'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     __snake_case			      =    '''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import MarianTokenizer
     from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					__lowerCAmelCase=None		,					)->  Union[str, Any]:






      '''simple docstring'''





      if attention_mask is None:
            UpperCAmelCase    : str      =np.where(input_ids != config.pad_token_id		,					1		,					0						)
      if decoder_attention_mask is None:
            UpperCAmelCase    : List[str]      =np.where(decoder_input_ids != config.pad_token_id		,					1		,					0						)
      if head_mask is None:
            UpperCAmelCase    : Optional[Any]      =np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            UpperCAmelCase    : Tuple      =np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            UpperCAmelCase    : Optional[Any]      =np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }







class 			__snake_case   :
 def __init__(  self		,   snake_case__		,   snake_case__=13		,   snake_case__=7		,   snake_case__=True		,   snake_case__=False		,   snake_case__=99		,   snake_case__=16		,   snake_case__=2		,   snake_case__=4		,   snake_case__=4		,   snake_case__="gelu"		,   snake_case__=0.1		,   snake_case__=0.1		,   snake_case__=32		,   snake_case__=2		,   snake_case__=1		,   snake_case__=0		,   snake_case__=0.02		,   )	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : int      =parent
       UpperCAmelCase    : int      =batch_size
       UpperCAmelCase    : Dict      =seq_length
       UpperCAmelCase    : List[Any]      =is_training
       UpperCAmelCase    : Dict      =use_labels
       UpperCAmelCase    : Union[str, Any]      =vocab_size
       UpperCAmelCase    : Union[str, Any]      =hidden_size
       UpperCAmelCase    : Union[str, Any]      =num_hidden_layers
       UpperCAmelCase    : Optional[int]      =num_attention_heads
       UpperCAmelCase    : Optional[int]      =intermediate_size
       UpperCAmelCase    : Tuple      =hidden_act
       UpperCAmelCase    : Union[str, Any]      =hidden_dropout_prob
       UpperCAmelCase    : List[str]      =attention_probs_dropout_prob
       UpperCAmelCase    : List[str]      =max_position_embeddings
       UpperCAmelCase    : Tuple      =eos_token_id
       UpperCAmelCase    : Optional[Any]      =pad_token_id
       UpperCAmelCase    : Dict      =bos_token_id
       UpperCAmelCase    : int      =initializer_range
 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,   self.vocab_size						)		,   3		,   self.vocab_size						)
       UpperCAmelCase    : List[str]      =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,   dtype=np.intaa						))		,   -1						)

       UpperCAmelCase    : str      =shift_tokens_right(snake_case__		,   1		,   2						)

       UpperCAmelCase    : Any      =MarianConfig(
           vocab_size=self.vocab_size		,   d_model=self.hidden_size		,   encoder_layers=self.num_hidden_layers		,   decoder_layers=self.num_hidden_layers		,   encoder_attention_heads=self.num_attention_heads		,   decoder_attention_heads=self.num_attention_heads		,   encoder_ffn_dim=self.intermediate_size		,   decoder_ffn_dim=self.intermediate_size		,   dropout=self.hidden_dropout_prob		,   attention_dropout=self.attention_probs_dropout_prob		,   max_position_embeddings=self.max_position_embeddings		,   eos_token_id=self.eos_token_id		,   bos_token_id=self.bos_token_id		,   pad_token_id=self.pad_token_id		,   initializer_range=self.initializer_range		,   use_cache=snake_case__		,   )
       UpperCAmelCase    : Union[str, Any]      =prepare_marian_inputs_dict(snake_case__		,   snake_case__		,   snake_case__						)
       return config, inputs_dict
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : str      =self.prepare_config_and_inputs()
       return config, inputs_dict
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : str      =20
       UpperCAmelCase    : Optional[Any]      =model_class_name(snake_case__						)

       UpperCAmelCase    : Tuple      =model.encode(inputs_dict['''input_ids''']						)

       UpperCAmelCase    : Any      =(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCAmelCase    : int      =model.init_cache(decoder_input_ids.shape[0]		,   snake_case__		,   snake_case__						)
       UpperCAmelCase    : Any      =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,   dtype='''i4'''						)

       UpperCAmelCase    : Optional[Any]      =jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )
       UpperCAmelCase    : Tuple      =model.decode(
           decoder_input_ids[:, :-1]		,   snake_case__		,   decoder_attention_mask=snake_case__		,   past_key_values=snake_case__		,   decoder_position_ids=snake_case__		,   )

       UpperCAmelCase    : str      =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='''i4'''						)
       UpperCAmelCase    : List[str]      =model.decode(
           decoder_input_ids[:, -1:]		,   snake_case__		,   decoder_attention_mask=snake_case__		,   past_key_values=outputs_cache.past_key_values		,   decoder_position_ids=snake_case__		,   )

       UpperCAmelCase    : List[str]      =model.decode(snake_case__		,   snake_case__						)

       UpperCAmelCase    : Tuple      =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 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : Any      =20
       UpperCAmelCase    : List[Any]      =model_class_name(snake_case__						)

