



'''simple docstring'''





from __future__ import annotations






class 	lowercase		:






	"""simple docstring"""


	def __init__(			self			:		Optional[Any]		,						__UpperCAmelCase			:		int  )    ->				None:
							UpperCAmelCase_=     vertices
							UpperCAmelCase_=     [[0] * vertices for _ in range(__UpperCAmelCase  )]




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		list[int]  )    ->				None:
							print("""Vertex \t Distance from Source"""  )
							for vertex in range(self.vertices  ):
													print(__UpperCAmelCase		,						"""\t\t"""		,						distances_from_source[vertex]  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		list[int]		,						__UpperCAmelCase			:		list[bool]  )    ->				int:
							UpperCAmelCase_=     1E7
							UpperCAmelCase_=     0

							# Search not nearest vertex not in the shortest path tree
							for vertex in range(self.vertices  ):
													if distances_from_source[vertex] < minimum and visited[vertex] is False:
																			UpperCAmelCase_=     distances_from_source[vertex]
																			UpperCAmelCase_=     vertex
							return min_index




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		int  )    ->				None:
							UpperCAmelCase_=     [int(1E7  )] * self.vertices  # distances from the source
							UpperCAmelCase_=     0
							UpperCAmelCase_=     [False] * self.vertices

							for _ in range(self.vertices  ):
													UpperCAmelCase_=     self.minimum_distance(__UpperCAmelCase		,						__UpperCAmelCase  )
													UpperCAmelCase_=     True

													# Update dist value of the adjacent vertices
													# of the picked vertex only if the current
													# distance is greater than new distance and
													# the vertex in not in the shortest path tree
													for v in range(self.vertices  ):
																			if (
																			    self.graph[u][v] > 0
																			    and visited[v] is False
																			    and distances[v] > distances[u] + self.graph[u][v]
																			):
																									UpperCAmelCase_=     distances[u] + self.graph[u][v]

							self.print_solution(__UpperCAmelCase  )


if __name__ == "__main__":
	__A       					=		Graph(9)
	__A       					=		[
	    [0, 4, 0, 0, 0, 0, 0, 8, 0],
	    [4, 0, 8, 0, 0, 0, 0, 11, 0],
	    [0, 8, 0, 7, 0, 4, 0, 0, 2],
	    [0, 0, 7, 0, 9, 14, 0, 0, 0],
	    [0, 0, 0, 9, 0, 10, 0, 0, 0],
	    [0, 0, 4, 14, 10, 0, 2, 0, 0],
	    [0, 0, 0, 0, 0, 2, 0, 1, 6],
	    [8, 11, 0, 0, 0, 0, 1, 0, 7],
	    [0, 0, 2, 0, 0, 0, 6, 7, 0],
	]
	graph.dijkstra(0)









import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
	import torch






class 	lowercase		:






	"""simple docstring"""


	def __init__(			self			:		int		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]=14		,						__UpperCAmelCase			:		Tuple=7		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Any=True		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		Optional[Any]=99		,						__UpperCAmelCase			:		str=32		,						__UpperCAmelCase			:		Any=4		,						__UpperCAmelCase			:		Optional[Any]=4		,						__UpperCAmelCase			:		Union[str, Any]=[[["global", "local"], 2]]		,						__UpperCAmelCase			:		int=37		,						__UpperCAmelCase			:		str="gelu"		,						__UpperCAmelCase			:		int=0.1		,						__UpperCAmelCase			:		Tuple=0.1		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=7		,						__UpperCAmelCase			:		List[str]=0.02		,						)    ->				Union[str, Any]:
							UpperCAmelCase_=     parent
							UpperCAmelCase_=     batch_size
							UpperCAmelCase_=     seq_length
							UpperCAmelCase_=     is_training
							UpperCAmelCase_=     use_input_mask
							UpperCAmelCase_=     use_token_type_ids
							UpperCAmelCase_=     use_labels
							UpperCAmelCase_=     vocab_size
							UpperCAmelCase_=     hidden_size
							UpperCAmelCase_=     num_hidden_layers
							UpperCAmelCase_=     num_attention_heads
							UpperCAmelCase_=     attention_types
							UpperCAmelCase_=     intermediate_size
							UpperCAmelCase_=     hidden_act
							UpperCAmelCase_=     hidden_dropout_prob
							UpperCAmelCase_=     attention_probs_dropout_prob
							UpperCAmelCase_=     max_position_embeddings
							UpperCAmelCase_=     window_size
							UpperCAmelCase_=     initializer_range
							UpperCAmelCase_=     None
							UpperCAmelCase_=     vocab_size - 1
							UpperCAmelCase_=     vocab_size - 1
							UpperCAmelCase_=     vocab_size - 1




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Optional[Any]:
							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size  )

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

							UpperCAmelCase_=     GPTNeoConfig(
							    vocab_size=self.vocab_size		,						hidden_size=self.hidden_size		,						num_layers=self.num_hidden_layers		,						num_heads=self.num_attention_heads		,						max_position_embeddings=self.max_position_embeddings		,						use_cache=__UpperCAmelCase		,						bos_token_id=self.bos_token_id		,						eos_token_id=self.eos_token_id		,						pad_token_id=self.pad_token_id		,						window_size=self.window_size		,						attention_types=self.attention_types		,						)

							return (config, input_ids, input_mask)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Dict:
							UpperCAmelCase_=     self.prepare_config_and_inputs()
							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     config_and_inputs
							UpperCAmelCase_=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
							return config, inputs_dict




