
'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch


class 	UpperCamelCase__					:





 def __init__(     self					:Any		,			_A					:Optional[int]		,			_A					:List[Any]=14		,			_A					:int=7		,			_A					:Dict=True		,			_A					:Optional[Any]=True		,			_A					:Dict=False		,			_A					:Tuple=True		,			_A					:Union[str, Any]=99		,			_A					:List[str]=32		,			_A					:Any=5		,			_A					:Any=4		,			_A					:Any=37		,			_A					:Optional[Any]="gelu"		,			_A					:Tuple=0.1		,			_A					:Dict=0.1		,			_A					:Union[str, Any]=512		,			_A					:str=0.02		,			)			->     str:







       '''simple docstring'''



       __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    		=     initializer_range
       __A    		=     None
       __A    		=     vocab_size - 1
       __A    		=     vocab_size - 1
       __A    		=     vocab_size - 1





 def 		lowercase_  (     self					:Optional[int]     )			->     Optional[Any]:







       '''simple docstring'''



       __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    		=     GPTaConfig(
           vocab_size=self.vocab_size		,			n_embd=self.hidden_size		,			n_layer=self.num_hidden_layers		,			n_head=self.num_attention_heads		,			n_positions=self.max_position_embeddings		,			use_cache=_A		,			bos_token_id=self.bos_token_id		,			eos_token_id=self.eos_token_id		,			pad_token_id=self.pad_token_id		,			)

       return (config, input_ids, input_mask)





 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       __A    		=     self.prepare_config_and_inputs()
       __A    		=     config_and_inputs
       __A    		=     {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict





 def 		lowercase_  (     self					:List[Any]     )			->     List[str]:







       '''simple docstring'''



       __A    		=     self.prepare_config_and_inputs()

       __A    		=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
       __A    		=     ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2     )

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )





 def 		lowercase_  (     self					:Optional[int]		,			_A					:List[str]		,			_A					:Any		,			_A					:Dict		,			_A					:str     )			->     int:







       '''simple docstring'''



       __A    		=     20
       __A    		=     model_class_name(_A     )

       __A    		=     model.init_cache(input_ids.shape[0]		,			_A     )
       __A    		=     jnp.ones((input_ids.shape[0], max_decoder_length)		,			dtype='i4'     )

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

       __A    		=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='i4'     )
       __A    		=     model(
           input_ids[:, -1:]		,			attention_mask=_A		,			past_key_values=outputs_cache.past_key_values		,			position_ids=_A		,			)

       __A    		=     model(_A     )

       __A    		=     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 		lowercase_  (     self					:Optional[Any]		,			_A					:Tuple		,			_A					:Any		,			_A					:List[Any]		,			_A					:Any     )			->     List[str]:







       '''simple docstring'''



       __A    		=     20
       __A    		=     model_class_name(_A     )

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

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

       __A    		=     model(
           input_ids[:, :-1]		,			attention_mask=_A		,			past_key_values=_A		,			position_ids=_A		,			)
       __A    		=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,			dtype='i4'     )
       __A    		=     model(
           input_ids[:, -1:]		,			past_key_values=outputs_cache.past_key_values		,			attention_mask=_A		,			position_ids=_A		,			)

       __A    		=     model(_A		,			attention_mask=_A     )

       __A    		=     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 	UpperCamelCase__					(      snake_case__ ,       snake_case__ ,       unittest.TestCase):
 UpperCAmelCase__      :      int  			=   (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 UpperCAmelCase__      :      Dict  			=   (FlaxGPTaLMHeadModel,) if is_flax_available() else ()





 def 		lowercase_  (     self					:Optional[int]     )			->     Union[str, Any]:







       '''simple docstring'''



       __A    		=     FlaxGPTaModelTester(self     )





 def 		lowercase_  (     self					:Any     )			->     Optional[Any]:







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
             __A    		=     self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_A		,			_A		,			_A		,			_A     )





 def 		lowercase_  (     self					:Optional[Any]     )			->     Optional[Any]:







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
             __A    		=     self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _A		,			_A		,			_A		,			_A     )





 @slow
 def 		lowercase_  (     self					:Optional[int]     )			->     int:







       '''simple docstring'''