       UpperCAmelCase    : Optional[Any]      =model.encode(inputs_dict['''input_ids''']						)

       UpperCAmelCase    : int      =(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCAmelCase    : int      =jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
           ]		,   axis=-1		,   )

       UpperCAmelCase    : Dict      =model.init_cache(decoder_input_ids.shape[0]		,   snake_case__		,   snake_case__						)
       UpperCAmelCase    : Union[str, Any]      =jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]		,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,   )

       UpperCAmelCase    : Union[str, Any]      =model.decode(
           decoder_input_ids[:, :-1]		,   snake_case__		,   decoder_attention_mask=snake_case__		,   past_key_values=snake_case__		,   decoder_position_ids=snake_case__		,   )
       UpperCAmelCase    : Union[str, Any]      =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,   dtype='''i4'''						)
       UpperCAmelCase    : Optional[Any]      =model.decode(
           decoder_input_ids[:, -1:]		,   snake_case__		,   past_key_values=outputs_cache.past_key_values		,   decoder_attention_mask=snake_case__		,   decoder_position_ids=snake_case__		,   )

       UpperCAmelCase    : List[Any]      =model.decode(snake_case__		,   snake_case__		,   decoder_attention_mask=snake_case__						)

       UpperCAmelCase    : Optional[Any]      =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 			__snake_case   (     lowerCamelCase__					,   unittest.TestCase					,   lowerCamelCase__   ):
 __lowerCamelCase    :   Optional[int]     =      True
 __lowerCamelCase    :   List[Any]     =      (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 __lowerCamelCase    :   Union[str, Any]     =      (FlaxMarianMTModel,) if is_flax_available() else ()
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =FlaxMarianModelTester(self						)
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : int      =self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(snake_case__		,   snake_case__		,   snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__		,   snake_case__		,   snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__						):
                   UpperCAmelCase    : List[str]      =self._prepare_for_class(snake_case__		,   snake_case__						)
                   UpperCAmelCase    : List[Any]      =model_class(snake_case__						)

                   @jax.jit
                   def encode_jitted(snake_case__		,   snake_case__=None		,   **snake_case__						):
                         return model.encode(input_ids=snake_case__		,   attention_mask=snake_case__						)

                   with self.subTest('''JIT Enabled'''						):
                         UpperCAmelCase    : int      =encode_jitted(**snake_case__						).to_tuple()

                   with self.subTest('''JIT Disabled'''						):
                         with jax.disable_jit():
                               UpperCAmelCase    : Optional[Any]      =encode_jitted(**snake_case__						).to_tuple()

                   self.assertEqual(len(snake_case__						)		,   len(snake_case__						)						)
                   for jitted_output, output in zip(snake_case__		,   snake_case__						):
                         self.assertEqual(jitted_output.shape		,   output.shape						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__						):
                   UpperCAmelCase    : str      =model_class(snake_case__						)
                   UpperCAmelCase    : Any      =model.encode(inputs_dict['''input_ids''']		,   inputs_dict['''attention_mask''']						)

                   UpperCAmelCase    : List[Any]      ={
                       '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                       '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                       '''encoder_outputs''': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(snake_case__		,   snake_case__		,   snake_case__						):
                         return model.decode(
                             decoder_input_ids=snake_case__		,   decoder_attention_mask=snake_case__		,   encoder_outputs=snake_case__		,   )

                   with self.subTest('''JIT Enabled'''						):
                         UpperCAmelCase    : str      =decode_jitted(**snake_case__						).to_tuple()

                   with self.subTest('''JIT Disabled'''						):
                         with jax.disable_jit():
                               UpperCAmelCase    : Any      =decode_jitted(**snake_case__						).to_tuple()

                   self.assertEqual(len(snake_case__						)		,   len(snake_case__						)						)
                   for jitted_output, output in zip(snake_case__		,   snake_case__						):
                         self.assertEqual(jitted_output.shape		,   output.shape						)






 @slow
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             UpperCAmelCase    : str      =model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''						)
             # FlaxMarianForSequenceClassification expects eos token in input_ids
             UpperCAmelCase    : List[Any]      =np.ones((1, 1)						) * model.config.eos_token_id
             UpperCAmelCase    : Any      =model(snake_case__						)
             self.assertIsNotNone(snake_case__						)







