




import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      List[Any] ,   __snake_case       :      Optional[Any]=13 ,   __snake_case       :      int=7 ,   __snake_case       :      List[Any]=True ,   __snake_case       :      Optional[Any]=True ,   __snake_case       :      Optional[int]=False ,   __snake_case       :      int=True ,   __snake_case       :      Union[str, Any]=99 ,   __snake_case       :      Optional[int]=32 ,   __snake_case       :      Union[str, Any]=5 ,   __snake_case       :      List[Any]=4 ,   __snake_case       :      Union[str, Any]=64 ,   __snake_case       :      Dict="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      List[Any]=0.1 ,   __snake_case       :      int=5_12 ,   __snake_case       :      Optional[int]=16 ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      Any=0.02 ,   __snake_case       :      List[str]=3 ,   __snake_case       :      List[Any]=4 ,   __snake_case       :      str=None ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      Optional[int]=2 ,   __snake_case       :      Optional[Any]=2 ,   __snake_case       :      List[str]=2 ,   __snake_case       :      Dict=4 ,   __snake_case       :      Any=1 ,   ):
         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_										=					intermediate_size
         UpperCAmelCase_										=					hidden_act
         UpperCAmelCase_										=					hidden_dropout_prob
         UpperCAmelCase_										=					attention_probs_dropout_prob
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					type_vocab_size
         UpperCAmelCase_										=					type_sequence_label_size
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					num_labels
         UpperCAmelCase_										=					num_choices
         UpperCAmelCase_										=					scope
         UpperCAmelCase_										=					q_groups
         UpperCAmelCase_										=					k_groups
         UpperCAmelCase_										=					v_groups
         UpperCAmelCase_										=					post_attention_groups
         UpperCAmelCase_										=					intermediate_groups
         UpperCAmelCase_										=					output_groups
     def   lowerCamelCase_       (     self       :      List[str]    ):
         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_										=					None
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
         if self.use_labels:
             UpperCAmelCase_										=					ids_tensor([self.batch_size] ,   self.type_sequence_label_size    )
             UpperCAmelCase_										=					ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels    )
             UpperCAmelCase_										=					ids_tensor([self.batch_size] ,   self.num_choices    )

         UpperCAmelCase_										=					self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
     def   lowerCamelCase_       (     self       :      str    ):
         return SqueezeBertConfig(
             embedding_size=self.hidden_size ,   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 ,   attention_probs_dropout_prob=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   q_groups=self.q_groups ,   k_groups=self.k_groups ,   v_groups=self.v_groups ,   post_attention_groups=self.post_attention_groups ,   intermediate_groups=self.intermediate_groups ,   output_groups=self.output_groups ,   )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Tuple ,   __snake_case       :      Tuple ,   __snake_case       :      List[Any] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					SqueezeBertModel(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   __snake_case    )
         UpperCAmelCase_										=					model(__snake_case    )
         self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      List[str] ,   __snake_case       :      str ,   __snake_case       :      List[Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Optional[Any]    ):
         UpperCAmelCase_										=					SqueezeBertForMaskedLM(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   labels=__snake_case    )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      str ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      int ,   __snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					SqueezeBertForQuestionAnswering(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(
             __snake_case ,   attention_mask=__snake_case ,   start_positions=__snake_case ,   end_positions=__snake_case    )
         self.parent.assertEqual(result.start_logits.shape ,   (self.batch_size, self.seq_length)    )
         self.parent.assertEqual(result.end_logits.shape ,   (self.batch_size, self.seq_length)    )
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      int ,   __snake_case       :      List[Any] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Tuple ,   __snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					self.num_labels
         UpperCAmelCase_										=					SqueezeBertForSequenceClassification(__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   labels=__snake_case    )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)    )
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Optional[int] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Any ,   __snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					self.num_labels
         UpperCAmelCase_										=					SqueezeBertForTokenClassification(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()

         UpperCAmelCase_										=					model(__snake_case ,   attention_mask=__snake_case ,   labels=__snake_case    )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      Optional[int] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Dict ,   __snake_case       :      Tuple ,   __snake_case       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.num_choices
         UpperCAmelCase_										=					SqueezeBertForMultipleChoice(config=__snake_case    )
         model.to(__snake_case    )
         model.eval()
         UpperCAmelCase_										=					input_ids.unsqueeze(1    ).expand(-1 ,   self.num_choices ,   -1    ).contiguous()
         UpperCAmelCase_										=					input_mask.unsqueeze(1    ).expand(-1 ,   self.num_choices ,   -1    ).contiguous()
         UpperCAmelCase_										=					model(
             __snake_case ,   attention_mask=__snake_case ,   labels=__snake_case ,   )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)    )




     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					self.prepare_config_and_inputs()
         ((UpperCAmelCase_)			,							(UpperCAmelCase_)			,							(UpperCAmelCase_)			,							(UpperCAmelCase_)			,							(UpperCAmelCase_)			,							(UpperCAmelCase_))										=					config_and_inputs
         UpperCAmelCase_										=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict



@require_torch
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  (
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     lowerCAmelCase    :	Optional[int]       		=  (
         {
             'feature-extraction': SqueezeBertModel,
             'fill-mask': SqueezeBertForMaskedLM,
             'question-answering': SqueezeBertForQuestionAnswering,
             'text-classification': SqueezeBertForSequenceClassification,
             'token-classification': SqueezeBertForTokenClassification,
             'zero-shot': SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCAmelCase    :	str       		=  False
     lowerCAmelCase    :	Dict       		=  True
     lowerCAmelCase    :	List[Any]       		=  False
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					SqueezeBertModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case ,   dim=37    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         self.config_tester.run_common_tests()
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_model(*__snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_masked_lm(*__snake_case    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_question_answering(*__snake_case    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_token_classification(*__snake_case    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__snake_case    )




     @slow
     def   lowerCamelCase_       (     self       :      int    ):
         for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_										=					SqueezeBertModel.from_pretrained(__snake_case    )
             self.assertIsNotNone(__snake_case    )



@require_sentencepiece
@require_tokenizers
@require_torch
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     @slow
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli'''    )

         UpperCAmelCase_										=					torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]    )
         UpperCAmelCase_										=					model(__snake_case    )[0]
         UpperCAmelCase_										=					torch.Size((1, 3)    )
         self.assertEqual(output.shape ,   __snake_case    )
         UpperCAmelCase_										=					torch.tensor([[0.6_401, -0.0_349, -0.6_041]]    )
         self.assertTrue(torch.allclose(__snake_case ,   __snake_case ,   atol=1E-4    )    )











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					max(self.edges.values()    ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


if __name__ == "__main__":
 print(F"{solution() = }")





