import unittest

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax

    from transformers.models.big_bird.modeling_flax_big_bird import (
        FlaxBigBirdForCausalLM,
        FlaxBigBirdForMaskedLM,
        FlaxBigBirdForMultipleChoice,
        FlaxBigBirdForPreTraining,
        FlaxBigBirdForQuestionAnswering,
        FlaxBigBirdForSequenceClassification,
        FlaxBigBirdForTokenClassification,
        FlaxBigBirdModel,
    )


class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=2			,			__lowercase=56			,			__lowercase=True			,			__lowercase=True			,			__lowercase=True			,			__lowercase=True			,			__lowercase=99			,			__lowercase=32			,			__lowercase=2			,			__lowercase=2			,			__lowercase=7			,			__lowercase="gelu_new"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=512			,			__lowercase=16			,			__lowercase=2			,			__lowercase=0.02			,			__lowercase=4			,			__lowercase="block_sparse"			,			__lowercase=True			,			__lowercase=False			,			__lowercase=2			,			__lowercase=3			,			)	->     List[str]:
        __UpperCamelCase					:str        =		parent
        __UpperCamelCase					:List[str]        =		batch_size
        __UpperCamelCase					:int        =		seq_length
        __UpperCamelCase					:Tuple        =		is_training
        __UpperCamelCase					:int        =		use_attention_mask
        __UpperCamelCase					:Dict        =		use_token_type_ids
        __UpperCamelCase					:Any        =		use_labels
        __UpperCamelCase					:int        =		vocab_size
        __UpperCamelCase					:List[Any]        =		hidden_size
        __UpperCamelCase					:Dict        =		num_hidden_layers
        __UpperCamelCase					:Dict        =		num_attention_heads
        __UpperCamelCase					:str        =		intermediate_size
        __UpperCamelCase					:List[Any]        =		hidden_act
        __UpperCamelCase					:Tuple        =		hidden_dropout_prob
        __UpperCamelCase					:Optional[Any]        =		attention_probs_dropout_prob
        __UpperCamelCase					:int        =		max_position_embeddings
        __UpperCamelCase					:Dict        =		type_vocab_size
        __UpperCamelCase					:Tuple        =		type_sequence_label_size
        __UpperCamelCase					:Any        =		initializer_range
        __UpperCamelCase					:int        =		num_choices

        __UpperCamelCase					:Dict        =		rescale_embeddings
        __UpperCamelCase					:Dict        =		attention_type
        __UpperCamelCase					:Tuple        =		use_bias
        __UpperCamelCase					:int        =		block_size
        __UpperCamelCase					:Union[str, Any]        =		num_random_blocks







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

        __UpperCamelCase					:int        =		None
        if self.use_attention_mask:
            __UpperCamelCase					:Optional[int]        =		random_attention_mask([self.batch_size, self.seq_length])

        __UpperCamelCase					:int        =		None
        if self.use_token_type_ids:
            __UpperCamelCase					:List[Any]        =		ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size)

        __UpperCamelCase					:Optional[Any]        =		BigBirdConfig(
            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=_lowerCamelCase			,			initializer_range=self.initializer_range			,			attention_type=self.attention_type			,			block_size=self.block_size			,			num_random_blocks=self.num_random_blocks			,			use_bias=self.use_bias			,			rescale_embeddings=self.rescale_embeddings			,			)

        return config, input_ids, token_type_ids, attention_mask







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:List[Any]        =		self.prepare_config_and_inputs()
        __UpperCamelCase					:List[str]        =		config_and_inputs
        __UpperCamelCase					:Any        =		{
            '''input_ids''': input_ids,
            '''token_type_ids''': token_type_ids,
            '''attention_mask''': attention_mask,
        }
        return config, inputs_dict


