'''simple docstring'''

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormModel,
       )
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







  """simple docstring"""






  def __init__(   self		,						__A		,						__A=13		,						__A=7		,						__A=True		,						__A=True		,						__A=True		,						__A=True		,						__A=99		,						__A=32		,						__A=5		,						__A=4		,						__A=37		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=16		,						__A=2		,						__A=0.02		,						__A=4		,						) ->     Union[str, Any]:
        _lowerCAmelCase										=parent
        _lowerCAmelCase										=batch_size
        _lowerCAmelCase										=seq_length
        _lowerCAmelCase										=is_training
        _lowerCAmelCase										=use_attention_mask
        _lowerCAmelCase										=use_token_type_ids
        _lowerCAmelCase										=use_labels
        _lowerCAmelCase										=vocab_size
        _lowerCAmelCase										=hidden_size
        _lowerCAmelCase										=num_hidden_layers
        _lowerCAmelCase										=num_attention_heads
        _lowerCAmelCase										=intermediate_size
        _lowerCAmelCase										=hidden_act
        _lowerCAmelCase										=hidden_dropout_prob
        _lowerCAmelCase										=attention_probs_dropout_prob
        _lowerCAmelCase										=max_position_embeddings
        _lowerCAmelCase										=type_vocab_size
        _lowerCAmelCase										=type_sequence_label_size
        _lowerCAmelCase										=initializer_range
        _lowerCAmelCase										=num_choices






  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size				)

        _lowerCAmelCase										=None
        if self.use_attention_mask:
              _lowerCAmelCase										=random_attention_mask([self.batch_size, self.seq_length]				)

        _lowerCAmelCase										=None
        if self.use_token_type_ids:
              _lowerCAmelCase										=ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size				)

        _lowerCAmelCase										=RobertaPreLayerNormConfig(
            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=_lowercase		,						initializer_range=self.initializer_range		,						)

        return config, input_ids, token_type_ids, attention_mask






  def 		UpperCamelCase__       (   self				) ->     Any:
        _lowerCAmelCase										=self.prepare_config_and_inputs()
        _lowerCAmelCase										=config_and_inputs
        _lowerCAmelCase										={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict






  def 		UpperCamelCase__       (   self				) ->     str:
        _lowerCAmelCase										=self.prepare_config_and_inputs()
        _lowerCAmelCase										=config_and_inputs

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

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

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class   SCREAMING_SNAKE_CASE			(						SCREAMING_SNAKE_CASE__    ,			unittest.TestCase):







  """simple docstring"""
  lowercase   :      int       =							True

  lowercase   :      int       =							(
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 		UpperCamelCase__       (   self				) ->     int:
        _lowerCAmelCase										=FlaxRobertaPreLayerNormModelTester(self				)






  @slow
  def 		UpperCamelCase__       (   self				) ->     Tuple:

        for model_class_name in self.all_model_classes:
              _lowerCAmelCase										=model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'		,						from_pt=_lowercase				)
              _lowerCAmelCase										=model(np.ones((1, 1)				)				)
              self.assertIsNotNone(_lowercase				)

@require_flax
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







  """simple docstring"""






  @slow
  def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
        _lowerCAmelCase										=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'		,						from_pt=_lowercase				)

        _lowerCAmelCase										=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		,						dtype=jnp.intaa				)
        _lowerCAmelCase										=model(_lowercase				)[0]
        _lowerCAmelCase										=[1, 11, 5_0265]
        self.assertEqual(list(output.shape				)		,						_lowercase				)
        # compare the actual values for a slice.
        _lowerCAmelCase										=np.array(
            [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]		,						dtype=np.floataa				)
        self.assertTrue(np.allclose(output[:, :3, :3]		,						_lowercase		,						atol=1E-4				)				)






  @slow
  def 		UpperCamelCase__       (   self				) ->     int:
        _lowerCAmelCase										=FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'		,						from_pt=_lowercase				)

        _lowerCAmelCase										=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		,						dtype=jnp.intaa				)
        _lowerCAmelCase										=model(_lowercase				)[0]
        # compare the actual values for a slice.
        _lowerCAmelCase										=np.array(
            [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]		,						dtype=np.floataa				)
        self.assertTrue(np.allclose(output[:, :3, :3]		,						_lowercase		,						atol=1E-4				)				)






'''simple docstring'''

from __future__ import annotations

from typing import Any
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A				) ->     None:
								_lowerCAmelCase										=num_of_nodes
								_lowerCAmelCase										=[]
								_lowerCAmelCase										={}






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:
								self.m_edges.append([u_node, v_node, weight]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:

								if self.m_component[u_node] == u_node:
														return u_node
								return self.find_component(self.m_component[u_node]				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:

								if self.m_component[u_node] != u_node:
														for k in self.m_component:
																				_lowerCAmelCase										=self.find_component(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     None:

								if component_size[u_node] <= component_size[v_node]:
														_lowerCAmelCase										=v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(__A				)

								elif component_size[u_node] >= component_size[v_node]:
														_lowerCAmelCase										=self.find_component(__A				)
														component_size[u_node] += component_size[v_node]
														self.set_component(__A				)






		def 		UpperCamelCase__       (   self				) ->     None:
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=0

								_lowerCAmelCase										=[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes				):
														self.m_component.update({node: node}				)
														component_size.append(1				)

								_lowerCAmelCase										=self.m_num_of_nodes

								while num_of_components > 1:
														for edge in self.m_edges:
																				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																				_lowerCAmelCase										=self.m_component[u]
																				_lowerCAmelCase										=self.m_component[v]

																				if u_component != v_component:

																										for component in (u_component, v_component):
																																if (
																																    minimum_weight_edge[component] == -1
																																    or minimum_weight_edge[component][2] > w
																																):
																																						_lowerCAmelCase										=[u, v, w]

														for edge in minimum_weight_edge:
																				if isinstance(__A		,						__A				):
																										_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=edge

																										_lowerCAmelCase										=self.m_component[u]
																										_lowerCAmelCase										=self.m_component[v]

																										if u_component != v_component:
																																mst_weight += w
																																self.union(__A		,						__A		,						__A				)
																																print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''				)
																																num_of_components -= 1

														_lowerCAmelCase										=[-1] * self.m_num_of_nodes
								print(F'''The total weight of the minimal spanning tree is: {mst_weight}'''				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''








if __name__ == "__main__":
							import doctest

							doctest.testmod()