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     unittest.TestCase   ):
 __lowerCamelCase    :   int     =      None
 __lowerCamelCase    :   Tuple     =      None
 @classmethod
 def 	UpperCAmelCase__       (  cls						)	->							None:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
       return cls
 @cached_property
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       return MarianTokenizer.from_pretrained(self.model_name						)
 @property
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       return self.tokenizer.eos_token_id
 @cached_property
 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       UpperCAmelCase    : FlaxMarianMTModel      =FlaxMarianMTModel.from_pretrained(self.model_name						)
       self.assertEqual(model.config.decoder_start_token_id		,   model.config.pad_token_id						)
       return model
 def 	UpperCAmelCase__       (  self		,   **snake_case__						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =self.translate_src_text(**snake_case__						)
       self.assertListEqual(self.expected_text		,   snake_case__						)






 def 	UpperCAmelCase__       (  self		,   **snake_case__						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =self.tokenizer(self.src_text		,   padding=snake_case__		,   return_tensors='''np'''		,   **snake_case__						)
       UpperCAmelCase    : List[Any]      =self.model.generate(
           model_inputs.input_ids		,   attention_mask=model_inputs.attention_mask		,   num_beams=2		,   max_length=128		,   ).sequences
       UpperCAmelCase    : Optional[Any]      =self.tokenizer.batch_decode(snake_case__		,   skip_special_tokens=snake_case__						)
       return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   List[Any]     =      """en"""
 __lowerCamelCase    :   int     =      """fr"""
 __lowerCamelCase    :   str     =      [
     """I am a small frog.""",
     """Now I can forget the 100 words of german that I know.""",
 ]
 __lowerCamelCase    :   Dict     =      [
     """Je suis une petite grenouille.""",
     """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
 ]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   Dict     =      """fr"""
 __lowerCamelCase    :   Optional[Any]     =      """en"""
 __lowerCamelCase    :   Optional[int]     =      [
     """Donnez moi le micro.""",
     """Tom et Mary étaient assis à une table.""",  # Accents
 ]
 __lowerCamelCase    :   List[Any]     =      [
     """Give me the microphone.""",
     """Tom and Mary were sitting at a table.""",
 ]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							List[str]:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   int     =      """mt"""
 __lowerCamelCase    :   List[Any]     =      """en"""
 __lowerCamelCase    :   Optional[Any]     =      ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
 __lowerCamelCase    :   str     =      ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   List[Any]     =      """en"""
 __lowerCamelCase    :   str     =      """de"""
 __lowerCamelCase    :   Tuple     =      [
     """I am a small frog.""",
     """Now I can forget the 100 words of german that I know.""",
     """Tom asked his teacher for advice.""",
     """That's how I would do it.""",
     """Tom really admired Mary's courage.""",
     """Turn around and close your eyes.""",
 ]
 __lowerCamelCase    :   Any     =      [
     """Ich bin ein kleiner Frosch.""",
     """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
     """Tom bat seinen Lehrer um Rat.""",
     """So würde ich das machen.""",
     """Tom bewunderte Marias Mut wirklich.""",
     """Drehen Sie sich um und schließen Sie die Augen.""",
 ]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   str     =      """en"""
 __lowerCamelCase    :   int     =      """zh"""
 __lowerCamelCase    :   Any     =      ["""My name is Wolfgang and I live in Berlin"""]
 __lowerCamelCase    :   Any     =      ["""我叫沃尔夫冈 我住在柏林"""]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   List[Any]     =      """ru"""
 __lowerCamelCase    :   Any     =      """fr"""
 __lowerCamelCase    :   List[str]     =      ["""Он показал мне рукопись своей новой пьесы."""]
 __lowerCamelCase    :   Tuple     =      ["""Il m'a montré le manuscrit de sa nouvelle pièce."""]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   List[Any]     =      """en"""
 __lowerCamelCase    :   Tuple     =      """ROMANCE"""
 __lowerCamelCase    :   int     =      [
     """>>fr<< Don't spend so much time watching TV.""",
     """>>pt<< Your message has been sent.""",
     """>>es<< He's two years older than me.""",
 ]
 __lowerCamelCase    :   Tuple     =      [
     """Ne passez pas autant de temps à regarder la télé.""",
     """A sua mensagem foi enviada.""",
     """Es dos años más viejo que yo.""",
 ]
 @slow
 def 	UpperCAmelCase__       (  self						)	->							Any:
       '''simple docstring'''





       self._assert_generated_batch_equal_expected()

def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  list[tuple[int, int, int]]:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      =sorted(__lowerCAmelCase		,					key=lambda __lowerCAmelCase						: edge[2]						)

      UpperCAmelCase    : Union[str, Any]      =list(range(__lowerCAmelCase						)						)

      def find_parent(__lowerCAmelCase						):
            if i != parent[i]:
                  UpperCAmelCase    : Optional[int]      =find_parent(parent[i]						)
            return parent[i]

      UpperCAmelCase    : Optional[Any]      =0
      UpperCAmelCase    : List[str]      =[]

      for edge in edges:
            UpperCAmelCase    : Dict      =find_parent(edge[0]						)
            UpperCAmelCase    : Optional[Any]      =find_parent(edge[1]						)
            if parent_a != parent_b:
                  minimum_spanning_tree_cost += edge[2]
                  minimum_spanning_tree.append(__lowerCAmelCase						)
                  UpperCAmelCase    : Optional[Any]      =parent_b

      return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
     __snake_case      ,  __snake_case			      =    list(map(int, input().strip().split()))
     __snake_case			      =    []

     for _ in range(num_edges):
          __snake_case      ,  __snake_case      ,  __snake_case			      =    (int(x) for x in input().strip().split())
          edges.append((nodea, nodea, cost))

     kruskal(num_nodes, edges)
