






import unittest

from transformers import AutoTokenizer, FalconConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    FalconForCausalLM,
							    FalconForQuestionAnswering,
							    FalconForSequenceClassification,
							    FalconForTokenClassification,
							    FalconModel,
							)


class 	__SCREAMING_SNAKE_CASE:
							def __init__(  self:   List[Any]							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   Tuple=3							,						UpperCamelCase:   List[Any]=7							,						UpperCamelCase:   Tuple=True							,						UpperCamelCase:   List[str]=True							,						UpperCamelCase:   Optional[Any]=False							,						UpperCamelCase:   Union[str, Any]=True							,						UpperCamelCase:   Tuple=99							,						UpperCamelCase:   Dict=32							,						UpperCamelCase:   Tuple=5							,						UpperCamelCase:   str=4							,						UpperCamelCase:   List[Any]=37							,						UpperCamelCase:   int="gelu"							,						UpperCamelCase:   Optional[int]=0.1							,						UpperCamelCase:   Any=0.1							,						UpperCamelCase:   int=5_12							,						UpperCamelCase:   List[Any]=16							,						UpperCamelCase:   Tuple=2							,						UpperCamelCase:   List[str]=0.02							,						UpperCamelCase:   List[str]=3							,						UpperCamelCase:   Any=4							,						UpperCamelCase:   List[Any]=None							,						)		->		Tuple:
											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
							def lowerCAmelCase_			(  self:   List[str]    )		->		int:
											snake_case__						=  ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

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

											snake_case__						=  None

											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__						=  self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
							def lowerCAmelCase_			(  self:   Dict    )		->		Tuple:
											return FalconConfig(
											    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=UpperCamelCase							,						initializer_range=self.initializer_range							,						pad_token_id=1							,						new_decoder_architecture=UpperCamelCase							,						)
							def lowerCAmelCase_			(  self:   str							,						UpperCamelCase:   Dict							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   Dict							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   Any							,						UpperCamelCase:   Optional[Any]							,						UpperCamelCase:   str    )		->		str:
											snake_case__						=  FalconModel(config=UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(UpperCamelCase							,						attention_mask=UpperCamelCase    )
											snake_case__						=  model(UpperCamelCase    )
											self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)    )
							def lowerCAmelCase_			(  self:   Dict							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   Dict							,						UpperCamelCase:   Any							,						UpperCamelCase:   str							,						UpperCamelCase:   int							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Optional[Any]							,						UpperCamelCase:   List[Any]							,						UpperCamelCase:   List[Any]							,						)		->		List[Any]:
											snake_case__						=  True
											snake_case__						=  FalconModel(UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(
											    UpperCamelCase							,						attention_mask=UpperCamelCase							,						encoder_hidden_states=UpperCamelCase							,						encoder_attention_mask=UpperCamelCase							,						)
											snake_case__						=  model(
											    UpperCamelCase							,						attention_mask=UpperCamelCase							,						encoder_hidden_states=UpperCamelCase							,						)
											snake_case__						=  model(UpperCamelCase							,						attention_mask=UpperCamelCase    )
											self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)    )
							def lowerCAmelCase_			(  self:   str							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   str							,						UpperCamelCase:   Tuple							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   Union[str, Any]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   str							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   Optional[int]							,						)		->		Tuple:
											snake_case__						=  FalconForCausalLM(config=UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(UpperCamelCase							,						attention_mask=UpperCamelCase							,						labels=UpperCamelCase    )
											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)    )
							def lowerCAmelCase_			(  self:   Optional[int]							,						UpperCamelCase:   Any							,						UpperCamelCase:   Tuple							,						UpperCamelCase:   int							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Dict							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Optional[int]							,						UpperCamelCase:   List[str]							,						UpperCamelCase:   Any							,						)		->		Any:
											snake_case__						=  True
											snake_case__						=  True
											snake_case__						=  FalconForCausalLM(config=UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()

											# first forward pass
											snake_case__						=  model(
											    UpperCamelCase							,						attention_mask=UpperCamelCase							,						encoder_hidden_states=UpperCamelCase							,						encoder_attention_mask=UpperCamelCase							,						use_cache=UpperCamelCase							,						)
											snake_case__						=  outputs.past_key_values

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

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

											snake_case__						=  model(
											    UpperCamelCase							,						attention_mask=UpperCamelCase							,						encoder_hidden_states=UpperCamelCase							,						encoder_attention_mask=UpperCamelCase							,						output_hidden_states=UpperCamelCase							,						)['hidden_states'][0]
											snake_case__						=  model(
											    UpperCamelCase							,						attention_mask=UpperCamelCase							,						encoder_hidden_states=UpperCamelCase							,						encoder_attention_mask=UpperCamelCase							,						past_key_values=UpperCamelCase							,						output_hidden_states=UpperCamelCase							,						)['hidden_states'][0]

