


import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DistilBertForMaskedLM,
				    DistilBertForMultipleChoice,
				    DistilBertForQuestionAnswering,
				    DistilBertForSequenceClassification,
				    DistilBertForTokenClassification,
				    DistilBertModel,
				)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=13				,   a__=7				,   a__=True				,   a__=True				,   a__=False				,   a__=True				,   a__=99				,   a__=32				,   a__=5				,   a__=4				,   a__=37				,   a__="gelu"				,   a__=0.1				,   a__=0.1				,   a__=512				,   a__=16				,   a__=2				,   a__=0.02				,   a__=3				,   a__=4				,   a__=None				,   )	->				Any:
					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								=				num_choices
					A								=				scope

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

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

					A								=				None
					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								=				ids_tensor([self.batch_size]				,   self.num_choices   )

					A								=				self.get_config()

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

				def      _UpperCAmelCase					(						self   )	->				int:
					return DistilBertConfig(
					    vocab_size=self.vocab_size				,   dim=self.hidden_size				,   n_layers=self.num_hidden_layers				,   n_heads=self.num_attention_heads				,   hidden_dim=self.intermediate_size				,   hidden_act=self.hidden_act				,   dropout=self.hidden_dropout_prob				,   attention_dropout=self.attention_probs_dropout_prob				,   max_position_embeddings=self.max_position_embeddings				,   initializer_range=self.initializer_range				,   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__   )	->				Optional[int]:
					A								=				DistilBertModel(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__				,   a__   )
					A								=				model(a__   )
					self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__   )	->				int:
					A								=				DistilBertForMaskedLM(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__				,   attention_mask=a__				,   labels=a__   )
					self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__   )	->				Tuple:
					A								=				DistilBertForQuestionAnswering(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				model(
					    a__				,   attention_mask=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				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__   )	->				Optional[int]:
					A								=				self.num_labels
					A								=				DistilBertForSequenceClassification(a__   )
					model.to(a__   )
					model.eval()
					A								=				model(a__				,   attention_mask=a__				,   labels=a__   )
					self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__				,   a__				,   a__				,   a__   )	->				Optional[Any]:
					A								=				self.num_labels
					A								=				DistilBertForTokenClassification(config=a__   )
					model.to(a__   )
					model.eval()

					A								=				model(a__				,   attention_mask=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__   )	->				List[Any]:
					A								=				self.num_choices
					A								=				DistilBertForMultipleChoice(config=a__   )
					model.to(a__   )
					model.eval()
					A								=				input_ids.unsqueeze(1   ).expand(-1				,   self.num_choices				,   -1   ).contiguous()
					A								=				input_mask.unsqueeze(1   ).expand(-1				,   self.num_choices				,   -1   ).contiguous()
					A								=				model(
					    a__				,   attention_mask=a__				,   labels=a__				,   )
					self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_choices)   )

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				self.prepare_config_and_inputs()
					((A)					,			(A)					,			(A)					,			(A)					,			(A)					,			(A))								=				config_and_inputs
					A								=				{"""input_ids""": input_ids, """attention_mask""": input_mask}
					return config, inputs_dict






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

				"""simple docstring"""







				lowerCAmelCase 		= (
				    (
				        DistilBertModel,
				        DistilBertForMaskedLM,
				        DistilBertForMultipleChoice,
				        DistilBertForQuestionAnswering,
				        DistilBertForSequenceClassification,
				        DistilBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				lowerCAmelCase 		= (
				    {
				        'feature-extraction': DistilBertModel,
				        'fill-mask': DistilBertForMaskedLM,
				        'question-answering': DistilBertForQuestionAnswering,
				        'text-classification': DistilBertForSequenceClassification,
				        'token-classification': DistilBertForTokenClassification,
				        'zero-shot': DistilBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCAmelCase 		= True
				lowerCAmelCase 		= True
				lowerCAmelCase 		= True
				lowerCAmelCase 		= True

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				DistilBertModelTester(self   )
					A								=				ConfigTester(self				,   config_class=a__				,   dim=37   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					self.config_tester.run_common_tests()

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

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_masked_lm(*a__   )

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

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_sequence_classification(*a__   )

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_distilbert_for_token_classification(*a__   )

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

				@slow
				def      _UpperCAmelCase					(						self   )	->				Any:
					for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A								=				DistilBertModel.from_pretrained(a__   )
						self.assertIsNotNone(a__   )

				@slow
				@require_torch_gpu
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
						# BertForMultipleChoice behaves incorrectly in JIT environments.
						if model_class == DistilBertForMultipleChoice:
							return

						A								=				True
						A								=				model_class(config=a__   )

						A								=				self._prepare_for_class(a__				,   a__   )
						A								=				torch.jit.trace(
						    a__				,   (inputs_dict["""input_ids"""].to("""cpu"""   ), inputs_dict["""attention_mask"""].to("""cpu"""   ))   )

						with tempfile.TemporaryDirectory() as tmp:
							torch.jit.save(a__				,   os.path.join(a__				,   """traced_model.pt"""   )   )
							A								=				torch.jit.load(os.path.join(a__				,   """traced_model.pt"""   )				,   map_location=a__   )
							loaded(inputs_dict["""input_ids"""].to(a__   )				,   inputs_dict["""attention_mask"""].to(a__   )   )






@require_torch
class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				@slow
				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				DistilBertModel.from_pretrained("""distilbert-base-uncased"""   )
					A								=				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
					A								=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
					with torch.no_grad():
						A								=				model(a__				,   attention_mask=a__   )[0]
					A								=				torch.Size((1, 11, 768)   )
					self.assertEqual(output.shape				,   a__   )
					A								=				torch.tensor(
					    [[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]   )

					self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,   a__				,   atol=1e-4   )   )







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() = }''')



