


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 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=1_3   ,						__lowercase=7   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=9_9   ,						__lowercase=3_2   ,						__lowercase=2   ,						__lowercase=4   ,						__lowercase=3_7   ,						__lowercase="gelu"   ,						__lowercase=0.1   ,						__lowercase=0.1   ,						__lowercase=5_1_2   ,						__lowercase=1_6   ,						__lowercase=2   ,						__lowercase=0.02   ,						__lowercase=3   ,						__lowercase=4   ,						__lowercase=None   ,						__lowercase=1_0_0_0   ,						)					->    str:
										lowerCAmelCase_ :      Dict    				=				parent
										lowerCAmelCase_ :      Union[str, Any]    				=				batch_size
										lowerCAmelCase_ :      int    				=				seq_length
										lowerCAmelCase_ :      Dict    				=				is_training
										lowerCAmelCase_ :      List[str]    				=				use_input_mask
										lowerCAmelCase_ :      Union[str, Any]    				=				use_token_type_ids
										lowerCAmelCase_ :      List[Any]    				=				use_labels
										lowerCAmelCase_ :      Dict    				=				vocab_size
										lowerCAmelCase_ :      Optional[Any]    				=				hidden_size
										lowerCAmelCase_ :      str    				=				num_hidden_layers
										lowerCAmelCase_ :      Dict    				=				num_attention_heads
										lowerCAmelCase_ :      str    				=				intermediate_size
										lowerCAmelCase_ :      Union[str, Any]    				=				hidden_act
										lowerCAmelCase_ :      str    				=				hidden_dropout_prob
										lowerCAmelCase_ :      List[Any]    				=				attention_probs_dropout_prob
										lowerCAmelCase_ :      Any    				=				max_position_embeddings
										lowerCAmelCase_ :      List[str]    				=				type_vocab_size
										lowerCAmelCase_ :      List[str]    				=				type_sequence_label_size
										lowerCAmelCase_ :      int    				=				initializer_range
										lowerCAmelCase_ :      Dict    				=				num_labels
										lowerCAmelCase_ :      str    				=				num_choices
										lowerCAmelCase_ :      int    				=				scope
										lowerCAmelCase_ :      int    				=				range_bbox




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Any    				=				ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size							)

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

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

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

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

										lowerCAmelCase_ :      Optional[int]    				=				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   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[int]:
										lowerCAmelCase_ :      str    				=				TFLayoutLMModel(config=__lowercase							)

										lowerCAmelCase_ :      str    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)
										lowerCAmelCase_ :      List[Any]    				=				model(__lowercase   ,						__lowercase   ,						token_type_ids=__lowercase							)
										lowerCAmelCase_ :      Any    				=				model(__lowercase   ,						__lowercase							)
										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   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Dict    				=				TFLayoutLMForMaskedLM(config=__lowercase							)

										lowerCAmelCase_ :      int    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[Any]:
										lowerCAmelCase_ :      int    				=				self.num_labels
										lowerCAmelCase_ :      Dict    				=				TFLayoutLMForSequenceClassification(config=__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[Any]:
										lowerCAmelCase_ :      List[str]    				=				self.num_labels
										lowerCAmelCase_ :      Union[str, Any]    				=				TFLayoutLMForTokenClassification(config=__lowercase							)

										lowerCAmelCase_ :      int    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Tuple    				=				TFLayoutLMForQuestionAnswering(config=__lowercase							)

										lowerCAmelCase_ :      Dict    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)
										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							)					->    List[Any]:
										lowerCAmelCase_ :      List[Any]    				=				self.prepare_config_and_inputs()
										(
										    (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  (
										    lowerCAmelCase_
										)				,  
										) :      Any    				=				config_and_inputs
										lowerCAmelCase_ :      List[Any]    				=				{
										    '''input_ids''': input_ids,
										    '''bbox''': bbox,
										    '''token_type_ids''': token_type_ids,
										    '''attention_mask''': input_mask,
										}
										return config, inputs_dict







@require_tf
class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							(
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE__      :  List[str]					=							(
						    {
						        """feature-extraction""": TFLayoutLMModel,
						        """fill-mask""": TFLayoutLMForMaskedLM,
						        """text-classification""": TFLayoutLMForSequenceClassification,
						        """token-classification""": TFLayoutLMForTokenClassification,
						        """zero-shot""": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE__      :  Tuple					=							False
						SCREAMING_SNAKE_CASE__      :  Any					=							True
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							10