											# select random slice
											snake_case__						=  ids_tensor((1,)							,						output_from_past.shape[-1]    ).item()
											snake_case__						=  output_from_no_past[:, -3:, random_slice_idx].detach()
											snake_case__						=  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(UpperCamelCase							,						UpperCamelCase							,						atol=1e-3    )    )


							def lowerCAmelCase_			(  self:   Any    )		->		str:
											snake_case__						=  self.prepare_config_and_inputs()
											(
											    (
											    snake_case__							
											)				,    (
											    snake_case__							
											)				,    (
											    snake_case__							
											)				,    (
											    snake_case__							
											)				,    (
											    snake_case__							
											)				,    (
											    snake_case__							
											)				,    (
											    snake_case__							
											)				,    							
											)						=  config_and_inputs
											snake_case__						=  {'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict


@require_torch
class 	__SCREAMING_SNAKE_CASE(						__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	unittest.TestCase						):
							_UpperCAmelCase			     =							(
							    (
							        FalconModel,
							        FalconForCausalLM,
							        FalconForSequenceClassification,
							        FalconForTokenClassification,
							        FalconForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							_UpperCAmelCase			     =							(FalconForCausalLM,) if is_torch_available() else ()
							_UpperCAmelCase			     =							(
							    {
							        'feature-extraction': FalconModel,
							        'text-classification': FalconForSequenceClassification,
							        'text-generation': FalconForCausalLM,
							        'question-answering': FalconForQuestionAnswering,
							        'token-classification': FalconForTokenClassification,
							        'zero-shot': FalconForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_UpperCAmelCase			     =							False
							_UpperCAmelCase			     =							False
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		Union[str, Any]:
											snake_case__						=  FalconModelTester(self    )
											snake_case__						=  ConfigTester(self							,						config_class=UpperCamelCase							,						hidden_size=37    )
							def lowerCAmelCase_			(  self:   Optional[int]    )		->		Dict:
											self.config_tester.run_common_tests()
							def lowerCAmelCase_			(  self:   Dict    )		->		Union[str, Any]:
											snake_case__						=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase    )
							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		List[str]:
											snake_case__				,    *snake_case__						=  self.model_tester.prepare_config_and_inputs()
											for alibi in [True, False]:
															snake_case__						=  alibi
															self.model_tester.create_and_check_model(UpperCamelCase							,						*UpperCamelCase    )
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		List[str]:
											snake_case__				,    snake_case__						=  self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__						=  3
											snake_case__						=  input_dict['input_ids']
											snake_case__						=  input_ids.ne(1    ).to(UpperCamelCase    )
											snake_case__						=  ids_tensor([self.model_tester.batch_size]							,						self.model_tester.type_sequence_label_size    )
											snake_case__						=  FalconForSequenceClassification(UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(UpperCamelCase							,						attention_mask=UpperCamelCase							,						labels=UpperCamelCase    )
											self.assertEqual(result.logits.shape							,						(self.model_tester.batch_size, self.model_tester.num_labels)    )
							def lowerCAmelCase_			(  self:   List[Any]    )		->		Optional[Any]:
											snake_case__				,    snake_case__						=  self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__						=  3
											snake_case__						=  'single_label_classification'
											snake_case__						=  input_dict['input_ids']
											snake_case__						=  input_ids.ne(1    ).to(UpperCamelCase    )
											snake_case__						=  ids_tensor([self.model_tester.batch_size]							,						self.model_tester.type_sequence_label_size    )
											snake_case__						=  FalconForSequenceClassification(UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(UpperCamelCase							,						attention_mask=UpperCamelCase							,						labels=UpperCamelCase    )
											self.assertEqual(result.logits.shape							,						(self.model_tester.batch_size, self.model_tester.num_labels)    )
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		Optional[int]:
											snake_case__				,    snake_case__						=  self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__						=  input_dict['input_ids']
											snake_case__						=  FalconForCausalLM(UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(UpperCamelCase							,						use_cache=UpperCamelCase    )
											snake_case__						=  input_ids.shape[0]
											snake_case__						=  model._convert_to_rw_cache(result.past_key_values    )
											snake_case__						=  model._convert_cache_to_standard_format(UpperCamelCase							,						UpperCamelCase    )
											for layer in range(len(UpperCamelCase    )    ):
															for tensor_idx in range(2    ):
																			self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3    )
																			self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4    )
																			self.assertTrue(
																			    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]    )    )
							def lowerCAmelCase_			(  self:   Tuple    )		->		List[str]:
											snake_case__				,    snake_case__						=  self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__						=  3
											snake_case__						=  'multi_label_classification'
											snake_case__						=  input_dict['input_ids']
											snake_case__						=  input_ids.ne(1    ).to(UpperCamelCase    )
											snake_case__						=  ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]							,						self.model_tester.type_sequence_label_size    ).to(torch.float    )
											snake_case__						=  FalconForSequenceClassification(UpperCamelCase    )
											model.to(UpperCamelCase    )
											model.eval()
											snake_case__						=  model(UpperCamelCase							,						attention_mask=UpperCamelCase							,						labels=UpperCamelCase    )
											self.assertEqual(result.logits.shape							,						(self.model_tester.batch_size, self.model_tester.num_labels)    )


							def lowerCAmelCase_			(  self:   Optional[Any]    )		->		str:






											for model_class in self.all_generative_model_classes:
															snake_case__				,    snake_case__						=  self.model_tester.prepare_config_and_inputs_for_common()

