'''simple docstring'''




import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class       UpperCAmelCase__  :






			"""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       ,				)    ->     str:
								lowercase_					:       List[str]     				=					parent
								lowercase_					:       int     				=					batch_size
								lowercase_					:       Any     				=					seq_length
								lowercase_					:       Tuple     				=					is_training
								lowercase_					:       Tuple     				=					use_input_mask
								lowercase_					:       str     				=					use_token_type_ids
								lowercase_					:       List[Any]     				=					use_labels
								lowercase_					:       int     				=					vocab_size
								lowercase_					:       Dict     				=					hidden_size
								lowercase_					:       int     				=					num_hidden_layers
								lowercase_					:       Tuple     				=					num_attention_heads
								lowercase_					:       Optional[Any]     				=					intermediate_size
								lowercase_					:       Dict     				=					hidden_act
								lowercase_					:       Optional[Any]     				=					hidden_dropout_prob
								lowercase_					:       Union[str, Any]     				=					attention_probs_dropout_prob
								lowercase_					:       Dict     				=					max_position_embeddings
								lowercase_					:       Tuple     				=					type_vocab_size
								lowercase_					:       int     				=					type_sequence_label_size
								lowercase_					:       Dict     				=					initializer_range
								lowercase_					:       Tuple     				=					num_labels
								lowercase_					:       Optional[int]     				=					num_choices
								lowercase_					:       List[str]     				=					scope
			def 		_lowerCamelCase (self				)    ->     str:
								lowercase_					:       Dict     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

								lowercase_					:       Any     				=					None
								if self.use_input_mask:
													lowercase_					:       int     				=					random_attention_mask([self.batch_size, self.seq_length]				)

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

								lowercase_					:       int     				=					None
								lowercase_					:       Optional[int]     				=					None
								lowercase_					:       Dict     				=					None
								if self.use_labels:
													lowercase_					:       Any     				=					ids_tensor([self.batch_size]       ,				self.type_sequence_label_size				)
													lowercase_					:       List[str]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels				)
													lowercase_					:       int     				=					ids_tensor([self.batch_size]       ,				self.num_choices				)

								lowercase_					:       str     				=					self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 		_lowerCamelCase (self				)    ->     List[str]:
								return LlamaConfig(
								    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       ,				is_decoder=_a       ,				initializer_range=self.initializer_range       ,				)
			def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a				)    ->     Tuple:
								lowercase_					:       Any     				=					LlamaModel(config=_a				)
								model.to(_a				)
								model.eval()
								lowercase_					:       Any     				=					model(_a       ,				attention_mask=_a				)
								lowercase_					:       Optional[Any]     				=					model(_a				)
								self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
			def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     str:
								lowercase_					:       Dict     				=					True
								lowercase_					:       Any     				=					LlamaModel(_a				)
								model.to(_a				)
								model.eval()
								lowercase_					:       List[Any]     				=					model(
								    _a       ,				attention_mask=_a       ,				encoder_hidden_states=_a       ,				encoder_attention_mask=_a       ,				)
								lowercase_					:       List[str]     				=					model(
								    _a       ,				attention_mask=_a       ,				encoder_hidden_states=_a       ,				)
								lowercase_					:       Tuple     				=					model(_a       ,				attention_mask=_a				)
								self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
			def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     int:
								lowercase_					:       Any     				=					LlamaForCausalLM(config=_a				)
								model.to(_a				)
								model.eval()
								lowercase_					:       Any     				=					model(_a       ,				attention_mask=_a       ,				labels=_a				)
								self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)				)
			def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     List[Any]:
								lowercase_					:       int     				=					True
								lowercase_					:       Dict     				=					True
								lowercase_					:       Optional[int]     				=					LlamaForCausalLM(config=_a				)
								model.to(_a				)
								model.eval()

								# first forward pass
								lowercase_					:       Union[str, Any]     				=					model(
								    _a       ,				attention_mask=_a       ,				encoder_hidden_states=_a       ,				encoder_attention_mask=_a       ,				use_cache=_a       ,				)
								lowercase_					:       List[Any]     				=					outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowercase_					:       int     				=					ids_tensor((self.batch_size, 3)       ,				config.vocab_size				)
								lowercase_					:       Tuple     				=					ids_tensor((self.batch_size, 3)       ,				vocab_size=2				)