@require_flax
class      lowerCamelCase_					(       a__						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						Tuple      =				(
        (
            FlaxBigBirdForCausalLM,
            FlaxBigBirdModel,
            FlaxBigBirdForPreTraining,
            FlaxBigBirdForMaskedLM,
            FlaxBigBirdForMultipleChoice,
            FlaxBigBirdForQuestionAnswering,
            FlaxBigBirdForSequenceClassification,
            FlaxBigBirdForTokenClassification,
        )
        if is_flax_available()
        else ()
    )

    a__     :						str      =				False
    a__     :						str      =				False







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:Dict        =		FlaxBigBirdModelTester(self)







    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def       UpperCamelCase__       (     self)	->     str:
        super().test_from_pretrained_save_pretrained()







    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def       UpperCamelCase__       (     self)	->     List[str]:
        super().test_from_pretrained_with_no_automatic_init()







    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def       UpperCamelCase__       (     self)	->     List[str]:
        super().test_no_automatic_init()







    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        super().test_hidden_states_output()







    @slow
    def       UpperCamelCase__       (     self)	->     List[Any]:
        for model_class_name in self.all_model_classes:
            __UpperCamelCase					:Optional[Any]        =		model_class_name.from_pretrained('''google/bigbird-roberta-base''')
            self.assertIsNotNone(_lowerCamelCase)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        if self.test_attn_probs:
            super().test_attention_outputs()







    @slow
    # copied from `test_modeling_flax_common` because it takes much longer than other models
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Tuple        =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__):
                __UpperCamelCase					:Tuple        =		self._prepare_for_class(_lowerCamelCase			,			_lowerCamelCase)
                __UpperCamelCase					:List[Any]        =		model_class(_lowerCamelCase)

                @jax.jit
                def model_jitted(__lowercase			,			__lowercase=None			,			**__lowercase):
                    return model(input_ids=_lowerCamelCase			,			attention_mask=_lowerCamelCase			,			**_lowerCamelCase)

                with self.subTest('''JIT Enabled'''):
                    __UpperCamelCase					:str        =		model_jitted(**_lowerCamelCase).to_tuple()

                with self.subTest('''JIT Disabled'''):
                    with jax.disable_jit():
                        __UpperCamelCase					:str        =		model_jitted(**_lowerCamelCase).to_tuple()

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







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase=1E-5			,			__lowercase="outputs"			,			__lowercase=None)	->     Any:
        # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
        # an effort was done to return `attention_probs` (yet to be verified).
        if name.startswith('''outputs.attentions'''):
            return
        else:
            super().check_pt_flax_outputs(_lowerCamelCase			,			_lowerCamelCase			,			_lowerCamelCase			,			_lowerCamelCase			,			_lowerCamelCase			,			_lowerCamelCase)






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    print(f"""Vertex\tShortest Distance from vertex {src}"""     )
    for i, d in enumerate(SCREAMING_SNAKE_CASE     ):
        print(f"""{i}\t\t{d}"""     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for j in range(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:int        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
        if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
            return True
    return False



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[float('''inf'''     )] * vertex_count
    __UpperCamelCase					:str        =		0.0

    for _ in range(vertex_count - 1     ):
        for j in range(SCREAMING_SNAKE_CASE     ):
            __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Dict        =		(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

            if distance[u] != float('''inf'''     ) and distance[u] + w < distance[v]:
                __UpperCamelCase					:Any        =		distance[u] + w

    __UpperCamelCase					:Tuple        =		check_negative_cycle(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    if negative_cycle_exists:
        raise Exception('''Negative cycle found'''     )

    return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    __lowercase										= int(input('''Enter number of vertices: ''').strip())
    __lowercase										= int(input('''Enter number of edges: ''').strip())

    __lowercase										= [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        __lowercase			,	__lowercase			,	__lowercase										= (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        __lowercase										= {'''src''': src, '''dst''': dest, '''weight''': weight}

    __lowercase										= int(input('''\nEnter shortest path source:''').strip())
    __lowercase										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)