       __A    		=     GPTaTokenizer.from_pretrained('gpt2'		,			pad_token='</s>'		,			padding_side='left'     )
       __A    		=     tokenizer(['Hello this is a long string', 'Hey']		,			return_tensors='np'		,			padding=_A		,			truncation=_A     )

       __A    		=     FlaxGPTaLMHeadModel.from_pretrained('gpt2'     )
       __A    		=     False
       __A    		=     model.config.eos_token_id

       __A    		=     jax.jit(model.generate     )

       __A    		=     jit_generate(inputs['input_ids']		,			attention_mask=inputs['attention_mask']     ).sequences

       __A    		=     tokenizer.batch_decode(_A		,			skip_special_tokens=_A     )

       __A    		=     [
           "Hello this is a long string of words. I'm going to start with the first one.\n",
           "Hey, I'm not sure if I'm going to be able to do",
       ]

       self.assertListEqual(_A		,			_A     )





 @is_pt_flax_cross_test
 def 		lowercase_  (     self					:Optional[int]     )			->     Dict:







       '''simple docstring'''



       __A    		=     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
                   __A    		=     self._prepare_for_class(_A		,			_A     )
                   __A    		=     {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

                   __A    		=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,			_A     )
                   __A    		=     fx_state

                   with torch.no_grad():
                         __A    		=     pt_model(**_A     ).to_tuple()

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

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

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





 @is_pt_flax_cross_test
 def 		lowercase_  (     self					:int     )			->     Tuple:







       '''simple docstring'''



       __A    		=     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
                   __A    		=     self._prepare_for_class(_A		,			_A     )
                   __A    		=     {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

                   __A    		=     pt_model_class(_A     ).eval()
                   __A    		=     model_class(_A		,			dtype=jnp.floataa     )

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

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

                   with torch.no_grad():
                         __A    		=     pt_model(**_A     ).to_tuple()

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

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

                   with torch.no_grad():
                         __A    		=     pt_model_loaded(**_A     ).to_tuple()

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





 @slow
 def 		lowercase_  (     self					:Union[str, Any]     )			->     Tuple:







       '''simple docstring'''



       for model_class_name in self.all_model_classes:
             __A    		=     model_class_name.from_pretrained('gpt2'		,			from_pt=_A     )
             __A    		=     model(np.ones((1, 1)     )     )
             self.assertIsNotNone(_A     )








'''simple docstring'''





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      print('\nVertex Distance'		)
      for i in range(UpperCAmelCase		):
            if dist[i] != float('inf'		):
                  print(UpperCAmelCase ,					'\t' ,					int(dist[i]		) ,					end='\t'		)
            else:
                  print(UpperCAmelCase ,					'\t' ,					'INF' ,					end='\t'		)
            print()





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      __A    		=     float('inf'		)
      __A    		=     -1
      for i in range(UpperCAmelCase		):
            if (not vset[i]) and mdist[i] < min_val:
                  __A    		=     i
                  __A    		=     mdist[i]
      return min_ind





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     [float('inf'		) for _ in range(UpperCAmelCase		)]
      __A    		=     [False for _ in range(UpperCAmelCase		)]
      __A    		=     0.0

      for _ in range(v - 1		):
            __A    		=     min_dist(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
            __A    		=     True

            for i in range(UpperCAmelCase		):
                  if (
                      (not vset[i])
                      and graph[u][i] != float('inf'		)
                      and mdist[u] + graph[u][i] < mdist[i]
                  ):
                        __A    		=     mdist[u] + graph[u][i]

      print_dist(UpperCAmelCase ,					UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				Tuple           =    int(input("Enter number of vertices: ").strip())
 a__						:				List[str]           =    int(input("Enter number of edges: ").strip())

 a__						:				int           =    [[float("inf") for i in range(V)] for j in range(V)]

 for i in range(V):
  a__						:				Optional[int]           =    0.0

 for i in range(E):
  print("\nEdge ", i + 1)
  a__						:				Union[str, Any]           =    int(input("Enter source:").strip())
  a__						:				Tuple           =    int(input("Enter destination:").strip())
  a__						:				int           =    float(input("Enter weight:").strip())
  a__						:				Dict           =    weight

 a__						:				int           =    int(input("\nEnter shortest path source:").strip())
 dijkstra(graph, V, gsrc)