								# append to next input_ids and
								lowercase_					:       int     				=					torch.cat([input_ids, next_tokens]       ,				dim=-1				)
								lowercase_					:       Dict     				=					torch.cat([input_mask, next_mask]       ,				dim=-1				)

								lowercase_					:       int     				=					model(
								    _a       ,				attention_mask=_a       ,				encoder_hidden_states=_a       ,				encoder_attention_mask=_a       ,				output_hidden_states=_a       ,				)['hidden_states'][0]
								lowercase_					:       Optional[int]     				=					model(
								    _a       ,				attention_mask=_a       ,				encoder_hidden_states=_a       ,				encoder_attention_mask=_a       ,				past_key_values=_a       ,				output_hidden_states=_a       ,				)['hidden_states'][0]

								# select random slice
								lowercase_					:       Optional[int]     				=					ids_tensor((1,)       ,				output_from_past.shape[-1]				).item()
								lowercase_					:       str     				=					output_from_no_past[:, -3:, random_slice_idx].detach()
								lowercase_					:       Dict     				=					output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_a       ,				_a       ,				atol=1e-3				)				)





			def 		_lowerCamelCase (self				)    ->     Dict:
								lowercase_					:       Union[str, Any]     				=					self.prepare_config_and_inputs()
								(
								    lowercase_
								)					:       Dict     				=					config_and_inputs
								lowercase_					:       Optional[Any]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class       UpperCAmelCase__  (       _snake_case					,     _snake_case					,     _snake_case					,     unittest.TestCase			):






			"""simple docstring"""
			A							:       Union[str, Any]     =    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
			A							:       int     =    (LlamaForCausalLM,) if is_torch_available() else ()
			A							:       List[str]     =    (
			    {
			        '''feature-extraction''': LlamaModel,
			        '''text-classification''': LlamaForSequenceClassification,
			        '''text-generation''': LlamaForCausalLM,
			        '''zero-shot''': LlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			A							:       Tuple     =    False
			A							:       Dict     =    False
			def 		_lowerCamelCase (self				)    ->     Optional[int]:
								lowercase_					:       Optional[Any]     				=					LlamaModelTester(self				)
								lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
			def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
								self.config_tester.run_common_tests()
			def 		_lowerCamelCase (self				)    ->     List[str]:
								lowercase_					:       Union[str, Any]     				=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_a				)
			def 		_lowerCamelCase (self				)    ->     Tuple:
								lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
													lowercase_					:       Dict     				=					type
													self.model_tester.create_and_check_model(*_a				)
			def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
								lowercase_					:       Union[str, Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()
								lowercase_					:       Dict     				=					3
								lowercase_					:       Optional[int]     				=					input_dict['input_ids']
								lowercase_					:       Tuple     				=					input_ids.ne(1				).to(_a				)
								lowercase_					:       int     				=					ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size				)
								lowercase_					:       Optional[int]     				=					LlamaForSequenceClassification(_a				)
								model.to(_a				)
								model.eval()
								lowercase_					:       Dict     				=					model(_a       ,				attention_mask=_a       ,				labels=_a				)
								self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)
			def 		_lowerCamelCase (self				)    ->     Any:
								lowercase_					:       Tuple     				=					self.model_tester.prepare_config_and_inputs_for_common()
								lowercase_					:       Optional[int]     				=					3
								lowercase_					:       Optional[int]     				=					'single_label_classification'
								lowercase_					:       Union[str, Any]     				=					input_dict['input_ids']
								lowercase_					:       Optional[int]     				=					input_ids.ne(1				).to(_a				)
								lowercase_					:       str     				=					ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size				)
								lowercase_					:       List[str]     				=					LlamaForSequenceClassification(_a				)
								model.to(_a				)
								model.eval()
								lowercase_					:       Optional[int]     				=					model(_a       ,				attention_mask=_a       ,				labels=_a				)
								self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)
			def 		_lowerCamelCase (self				)    ->     int:
								lowercase_					:       Optional[Any]     				=					self.model_tester.prepare_config_and_inputs_for_common()
								lowercase_					:       Dict     				=					3
								lowercase_					:       Dict     				=					'multi_label_classification'
								lowercase_					:       List[Any]     				=					input_dict['input_ids']
								lowercase_					:       Any     				=					input_ids.ne(1				).to(_a				)
								lowercase_					:       Optional[int]     				=					ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]       ,				self.model_tester.type_sequence_label_size				).to(torch.float				)
								lowercase_					:       Union[str, Any]     				=					LlamaForSequenceClassification(_a				)
								model.to(_a				)
								model.eval()
								lowercase_					:       List[Any]     				=					model(_a       ,				attention_mask=_a       ,				labels=_a				)
								self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)				)
			@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'				)
			def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
								pass





