
"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
   SCREAMING_SNAKE_CASE_         =  '''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import AutoTokenizer
   from transformers.models.mbart.modeling_flax_mbart import (
       FlaxMBartForConditionalGeneration,
       FlaxMBartForQuestionAnswering,
       FlaxMBartForSequenceClassification,
       FlaxMBartModel,
       shift_tokens_right,
   )




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[Any]=None	,			lowerCAmelCase			:   Dict=None	,			lowerCAmelCase			:   Optional[int]=None	,			lowerCAmelCase			:   Dict=None	,			lowerCAmelCase			:   Optional[Any]=None	,			lowerCAmelCase			:   Dict=None	,			)	-> List[Any]:






    """simple docstring"""




    if attention_mask is None:
        UpperCAmelCase       =   np.where(input_ids != config.pad_token_id	,			1	,			0	)
    if decoder_attention_mask is None:
        UpperCAmelCase       =   np.where(decoder_input_ids != config.pad_token_id	,			1	,			0	)
    if head_mask is None:
        UpperCAmelCase       =   np.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        UpperCAmelCase       =   np.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        UpperCAmelCase       =   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": decoder_attention_mask,
    }
class        _UpperCAmelCase							(						unittest.TestCase							):




      def __init__(							self				,    lowercase_				,    lowercase_=1_3				,    lowercase_=7				,    lowercase_=True				,    lowercase_=False				,    lowercase_=9_9				,    lowercase_=1_6				,    lowercase_=2				,    lowercase_=4				,    lowercase_=4				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=3_2				,    lowercase_=2				,    lowercase_=1				,    lowercase_=0				,    lowercase_=2				,    lowercase_=0.0_2				,    )  ->   Any:
          UpperCAmelCase       =   parent
          UpperCAmelCase       =   batch_size
          UpperCAmelCase       =   seq_length
          UpperCAmelCase       =   is_training
          UpperCAmelCase       =   use_labels
          UpperCAmelCase       =   vocab_size
          UpperCAmelCase       =   hidden_size
          UpperCAmelCase       =   num_hidden_layers
          UpperCAmelCase       =   num_attention_heads
          UpperCAmelCase       =   intermediate_size
          UpperCAmelCase       =   hidden_act
          UpperCAmelCase       =   hidden_dropout_prob
          UpperCAmelCase       =   attention_probs_dropout_prob
          UpperCAmelCase       =   max_position_embeddings
          UpperCAmelCase       =   eos_token_id
          UpperCAmelCase       =   pad_token_id
          UpperCAmelCase       =   bos_token_id
          UpperCAmelCase       =   decoder_start_token_id
          UpperCAmelCase       =   initializer_range




      def       a_ (							self							)  ->   Optional[Any]:
          UpperCAmelCase       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,    self.vocab_size							)				,    3				,    self.vocab_size							)
          UpperCAmelCase       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,    dtype=np.intaa							))				,    -1							)

          UpperCAmelCase       =   shift_tokens_right(lowercase_				,    1							)

          UpperCAmelCase       =   MBartConfig(
              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				,    decoder_start_token_id=self.decoder_start_token_id				,    initializer_range=self.initializer_range				,    use_cache=lowercase_				,    )
          UpperCAmelCase       =   prepare_mbart_inputs_dict(lowercase_				,    lowercase_				,    lowercase_							)
          return config, inputs_dict




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.prepare_config_and_inputs()
          return config, inputs_dict




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   2_0
          UpperCAmelCase       =   model_class_name(lowercase_							)

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

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

          UpperCAmelCase       =   model.init_cache(decoder_input_ids.shape[0]				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,    dtype='i4'							)

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

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

          UpperCAmelCase       =   model.decode(lowercase_				,    lowercase_							)

          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       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Optional[Any]:
          UpperCAmelCase       =   2_0
          UpperCAmelCase       =   model_class_name(lowercase_							)

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

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

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

          UpperCAmelCase       =   model.init_cache(decoder_input_ids.shape[0]				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]				,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,    )

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

          UpperCAmelCase       =   model.decode(lowercase_				,    lowercase_				,    decoder_attention_mask=lowercase_							)

          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        _UpperCAmelCase							(						unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		9_9




