



'''simple docstring'''




import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

lowerCamelCase				:Union[str, Any]							       =   (
    '''4S 3H 2C 7S 5H''',
    '''9D 8H 2C 6S 7H''',
    '''2D 6D 9D TH 7D''',
    '''TC 8C 2S JH 6C''',
    '''JH 8S TH AH QH''',
    '''TS KS 5S 9S AC''',
    '''KD 6S 9D TH AD''',
    '''KS 8D 4D 9S 4S''',  # pair
    '''8C 4S KH JS 4D''',  # pair
    '''QH 8H KD JH 8S''',  # pair
    '''KC 4H KS 2H 8D''',  # pair
    '''KD 4S KC 3H 8S''',  # pair
    '''AH 8S AS KC JH''',  # pair
    '''3H 4C 4H 3S 2H''',  # 2 pairs
    '''5S 5D 2C KH KH''',  # 2 pairs
    '''3C KH 5D 5S KH''',  # 2 pairs
    '''AS 3C KH AD KH''',  # 2 pairs
    '''7C 7S 3S 7H 5S''',  # 3 of a kind
    '''7C 7S KH 2H 7H''',  # 3 of a kind
    '''AC KH QH AH AS''',  # 3 of a kind
    '''2H 4D 3C AS 5S''',  # straight (low ace)
    '''3C 5C 4C 2C 6H''',  # straight
    '''6S 8S 7S 5H 9H''',  # straight
    '''JS QS 9H TS KH''',  # straight
    '''QC KH TS JS AH''',  # straight (high ace)
    '''8C 9C 5C 3C TC''',  # flush
    '''3S 8S 9S 5S KS''',  # flush
    '''4C 5C 9C 8C KC''',  # flush
    '''JH 8H AH KH QH''',  # flush
    '''3D 2H 3H 2C 2D''',  # full house
    '''2H 2C 3S 3H 3D''',  # full house
    '''KH KC 3S 3H 3D''',  # full house
    '''JC 6H JS JD JH''',  # 4 of a kind
    '''JC 7H JS JD JH''',  # 4 of a kind
    '''JC KH JS JD JH''',  # 4 of a kind
    '''2S AS 4S 5S 3S''',  # straight flush (low ace)
    '''2D 6D 3D 4D 5D''',  # straight flush
    '''5C 6C 3C 7C 4C''',  # straight flush
    '''JH 9H TH KH QH''',  # straight flush
    '''JH AH TH KH QH''',  # royal flush (high ace straight flush)
)

lowerCamelCase				:Any							       =   (
    ('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
    ('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
    ('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
    ('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
    ('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
    ('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
    ('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
    ('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
    ('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
    ('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
    ('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
    ('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
    ('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
    ('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
    ('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
    ('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
    ('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
    ('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
    ('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
    ('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
    ('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
    ('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
    ('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
    ('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
    ('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
    ('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
    ('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)

lowerCamelCase				:int							       =   (
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', True),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', False),
    ('''AS 3S 4S 8S 2S''', True),
)

lowerCamelCase				:List[Any]							       =   (
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', False),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', True),
)

lowerCamelCase				:Dict							       =   (
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
    ('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

lowerCamelCase				:Optional[int]							       =   (
    ('''JH AH TH KH QH''', 0),
    ('''JH 9H TH KH QH''', 0),
    ('''JC KH JS JD JH''', 7),
    ('''KH KC 3S 3H 3D''', 6),
    ('''8C 9C 5C 3C TC''', 0),
    ('''JS QS 9H TS KH''', 0),
    ('''7C 7S KH 2H 7H''', 3),
    ('''3C KH 5D 5S KH''', 2),
    ('''QH 8H KD JH 8S''', 1),
    ('''2D 6D 9D TH 7D''', 0),
)

lowerCamelCase				:Optional[int]							       =   (
    ('''JH AH TH KH QH''', 2_3),
    ('''JH 9H TH KH QH''', 2_2),
    ('''JC KH JS JD JH''', 2_1),
    ('''KH KC 3S 3H 3D''', 2_0),
    ('''8C 9C 5C 3C TC''', 1_9),
    ('''JS QS 9H TS KH''', 1_8),
    ('''7C 7S KH 2H 7H''', 1_7),
    ('''3C KH 5D 5S KH''', 1_6),
    ('''QH 8H KD JH 8S''', 1_5),
    ('''2D 6D 9D TH 7D''', 1_4),
)



def       a     (					):
			'''simple docstring'''





			A_,       A_      :		Any      =	randrange(len(_UpperCAmelCase       )       ), randrange(len(_UpperCAmelCase       )       )
			A_      :		Union[str, Any]      =	["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
			A_,       A_      :		Optional[Any]      =	SORTED_HANDS[play], SORTED_HANDS[oppo]
			return hand, other, expected



def       a     (					lowerCamelCase__ = 1_00       ):
			'''simple docstring'''





			return (generate_random_hand() for _ in range(_UpperCAmelCase       ))



@pytest.mark.parametrize("""hand, expected"""		, _UpperCAmelCase       )
def       a     (					lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			assert PokerHand(_UpperCAmelCase       )._is_flush() == expected



@pytest.mark.parametrize("""hand, expected"""		, _UpperCAmelCase       )
def       a     (					lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			assert PokerHand(_UpperCAmelCase       )._is_straight() == expected