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Dict:
							UpperCAmelCase_=     20
							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )

							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
							UpperCAmelCase_=     jnp.ones((input_ids.shape[0], max_decoder_length)		,						dtype="""i4"""  )

							UpperCAmelCase_=     jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
							UpperCAmelCase_=     model(
							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)

							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
							UpperCAmelCase_=     model(
							    input_ids[:, -1:]		,						attention_mask=__UpperCAmelCase		,						past_key_values=outputs_cache.past_key_values		,						position_ids=__UpperCAmelCase		,						)

							UpperCAmelCase_=     model(__UpperCAmelCase  )

							UpperCAmelCase_=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
							self.parent.assertTrue(diff < 1E-3		,						msg=F"""Max diff is {diff}"""  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Dict  )    ->				List[Any]:
							UpperCAmelCase_=     20
							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )

							UpperCAmelCase_=     jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]		,						axis=-1		,						)

							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
							UpperCAmelCase_=     jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )

							UpperCAmelCase_=     model(
							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
							UpperCAmelCase_=     model(
							    input_ids[:, -1:]		,						past_key_values=outputs_cache.past_key_values		,						attention_mask=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)

							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase  )

							UpperCAmelCase_=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
							self.parent.assertTrue(diff < 1E-3		,						msg=F"""Max diff is {diff}"""  )






@require_flax
class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):






	"""simple docstring"""


	a__   :      List[Any]	     =							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	a__   :      List[Any]	     =							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Any:
							UpperCAmelCase_=     FlaxGPTNeoModelTester(self  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				str:
							for model_class_name in self.all_model_classes:
													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Any:
							for model_class_name in self.all_model_classes:
													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
													self.model_tester.check_use_cache_forward_with_attn_mask(
													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	@slow
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Union[str, Any]:
							UpperCAmelCase_=     GPTaTokenizer.from_pretrained("""gpt2"""		,						pad_token="""<|endoftext|>"""		,						padding_side="""left"""  )
							UpperCAmelCase_=     tokenizer(["""Hello this is a long string""", """Hey"""]		,						return_tensors="""np"""		,						padding=__UpperCAmelCase		,						truncation=__UpperCAmelCase  )

							UpperCAmelCase_=     FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
							UpperCAmelCase_=     False
							UpperCAmelCase_=     model.config.eos_token_id

							UpperCAmelCase_=     jax.jit(model.generate  )

							UpperCAmelCase_=     jit_generate(
							    inputs["""input_ids"""]		,						attention_mask=inputs["""attention_mask"""]		,						pad_token_id=tokenizer.pad_token_id  ).sequences

							UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )

							UpperCAmelCase_=     [
							    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
							    """Hey, I'm a little late to the party. I'm going to""",
							]

							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )




	@is_pt_flax_cross_test
	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Union[str, Any]:
							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																			# prepare inputs
																			UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )

																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
																									UpperCAmelCase_=     0
																									UpperCAmelCase_=     1
																									UpperCAmelCase_=     0
																									UpperCAmelCase_=     1
																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )

																			UpperCAmelCase_=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,						__UpperCAmelCase  )
																			UpperCAmelCase_=     fx_state

																			with torch.no_grad():
																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()

																			UpperCAmelCase_=     fx_model(**__UpperCAmelCase  ).to_tuple()
																			self.assertEqual(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									pt_model.save_pretrained(__UpperCAmelCase  )
																									UpperCAmelCase_=     model_class.from_pretrained(__UpperCAmelCase		,						from_pt=__UpperCAmelCase  )

																			UpperCAmelCase_=     fx_model_loaded(**__UpperCAmelCase  ).to_tuple()
																			self.assertEqual(
																			    len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
																			for fx_output_loaded, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
																									self.assert_almost_equals(fx_output_loaded[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )




	@is_pt_flax_cross_test
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Dict:
							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__  ):
																			# prepare inputs
																			UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )

																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )

																			UpperCAmelCase_=     load_flax_weights_in_pytorch_model(__UpperCAmelCase		,						fx_model.params  )
																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
																									UpperCAmelCase_=     0
																									UpperCAmelCase_=     1
																									UpperCAmelCase_=     0
																									UpperCAmelCase_=     1

																			# make sure weights are tied in PyTorch
																			pt_model.tie_weights()

																			with torch.no_grad():
																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()

																			UpperCAmelCase_=     fx_model(**__UpperCAmelCase  ).to_tuple()
																			self.assertEqual(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																									fx_model.save_pretrained(__UpperCAmelCase  )
																									UpperCAmelCase_=     pt_model_class.from_pretrained(__UpperCAmelCase		,						from_flax=__UpperCAmelCase  )

																			with torch.no_grad():
																									UpperCAmelCase_=     pt_model_loaded(**__UpperCAmelCase  ).to_tuple()

																			self.assertEqual(
																			    len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )




	@slow
	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Dict:
							for model_class_name in self.all_model_classes:
													UpperCAmelCase_=     model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
													UpperCAmelCase_=     model(np.ones((1, 1)  )  )
													self.assertIsNotNone(__UpperCAmelCase  )