      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   np.array(
              [
                  [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                  [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                  [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                  [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                  [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                  [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                  [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                  [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                  [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                  [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                  [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                  [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                  [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
              ]				,    dtype=np.intaa				,    )

          UpperCAmelCase       =   input_ids.shape[0]
          UpperCAmelCase       =   MBartConfig(
              vocab_size=self.vocab_size				,    d_model=2_4				,    encoder_layers=2				,    decoder_layers=2				,    encoder_attention_heads=2				,    decoder_attention_heads=2				,    encoder_ffn_dim=3_2				,    decoder_ffn_dim=3_2				,    max_position_embeddings=4_8				,    eos_token_id=2				,    pad_token_id=1				,    bos_token_id=0				,    )
          return config, input_ids, batch_size




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   self._get_config_and_data()
          UpperCAmelCase       =   FlaxMBartForSequenceClassification(lowercase_							)
          UpperCAmelCase       =   model(input_ids=lowercase_				,    decoder_input_ids=lowercase_							)
          UpperCAmelCase       =   (batch_size, config.num_labels)
          self.assertEqual(outputs['logits'].shape				,    lowercase_							)




      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   self._get_config_and_data()
          UpperCAmelCase       =   FlaxMBartForQuestionAnswering(lowercase_							)
          UpperCAmelCase       =   model(input_ids=lowercase_							)

          self.assertEqual(outputs['start_logits'].shape				,    input_ids.shape							)
          self.assertEqual(outputs['end_logits'].shape				,    input_ids.shape							)




      def       a_ (							self							)  ->   int:
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   self._get_config_and_data()
          UpperCAmelCase       =   FlaxMBartForConditionalGeneration(lowercase_							)
          UpperCAmelCase       =   lm_model(input_ids=lowercase_							)
          UpperCAmelCase       =   (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape				,    lowercase_							)




      def       a_ (							self							)  ->   Dict:
          UpperCAmelCase       =   MBartConfig(
              vocab_size=self.vocab_size				,    d_model=1_4				,    encoder_layers=2				,    decoder_layers=2				,    encoder_attention_heads=2				,    decoder_attention_heads=2				,    encoder_ffn_dim=8				,    decoder_ffn_dim=8				,    max_position_embeddings=4_8				,    )
          UpperCAmelCase       =   FlaxMBartForConditionalGeneration(lowercase_							)
          UpperCAmelCase       =   np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]				,    dtype=np.intaa							)
          UpperCAmelCase       =   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,    dtype=np.intaa							)
          UpperCAmelCase       =   lm_model(input_ids=lowercase_				,    decoder_input_ids=lowercase_							)
          UpperCAmelCase       =   (*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape				,    lowercase_							)




      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]				,    dtype=np.intaa							)
          UpperCAmelCase       =   shift_tokens_right(lowercase_				,    1							)
          UpperCAmelCase       =   np.equal(lowercase_				,    1							).astype(np.floataa							).sum()
          UpperCAmelCase       =   np.equal(lowercase_				,    1							).astype(np.floataa							).sum()
          self.assertEqual(shifted.shape				,    input_ids.shape							)
          self.assertEqual(lowercase_				,    n_pad_before - 1							)
          self.assertTrue(np.equal(shifted[:, 0]				,    2							).all()							)

@require_flax
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     unittest.TestCase       ,     SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Any							 =		True
      __SCREAMING_SNAKE_CASE				:							Optional[int]							 =		(
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




      def       a_ (							self							)  ->   Dict:
          UpperCAmelCase       =   FlaxMBartModelTester(self							)




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self							)  ->   Any:
          UpperCAmelCase     ,							UpperCAmelCase       =   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(lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self							)  ->   Tuple:
          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__							):
                  UpperCAmelCase       =   self._prepare_for_class(lowercase_				,    lowercase_							)
                  UpperCAmelCase       =   model_class(lowercase_							)

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

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

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase       =   encode_jitted(**lowercase_							).to_tuple()

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




      def       a_ (							self							)  ->   List[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__							):
                  UpperCAmelCase       =   model_class(lowercase_							)
                  UpperCAmelCase       =   model.encode(inputs_dict['input_ids']				,    inputs_dict['attention_mask']							)