@pytest.mark.parametrize("""hand, expected, card_values"""		, _UpperCAmelCase       )
def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Union[str, Any]      =	PokerHand(_UpperCAmelCase       )
			assert player._is_five_high_straight() == expected
			assert player._card_values == card_values



@pytest.mark.parametrize("""hand, expected"""		, _UpperCAmelCase       )
def       a     (					lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			assert PokerHand(_UpperCAmelCase       )._is_same_kind() == expected



@pytest.mark.parametrize("""hand, expected"""		, _UpperCAmelCase       )
def       a     (					lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			assert PokerHand(_UpperCAmelCase       )._hand_type == expected



@pytest.mark.parametrize("""hand, other, expected"""		, _UpperCAmelCase       )
def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			assert PokerHand(_UpperCAmelCase       ).compare_with(PokerHand(_UpperCAmelCase       )       ) == expected



@pytest.mark.parametrize("""hand, other, expected"""		, generate_random_hands()       )
def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			assert PokerHand(_UpperCAmelCase       ).compare_with(PokerHand(_UpperCAmelCase       )       ) == expected



def       a     (					):
			'''simple docstring'''





			A_      :		Tuple      =	[PokerHand(_UpperCAmelCase       ) for hand in SORTED_HANDS]
			A_      :		str      =	poker_hands.copy()
			shuffle(_UpperCAmelCase       )
			A_      :		Union[str, Any]      =	chain(sorted(_UpperCAmelCase       )       )
			for index, hand in enumerate(_UpperCAmelCase       ):
						assert hand == poker_hands[index]



def       a     (					):
			'''simple docstring'''





			A_      :		List[Any]      =	[PokerHand("""2D AC 3H 4H 5S"""       ), PokerHand("""2S 3H 4H 5S 6C"""       )]
			pokerhands.sort(reverse=_UpperCAmelCase       )
			assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"



def       a     (					):
			'''simple docstring'''





			A_      :		str      =	PokerHand("""2C 4S AS 3D 5C"""       )
			A_      :		str      =	True
			A_      :		Union[str, Any]      =	[5, 4, 3, 2, 14]
			for _ in range(10       ):
						assert pokerhand._is_five_high_straight() == expected
						assert pokerhand._card_values == expected_card_values



def       a     (					):
			'''simple docstring'''





			A_      :		Any      =	0
			A_      :		str      =	os.path.abspath(os.path.dirname(_UpperCAmelCase       )       )
			A_      :		List[Any]      =	os.path.join(_UpperCAmelCase		, """poker_hands.txt"""       )
			with open(_UpperCAmelCase       ) as file_hand:
						for line in file_hand:
									A_      :		Tuple      =	line[:14].strip()
									A_      :		List[str]      =	line[15:].strip()
									A_,       A_      :		Optional[Any]      =	PokerHand(_UpperCAmelCase       ), PokerHand(_UpperCAmelCase       )
									A_      :		str      =	player.compare_with(_UpperCAmelCase       )
									if output == "Win":
												answer += 1
			assert answer == 3_76






"""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				,       _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=5_12				,       _snake_case=16				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=3				,       _snake_case=4				,       _snake_case=None				,       _snake_case=10_00				,       ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

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

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

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

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

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel(config=_snake_case     )

  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= model(_snake_case				,       _snake_case				,       token_type_ids=_snake_case     )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForMaskedLM(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForSequenceClassification(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.num_labels
  lowerCAmelCase    				= TFLayoutLMForTokenClassification(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering(config=_snake_case     )

  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,(
      lowerCAmelCase
  )				,
  )    				= config_and_inputs
  lowerCAmelCase    				= {
      '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__             =       (
     (
         TFLayoutLMModel,
         TFLayoutLMForMaskedLM,
         TFLayoutLMForTokenClassification,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForQuestionAnswering,
     )
     if is_tf_available()
     else ()
 )
 snake_case__             =       (
     {
         '''feature-extraction''': TFLayoutLMModel,
         '''fill-mask''': TFLayoutLMForMaskedLM,
         '''text-classification''': TFLayoutLMForSequenceClassification,
         '''token-classification''': TFLayoutLMForTokenClassification,
         '''zero-shot''': TFLayoutLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 snake_case__             =       False
 snake_case__             =       True
 snake_case__             =       1_0



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 @unittest.skip('Onnx compliancy broke with TF 2.10'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  pass


def 		_SCREAMING_SNAKE_CASE			():
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase    				= tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]			)  # noqa: E231
 lowerCAmelCase    				= 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
 lowerCAmelCase    				= 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],[1000,1000,1000,1000]],[[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],[1000,1000,1000,1000]]]			)  # noqa: E231
 lowerCAmelCase    				= 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)
 lowerCAmelCase    				= 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       a							(   unittest.TestCase  ):



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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]
  lowerCAmelCase    				= 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]				,       _snake_case				,       atol=1E-3     )     )

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

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



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=2     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= outputs.loss
  lowerCAmelCase    				= (2,)
  self.assertEqual(loss.shape				,       _snake_case     )

  # test the shape of the logits
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= (2, 2)
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'				,       num_labels=13     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= 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
  lowerCAmelCase    				= outputs.logits
  lowerCAmelCase    				= tf.convert_to_tensor((2, 25, 13)     )
  self.assertEqual(logits.shape				,       _snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase    				= model(input_ids=_snake_case				,       bbox=_snake_case				,       attention_mask=_snake_case				,       token_type_ids=_snake_case     )

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