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Union[str, Any]    				=				TFLayoutLMModelTester(self							)
										lowerCAmelCase_ :      str    				=				ConfigTester(self   ,						config_class=__lowercase   ,						hidden_size=3_7							)




						def 						lowercase_ (				self							)					->    int:
										self.config_tester.run_common_tests()




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase							)




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      int    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      List[str]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__lowercase							)




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Tuple    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    Dict:
										for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_ :      str    				=				TFLayoutLMModel.from_pretrained(__lowercase							)
														self.assertIsNotNone(__lowercase							)




						@unittest.skip('''Onnx compliancy broke with TF 2.10'''							)
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										pass






def 			lowerCAmelCase (		)-> List[Any]:
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				lowerCAmelCase_ :      Union[str, Any]    				=				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
				lowerCAmelCase_ :      Dict    				=				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_ :      Union[str, Any]    				=				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
				lowerCAmelCase_ :      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)
				lowerCAmelCase_ :      List[str]    				=				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 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@slow
						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      int    				=				TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''							)

										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Dict    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      str    				=				model(input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)

										# test the sequence output on [0, :3, :3]
										lowerCAmelCase_ :      List[str]    				=				tf.convert_to_tensor(
										    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]   ,						)

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

										# test the pooled output on [1, :3]
										lowerCAmelCase_ :      Any    				=				tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]							)

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




						@slow
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										# initialize model with randomly initialized sequence classification head
										lowerCAmelCase_ :      str    				=				TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''   ,						num_labels=2							)

										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      str    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      Optional[int]    				=				model(
										    input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=tf.convert_to_tensor([1, 1]							)   ,						)

										# test whether we get a loss as a scalar
										lowerCAmelCase_ :      List[str]    				=				outputs.loss
										lowerCAmelCase_ :      Optional[int]    				=				(2,)
										self.assertEqual(loss.shape   ,						__lowercase							)

										# test the shape of the logits
										lowerCAmelCase_ :      List[str]    				=				outputs.logits
										lowerCAmelCase_ :      Union[str, Any]    				=				(2, 2)
										self.assertEqual(logits.shape   ,						__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    List[Any]:
										# initialize model with randomly initialized token classification head
										lowerCAmelCase_ :      int    				=				TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''   ,						num_labels=1_3							)

										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      Optional[Any]    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      Optional[Any]    				=				model(
										    input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=__lowercase							)

										# test the shape of the logits
										lowerCAmelCase_ :      Tuple    				=				outputs.logits
										lowerCAmelCase_ :      List[Any]    				=				tf.convert_to_tensor((2, 2_5, 1_3)							)
										self.assertEqual(logits.shape   ,						__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    Tuple:
										# initialize model with randomly initialized token classification head
										lowerCAmelCase_ :      Dict    				=				TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''							)

										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      List[str]    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      Any    				=				model(input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)

										# test the shape of the logits
										lowerCAmelCase_ :      Optional[int]    				=				tf.convert_to_tensor((2, 2_5)							)
										self.assertEqual(outputs.start_logits.shape   ,						__lowercase							)
										self.assertEqual(outputs.end_logits.shape   ,						__lowercase							)



_UpperCAmelCase			:  Dict     =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
				lowerCAmelCase_ :      Optional[int]    				=				0
				lowerCAmelCase_ :      List[str]    				=				0
				while place < len(lowerCAmelCase_ ):
								if (place + 1 < len(lowerCAmelCase_ )) and (vals[roman[place]] < vals[roman[place + 1]]):
												total += vals[roman[place + 1]] - vals[roman[place]]
												place += 2
								else:
												total += vals[roman[place]]
												place += 1
				return total



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      List[Any]    				=				[]
				for arabic, roman in ROMAN:
								((lowerCAmelCase_)				,  (lowerCAmelCase_)) :      Optional[int]    				=				divmod(lowerCAmelCase_ ,   lowerCAmelCase_ )
								result.append(roman * factor )
								if number == 0:
												break
				return "".join(lowerCAmelCase_ )


if __name__ == "__main__":
						import doctest

						doctest.testmod()