


import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				)
				from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=13				,   a__=7				,   a__=True				,   a__=True				,   a__=True				,   a__=True				,   a__=99				,   a__=24				,   a__=2				,   a__=6				,   a__=37				,   a__="gelu"				,   a__=0.1				,   a__=0.1				,   a__=512				,   a__=16				,   a__=2				,   a__=0.02				,   a__=3				,   a__=None				,   a__=1000				,   )	->				str:
					A								=				parent
					A								=				batch_size
					A								=				seq_length
					A								=				is_training
					A								=				use_input_mask
					A								=				use_token_type_ids
					A								=				use_labels
					A								=				vocab_size
					A								=				hidden_size
					A								=				num_hidden_layers
					A								=				num_attention_heads
					A								=				intermediate_size
					A								=				hidden_act
					A								=				hidden_dropout_prob
					A								=				attention_probs_dropout_prob
					A								=				max_position_embeddings
					A								=				type_vocab_size
					A								=				type_sequence_label_size
					A								=				initializer_range
					A								=				num_labels
					A								=				scope
					A								=				range_bbox

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size   )

					A								=				ids_tensor([self.batch_size, self.seq_length, 4]				,   self.range_bbox   )
					# 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]:
								A								=				bbox[i, j, 3]
								A								=				bbox[i, j, 1]
								A								=				t
							if bbox[i, j, 2] < bbox[i, j, 0]:
								A								=				bbox[i, j, 2]
								A								=				bbox[i, j, 0]
								A								=				t

					A								=				None
					if self.use_input_mask:
						A								=				ids_tensor([self.batch_size, self.seq_length]				,   vocab_size=2   )

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

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

					A								=				self.get_config()

					return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

				def      _UpperCAmelCase					(						self   )	->				int:
					return LiltConfig(
					    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				,   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   )	->				List[str]:
					A								=				LiltModel(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__				,   bbox=a__				,   attention_mask=a__				,   token_type_ids=a__   )
					A								=				model(a__				,   bbox=a__				,   token_type_ids=a__   )
					A								=				model(a__				,   bbox=a__   )

					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				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   )	->				int:
					A								=				self.num_labels
					A								=				LiltForTokenClassification(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(
					    a__				,   bbox=a__				,   attention_mask=a__				,   token_type_ids=a__				,   labels=a__   )
					self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.num_labels)   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__				,   )	->				Dict:
					A								=				LiltForQuestionAnswering(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(
					    a__				,   bbox=a__				,   attention_mask=a__				,   token_type_ids=a__				,   start_positions=a__				,   end_positions=a__				,   )
					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   )	->				Optional[Any]:
					A								=				self.prepare_config_and_inputs()
					(
					    (
					    A			
					)					,			(
					    A			
					)					,			(
					    A			
					)					,			(
					    A			
					)					,			(
					    A			
					)					,			(
					    A			
					)					,			(
					    A			
					)					,						
					)								=				config_and_inputs
					A								=				{
					    """input_ids""": input_ids,
					    """bbox""": bbox,
					    """token_type_ids""": token_type_ids,
					    """attention_mask""": input_mask,
					}
					return config, inputs_dict






@require_torch
class       _UpperCamelCase    (					__snake_case    ,		__snake_case    ,		__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= (
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				lowerCAmelCase 		= (
				    {
				        'feature-extraction': LiltModel,
				        'question-answering': LiltForQuestionAnswering,
				        'text-classification': LiltForSequenceClassification,
				        'token-classification': LiltForTokenClassification,
				        'zero-shot': LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCAmelCase 		= False
				lowerCAmelCase 		= False

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__   )	->				Union[str, Any]:
					return True

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				LiltModelTester(self   )
					A								=				ConfigTester(self				,   config_class=a__				,   hidden_size=37   )

				def      _UpperCAmelCase					(						self   )	->				str:
					self.config_tester.run_common_tests()

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a__   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						A								=				type
						self.model_tester.create_and_check_model(*a__   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*a__   )

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*a__   )

				@slow
				def      _UpperCAmelCase					(						self   )	->				Dict:
					for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A								=				LiltModel.from_pretrained(a__   )
						self.assertIsNotNone(a__   )






@require_torch
@slow
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""   ).to(a__   )

					A								=				torch.tensor([[1, 2]]				,   device=a__   )
					A								=				torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]				,   device=a__   )

					# forward pass
					with torch.no_grad():
						A								=				model(input_ids=a__				,   bbox=a__   )

					A								=				torch.Size([1, 2, 768]   )
					A								=				torch.tensor(
					    [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]				,   device=a__				,   )

					self.assertTrue(outputs.last_hidden_state.shape				,   a__   )
					self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]				,   a__				,   atol=1e-3   )   )







import os





def   _lowerCAmelCase				(   UpperCamelCase__:     str = "matrix.txt"						) ->   int:





	"""simple docstring"""







	with open(os.path.join(os.path.dirname(UpperCamelCase__						)							,					UpperCamelCase__						)						) as in_file:
		A								=				in_file.read()

	A								=				[[int(UpperCamelCase__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
	A								=				[[0 for cell in row] for row in grid]
	A								=				len(grid[0]						)

	A								=				[[0 for i in range(UpperCamelCase__						)] for j in range(UpperCamelCase__						)]
	A								=				grid[0][0]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[0][i] + dp[0][i - 1]
	for i in range(1							,					UpperCamelCase__						):
		A								=				grid[i][0] + dp[i - 1][0]

	for i in range(1							,					UpperCamelCase__						):
		for j in range(1							,					UpperCamelCase__						):
			A								=				grid[i][j] + min(dp[i - 1][j]							,					dp[i][j - 1]						)

	return dp[-1][-1]


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



