		= prepare_layoutlm_batch_inputs()

         # forward pass
         __UpperCAmelCase:					List[str]		= model(input_ids=snake_case_   ,    bbox=snake_case_   ,    attention_mask=snake_case_   ,    token_type_ids=snake_case_					)

         # test the shape of the logits
         __UpperCAmelCase:					Dict		= tf.convert_to_tensor((2, 25)					)
         self.assertEqual(outputs.start_logits.shape   ,    snake_case_					)
         self.assertEqual(outputs.end_logits.shape   ,    snake_case_					)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)