                  UpperCAmelCase       =   {
                      '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(lowercase_				,    lowercase_				,    lowercase_							):
                      return model.decode(
                          decoder_input_ids=lowercase_				,    decoder_attention_mask=lowercase_				,    encoder_outputs=lowercase_				,    )

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

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

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




      @slow
      def       a_ (							self							)  ->   Tuple:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase       =   model_class_name.from_pretrained('facebook/mbart-large-cc25'				,    from_pt=lowercase_							)
              # FlaxMBartForSequenceClassification expects eos token in input_ids
              UpperCAmelCase       =   np.ones((1, 1)							) * model.config.eos_token_id
              UpperCAmelCase       =   model(lowercase_							)
              self.assertIsNotNone(lowercase_							)

@require_flax
@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase							(						unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							Union[str, Any]							 =		[
          " UN Chief Says There Is No Military Solution in Syria",
      ]
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
      ]
      __SCREAMING_SNAKE_CASE				:							str							 =		"facebook/mbart-large-en-ro"




      @cached_property
      def       a_ (							self							)  ->   Optional[int]:
          return AutoTokenizer.from_pretrained(self.model_name							)




      @cached_property
      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase       =   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				,    from_pt=lowercase_							)
          return model




      def       a_ (							self				,    **lowercase_							)  ->   str:
          UpperCAmelCase       =   self.translate_src_text(**lowercase_							)
          self.assertListEqual(self.expected_text				,    lowercase_							)




      def       a_ (							self				,    **lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   self.tokenizer(self.src_text				,    **lowercase_				,    return_tensors='np'							)
          UpperCAmelCase       =   self.model.generate(
              model_inputs.input_ids				,    attention_mask=model_inputs.attention_mask				,    decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']				,    early_stopping=lowercase_				,    num_beams=2				,    ).sequences
          UpperCAmelCase       =   self.tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_							)
          return generated_words




      @slow
      def       a_ (							self							)  ->   Tuple:
          self._assert_generated_batch_equal_expected()




"""simple docstring"""

import math
import sys
class        _UpperCAmelCase							:




      def __init__(							self							)  ->   List[str]:
          UpperCAmelCase       =   0
          UpperCAmelCase       =   []
          UpperCAmelCase       =   {}  # To store the pos of node in array




      def       a_ (							self							)  ->   Dict:
          return self.cur_size == 0




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          UpperCAmelCase       =   self.left(lowercase_							)
          UpperCAmelCase       =   self.right(lowercase_							)
          if lc < self.cur_size and self.array(lowercase_							)[0] < self.array(lowercase_							)[0]:
              UpperCAmelCase       =   lc
          else:
              UpperCAmelCase       =   idx
          if rc < self.cur_size and self.array(lowercase_							)[0] < self.array(lowercase_							)[0]:
              UpperCAmelCase       =   rc
          if smallest != idx:
              self.swap(lowercase_				,    lowercase_							)
              self.min_heapify(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          # Inserts a node into the Priority Queue
          UpperCAmelCase       =   self.cur_size
          self.cur_size += 1
          self.array.append((sys.maxsize, tup[1])							)
          self.decrease_key((sys.maxsize, tup[1])				,    tup[0]							)




      def       a_ (							self							)  ->   int:
          # Removes and returns the min element at top of priority queue
          UpperCAmelCase       =   self.array[0][1]
          UpperCAmelCase       =   self.array[self.cur_size - 1]
          self.cur_size -= 1
          self.min_heapify(1							)
          del self.pos[min_node]
          return min_node




      def       a_ (							self				,    lowercase_							)  ->   Optional[int]:
          # returns the index of left child
          return 2 * i + 1




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          # returns the index of right child
          return 2 * i + 2




      def       a_ (							self				,    lowercase_							)  ->   List[Any]:
          # returns the index of parent
          return math.floor(i / 2							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          # swaps array elements at indices i and j
          # update the pos{}
          UpperCAmelCase       =   j
          UpperCAmelCase       =   i
          UpperCAmelCase       =   self.array[i]
          UpperCAmelCase       =   self.array[j]
          UpperCAmelCase       =   temp