															# If it doesn't support cache, pass the test
															if not hasattr(UpperCamelCase							,						'use_cache'    ):
																			return

															snake_case__						=  model_class(UpperCamelCase    ).to(UpperCamelCase    )
															if "use_cache" not in inputs:
																			snake_case__						=  True
															snake_case__						=  model(**UpperCamelCase    )

															# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
															if "past_key_values" not in outputs:
																			return

															snake_case__						=  (
															    getattr(UpperCamelCase							,						'decoder_layers'							,						UpperCamelCase    )
															    or getattr(UpperCamelCase							,						'num_decoder_layers'							,						UpperCamelCase    )
															    or config.num_hidden_layers
															)
															snake_case__						=  getattr(UpperCamelCase							,						'num_kv_heads'							,						config.num_attention_heads    )
															snake_case__						=  getattr(UpperCamelCase							,						'd_model'							,						config.hidden_size    )
															snake_case__						=  embed_dim // num_attention_heads

															snake_case__						=  outputs['past_key_values']
															self.assertEqual(len(UpperCamelCase    )							,						UpperCamelCase    )

															snake_case__				,    snake_case__						=  inputs['input_ids'].shape
															for i in range(UpperCamelCase    ):
																			if config.new_decoder_architecture:
																							snake_case__						=  config.num_attention_heads
																			elif config.multi_query:
																							snake_case__						=  1
																			self.assertEqual(len(past_kv[0]    )							,						2    )  # K V for the decoder = 2
																			self.assertEqual(
																			    past_kv[i][0].shape							,						(batch_size, num_attention_heads, seq_length, per_head_embed_dim)    )
																			self.assertEqual(
																			    past_kv[i][1].shape							,						(batch_size, num_attention_heads, seq_length, per_head_embed_dim)    )


@require_torch
class 	__SCREAMING_SNAKE_CASE(						unittest.TestCase						):
							@slow
							def lowerCAmelCase_			(  self:   List[Any]    )		->		Optional[int]:
											snake_case__						=  AutoTokenizer.from_pretrained('Rocketknight1/falcon-rw-1b'    )
											snake_case__						=  FalconForCausalLM.from_pretrained('Rocketknight1/falcon-rw-1b'    )
											model.eval()
											model.to(UpperCamelCase    )
											snake_case__						=  tokenizer('My favorite food is'							,						return_tensors='pt'    ).to(UpperCamelCase    )

											snake_case__						=  (
											    'My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'
											)

											snake_case__						=  model.generate(**UpperCamelCase							,						do_sample=UpperCamelCase							,						max_new_tokens=19    )
											snake_case__						=  tokenizer.batch_decode(UpperCamelCase    )[0]

											self.assertEqual(UpperCamelCase							,						UpperCamelCase    )
							@slow
							def lowerCAmelCase_			(  self:   Union[str, Any]    )		->		List[Any]:






											for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
															snake_case__						=  AutoTokenizer.from_pretrained(UpperCamelCase    )
															snake_case__						=  FalconForCausalLM.from_pretrained(UpperCamelCase    )
															model.eval()
															model.to(UpperCamelCase    )
															snake_case__						=  tokenizer('My favorite food is'							,						return_tensors='pt'    ).to(UpperCamelCase    )

															# We just test that these run without errors - the models are randomly initialized
															# and so the actual text outputs will be garbage
															model.generate(**UpperCamelCase							,						do_sample=UpperCamelCase							,						max_new_tokens=4    )
															model.generate(**UpperCamelCase							,						do_sample=UpperCamelCase							,						max_new_tokens=4    )
															model.generate(**UpperCamelCase							,						num_beams=2							,						max_new_tokens=4    )


							@slow
							def lowerCAmelCase_			(  self:   List[Any]    )		->		int:






											with torch.no_grad():
															for repo in [
															    "Rocketknight1/falcon-rw-1b",
															    "Rocketknight1/tiny-random-falcon-7b",
															    "Rocketknight1/tiny-random-falcon-40b",
															]:
																			snake_case__						=  AutoTokenizer.from_pretrained(UpperCamelCase    )
																			snake_case__						=  FalconForCausalLM.from_pretrained(UpperCamelCase    )
																			model.eval()
																			model.to(device=UpperCamelCase    )
																			snake_case__						=  tokenizer('My favorite food is'							,						return_tensors='pt'    ).to(UpperCamelCase    )

																			# Test results are the same with and without cache
																			snake_case__						=  model.generate(**UpperCamelCase							,						do_sample=UpperCamelCase							,						max_new_tokens=20							,						use_cache=UpperCamelCase    )
																			snake_case__						=  model.generate(**UpperCamelCase							,						do_sample=UpperCamelCase							,						max_new_tokens=20							,						use_cache=UpperCamelCase    )
																			self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0    )






import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

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

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

			return dp[-1][-1]


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