





'''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 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=2      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=4      ,       UpperCamelCase_=None      ,       UpperCamelCase_=1000      ,       )      ->		Union[str, Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_input_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_labels
		snake_case__	       =   num_choices
		snake_case__	       =   scope
		snake_case__	       =   range_bbox





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

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

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

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

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

		snake_case__	       =   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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Dict:
		snake_case__	       =   TFLayoutLMModel(config=UpperCamelCase_		)

		snake_case__	       =   model(UpperCamelCase_      ,       UpperCamelCase_      ,       attention_mask=UpperCamelCase_      ,       token_type_ids=UpperCamelCase_		)
		snake_case__	       =   model(UpperCamelCase_      ,       UpperCamelCase_      ,       token_type_ids=UpperCamelCase_		)
		snake_case__	       =   model(UpperCamelCase_      ,       UpperCamelCase_		)
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		snake_case__	       =   TFLayoutLMForMaskedLM(config=UpperCamelCase_		)

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   self.num_labels
		snake_case__	       =   TFLayoutLMForSequenceClassification(config=UpperCamelCase_		)

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   self.num_labels
		snake_case__	       =   TFLayoutLMForTokenClassification(config=UpperCamelCase_		)

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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Union[str, Any]:
		snake_case__	       =   TFLayoutLMForQuestionAnswering(config=UpperCamelCase_		)

		snake_case__	       =   model(UpperCamelCase_      ,       UpperCamelCase_      ,       attention_mask=UpperCamelCase_      ,       token_type_ids=UpperCamelCase_		)
		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   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		(
		    (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  (
		    snake_case__
		)       ,  
		)	       =   config_and_inputs
		snake_case__	       =   {
		    '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     (					__magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__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       						=  10





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   TFLayoutLMModelTester(self		)
		snake_case__	       =   ConfigTester(self      ,       config_class=UpperCamelCase_      ,       hidden_size=37		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		self.config_tester.run_common_tests()





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_		)





	@slow
	def   _snake_case   (		self		)      ->		List[str]:
		for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			snake_case__	       =   TFLayoutLMModel.from_pretrained(UpperCamelCase_		)
			self.assertIsNotNone(UpperCamelCase_		)





	@unittest.skip('Onnx compliancy broke with TF 2.10'		)
	def   _snake_case   (		self		)      ->		int:
		pass





def 						__lowerCamelCase			(	)   ->List[Any]:
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	snake_case__	       =   tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]							)  # noqa: E231
	snake_case__	       =   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
	snake_case__	       =   tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]							)  # noqa: E231
	snake_case__	       =   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)
	snake_case__	       =   tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]							)  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class 				__snake_case     (					unittest.TestCase							):





	@slow
	def   _snake_case   (		self		)      ->		Optional[Any]:
		snake_case__	       =   TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'		)

		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   prepare_layoutlm_batch_inputs()

		# forward pass
		snake_case__	       =   model(input_ids=UpperCamelCase_      ,       bbox=UpperCamelCase_      ,       attention_mask=UpperCamelCase_      ,       token_type_ids=UpperCamelCase_		)

		# test the sequence output on [0, :3, :3]
		snake_case__	       =   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]      ,       UpperCamelCase_      ,       atol=1E-3		)		)

		# test the pooled output on [1, :3]
		snake_case__	       =   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]      ,       UpperCamelCase_      ,       atol=1E-3		)		)





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

		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   prepare_layoutlm_batch_inputs()

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

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

		# test the shape of the logits
		snake_case__	       =   outputs.logits
		snake_case__	       =   (2, 2)
		self.assertEqual(logits.shape      ,       UpperCamelCase_		)





	@slow
	def   _snake_case   (		self		)      ->		Optional[int]:
		# initialize model with randomly initialized token classification head
		snake_case__	       =   TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'      ,       num_labels=13		)

		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   prepare_layoutlm_batch_inputs()

		# forward pass
		snake_case__	       =   model(
		    input_ids=UpperCamelCase_      ,       bbox=UpperCamelCase_      ,       attention_mask=UpperCamelCase_      ,       token_type_ids=UpperCamelCase_      ,       labels=UpperCamelCase_		)

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





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

		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   prepare_layoutlm_batch_inputs()

		# forward pass
		snake_case__	       =   model(input_ids=UpperCamelCase_      ,       bbox=UpperCamelCase_      ,       attention_mask=UpperCamelCase_      ,       token_type_ids=UpperCamelCase_		)

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










'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i + 1][j] + matrix[i][j]							)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums							)


if __name__ == "__main__":
			print(f"""{solution() = }""")