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   self.pos[tup[1]]
          # assuming the new_d is atmost old_d
          UpperCAmelCase       =   (new_d, tup[1])
          while idx > 0 and self.array[self.par(lowercase_							)][0] > self.array[idx][0]:
              self.swap(lowercase_				,    self.par(lowercase_							)							)
              UpperCAmelCase       =   self.par(lowercase_							)

class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   {}  # To store graph: u -> (v,w)
          UpperCAmelCase       =   num  # Number of nodes in graph
          # To store the distance from source vertex
          UpperCAmelCase       =   [0] * self.num_nodes
          UpperCAmelCase       =   [-1] * self.num_nodes  # To store the path




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Any:
          #  Edge going from node u to v and v to u with weight w
          # u (w)-> v, v (w) -> u
          # Check if u already in graph
          if u in self.adjList:
              self.adjList[u].append((v, w)							)
          else:
              UpperCAmelCase       =   [(v, w)]

          # Assuming undirected graph
          if v in self.adjList:
              self.adjList[v].append((u, w)							)
          else:
              UpperCAmelCase       =   [(u, w)]




      def       a_ (							self							)  ->   Optional[Any]:
          # u -> v(w)
          for u in self.adjList:
              print(lowercase_				,    '->'				,    ' -> '.join(str(F"{v}({w})"							) for v, w in self.adjList[u]							)							)




      def       a_ (							self				,    lowercase_							)  ->   int:
          # Flush old junk values in par[]
          UpperCAmelCase       =   [-1] * self.num_nodes
          # src is the source node
          UpperCAmelCase       =   0
          UpperCAmelCase       =   PriorityQueue()
          q.insert((0, src)							)  # (dist from src, node)
          for u in self.adjList:
              if u != src:
                  UpperCAmelCase       =   sys.maxsize  # Infinity
                  UpperCAmelCase       =   -1

          while not q.is_empty():
              UpperCAmelCase       =   q.extract_min()  # Returns node with the min dist from source
              # Update the distance of all the neighbours of u and
              # if their prev dist was INFINITY then push them in Q
              for v, w in self.adjList[u]:
                  UpperCAmelCase       =   self.dist[u] + w
                  if self.dist[v] > new_dist:
                      if self.dist[v] == sys.maxsize:
                          q.insert((new_dist, v)							)
                      else:
                          q.decrease_key((self.dist[v], v)				,    lowercase_							)
                      UpperCAmelCase       =   new_dist
                      UpperCAmelCase       =   u

        # Show the shortest distances from src
          self.show_distances(lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   Any:
          print(F"Distance from node: {src}"							)
          for u in range(self.num_nodes							):
              print(F"Node {u} has distance: {self.dist[u]}"							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          # To show the shortest path from src to dest
          # WARNING: Use it *after* calling dijkstra
          UpperCAmelCase       =   []
          UpperCAmelCase       =   0
          UpperCAmelCase       =   dest
          # Backtracking from dest to src
          while self.par[temp] != -1:
              path.append(lowercase_							)
              if temp != src:
                  for v, w in self.adjList[temp]:
                      if v == self.par[temp]:
                          cost += w
                          break
              UpperCAmelCase       =   self.par[temp]
          path.append(lowercase_							)
          path.reverse()

          print(F"----Path to reach {dest} from {src}----"							)
          for u in path:
              print(F"{u}"				,    end=' '							)
              if u != dest:
                  print('-> '				,    end=''							)

          print('\nTotal cost of path: '				,    lowercase_							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  Graph(9)
   graph.add_edge(0, 1, 4)
   graph.add_edge(0, 7, 8)
   graph.add_edge(1, 2, 8)
   graph.add_edge(1, 7, 11)
   graph.add_edge(2, 3, 7)
   graph.add_edge(2, 8, 2)
   graph.add_edge(2, 5, 4)
   graph.add_edge(3, 4, 9)
   graph.add_edge(3, 5, 14)
   graph.add_edge(4, 5, 10)
   graph.add_edge(5, 6, 2)
   graph.add_edge(6, 7, 1)
   graph.add_edge(6, 8, 6)
   graph.add_edge(7, 8, 7)
   graph.show_graph()
   graph.dijkstra(0)
   graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21