			@parameterized.expand([('linear',), ('dynamic',)]				)
			def 		_lowerCamelCase (self       ,				_a				)    ->     Tuple:
								lowercase_					:       List[str]     				=					self.model_tester.prepare_config_and_inputs_for_common()
								lowercase_					:       Union[str, Any]     				=					ids_tensor([1, 10]       ,				config.vocab_size				)
								lowercase_					:       Optional[int]     				=					ids_tensor([1, int(config.max_position_embeddings * 1.5				)]       ,				config.vocab_size				)

								set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
								lowercase_					:       Tuple     				=					LlamaModel(_a				)
								original_model.to(_a				)
								original_model.eval()
								lowercase_					:       Optional[Any]     				=					original_model(_a				).last_hidden_state
								lowercase_					:       int     				=					original_model(_a				).last_hidden_state

								set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
								lowercase_					:       List[Any]     				=					{'type': scaling_type, 'factor': 10.0}
								lowercase_					:       List[Any]     				=					LlamaModel(_a				)
								scaled_model.to(_a				)
								scaled_model.eval()
								lowercase_					:       Dict     				=					scaled_model(_a				).last_hidden_state
								lowercase_					:       Any     				=					scaled_model(_a				).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
													self.assertTrue(torch.allclose(_a       ,				_a       ,				atol=1e-5				)				)
								else:
													self.assertFalse(torch.allclose(_a       ,				_a       ,				atol=1e-5				)				)

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(_a       ,				_a       ,				atol=1e-5				)				)



@require_torch
class       UpperCAmelCase__  (       unittest.TestCase			):






			"""simple docstring"""
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'				)
			@slow
			def 		_lowerCamelCase (self				)    ->     List[str]:
								lowercase_					:       int     				=					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								lowercase_					:       List[str]     				=					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'       ,				device_map='auto'				)
								lowercase_					:       int     				=					model(torch.tensor([input_ids]				)				)
								# Expected mean on dim = -1
								lowercase_					:       List[str]     				=					torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]]				)
								torch.testing.assert_close(out.mean(-1				)       ,				_a       ,				atol=1e-2       ,				rtol=1e-2				)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								lowercase_					:       Union[str, Any]     				=					torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]				)
								# fmt: on
								torch.testing.assert_close(out[0, 0, :30]       ,				_a       ,				atol=1e-5       ,				rtol=1e-5				)
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'				)
			@slow
			def 		_lowerCamelCase (self				)    ->     Dict:
								lowercase_					:       Tuple     				=					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								lowercase_					:       List[Any]     				=					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'       ,				device_map='auto'				)
								lowercase_					:       Tuple     				=					model(torch.tensor(_a				)				)
								# Expected mean on dim = -1
								lowercase_					:       Optional[Any]     				=					torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]]				)
								torch.testing.assert_close(out.mean(-1				)       ,				_a       ,				atol=1e-2       ,				rtol=1e-2				)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								lowercase_					:       Any     				=					torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73]				)
								# fmt: on
								torch.testing.assert_close(out[0, 0, :30]       ,				_a       ,				atol=1e-5       ,				rtol=1e-5				)
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'				)
			@slow
			def 		_lowerCamelCase (self				)    ->     Optional[Any]:
								lowercase_					:       Optional[int]     				=					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								lowercase_					:       Union[str, Any]     				=					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'       ,				device_map='auto'				)
								lowercase_					:       Optional[Any]     				=					model(torch.tensor(_a				)				)
								# Expected mean on dim = -1
								lowercase_					:       str     				=					torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]]				)
								torch.testing.assert_close(out.mean(-1				)       ,				_a       ,				atol=1e-2       ,				rtol=1e-2				)
								# slicing logits[0, 0, 0:30]
								# fmt: off
								lowercase_					:       Dict     				=					torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13]				)
								# fmt: on
								torch.testing.assert_close(out.mean(-1				)       ,				_a       ,				atol=1e-2       ,				rtol=1e-2				)
			@unittest.skip(
			    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'				)
			@slow
			def 		_lowerCamelCase (self				)    ->     Optional[Any]:
								lowercase_					:       Union[str, Any]     				=					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
								lowercase_					:       Any     				=					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'       ,				device_map='auto'				)
								lowercase_					:       Any     				=					model(torch.tensor(_a				)				)

								lowercase_					:       Union[str, Any]     				=					torch.tensor(
								    [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]       ,				dtype=torch.floataa				)
								torch.testing.assert_close(out.mean(-1				)       ,				_a       ,				atol=1e-2       ,				rtol=1e-2				)
								# fmt: off
								lowercase_					:       Tuple     				=					torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12]				)
								# fmt: on
								torch.testing.assert_close(out[0, 0, :30]       ,				_a       ,				atol=1e-5       ,				rtol=1e-5				)





			@unittest.skip('Model is curently gated'				)
			@slow
			def 		_lowerCamelCase (self				)    ->     List[str]:
								lowercase_					:       Optional[Any]     				=					'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
								lowercase_					:       List[Any]     				=					'Simply put, the theory of relativity states that '
								lowercase_					:       str     				=					LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'				)
								lowercase_					:       str     				=					tokenizer.encode(_a       ,				return_tensors='pt'				)
								lowercase_					:       List[str]     				=					LlamaForCausalLM.from_pretrained(
								    'meta-llama/Llama-2-13b-chat-hf'       ,				device_map='sequential'       ,				use_safetensors=_a				)

								# greedy generation outputs
								lowercase_					:       Optional[Any]     				=					model.generate(_a       ,				max_new_tokens=64       ,				top_p=_a       ,				temperature=1       ,				do_sample=_a				)
								lowercase_					:       Optional[int]     				=					tokenizer.decode(generated_ids[0]       ,				skip_special_tokens=_a				)
								self.assertEqual(_a       ,				_a				)





'''simple docstring'''




import os

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(grid[0]							)
     lowercase_					:       Union[str, Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					0
     lowercase_					:       int     				=					0
     lowercase_					:       Union[str, Any]     				=					0

     # Check vertically, horizontally, diagonally at the same time (only works
     # for nxn grid)
     for i in range(SCREAMING_SNAKE_CASE_							):
          for j in range(n_rows - 3							):
               lowercase_					:       Dict     				=					grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
               lowercase_					:       Optional[Any]     				=					grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

               # Left-to-right diagonal (\) product
               if i < n_columns - 3:
                    lowercase_					:       Tuple     				=					(
                        grid[i][j]
                        * grid[i + 1][j + 1]
                        * grid[i + 2][j + 2]
                        * grid[i + 3][j + 3]
                    )

               # Right-to-left diagonal(/) product
               if i > 2:
                    lowercase_					:       int     				=					(
                        grid[i][j]
                        * grid[i - 1][j + 1]
                        * grid[i - 2][j + 2]
                        * grid[i - 3][j + 3]
                    )

               lowercase_					:       Optional[int]     				=					max(
                   SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               if max_product > largest:
                    lowercase_					:       List[Any]     				=					max_product

     return largest

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					[]
     with open(os.path.dirname(SCREAMING_SNAKE_CASE_							) + '/grid.txt'							) as file:
          for line in file:
               grid.append(line.strip('\n'							).split(' '							)							)

     lowercase_					:       Dict     				=					[[int(SCREAMING_SNAKE_CASE_							) for i in grid[j]] for j in range(len(SCREAMING_SNAKE_CASE_							)							)]

     return largest_product(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
 print(solution())




