from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Any      =				"""t5"""
    a__     :						List[str]      =				["""past_key_values"""]
    a__     :						Any      =				{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







    def __init__(     self			,			__lowercase=32_128			,			__lowercase=512			,			__lowercase=64			,			__lowercase=2_048			,			__lowercase=6			,			__lowercase=None			,			__lowercase=8			,			__lowercase=32			,			__lowercase=128			,			__lowercase=0.1			,			__lowercase=1E-6			,			__lowercase=1.0			,			__lowercase="relu"			,			__lowercase=True			,			__lowercase=True			,			__lowercase=0			,			__lowercase=1			,			**__lowercase			,			)	->     Dict:
        __UpperCamelCase					:Dict        =		vocab_size
        __UpperCamelCase					:List[str]        =		d_model
        __UpperCamelCase					:Any        =		d_kv
        __UpperCamelCase					:Tuple        =		d_ff
        __UpperCamelCase					:Optional[Any]        =		num_layers
        __UpperCamelCase					:Tuple        =		(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __UpperCamelCase					:Tuple        =		num_heads
        __UpperCamelCase					:Tuple        =		relative_attention_num_buckets
        __UpperCamelCase					:Optional[int]        =		relative_attention_max_distance
        __UpperCamelCase					:Any        =		dropout_rate
        __UpperCamelCase					:List[str]        =		layer_norm_epsilon
        __UpperCamelCase					:Optional[int]        =		initializer_factor
        __UpperCamelCase					:str        =		feed_forward_proj
        __UpperCamelCase					:Optional[int]        =		use_cache

        __UpperCamelCase					:List[str]        =		self.feed_forward_proj.split('''-''')
        __UpperCamelCase					:Optional[Any]        =		act_info[-1]
        __UpperCamelCase					:Any        =		act_info[0] == '''gated'''

        if len(__lowercase) > 1 and act_info[0] != "gated" or len(__lowercase) > 2:
            raise ValueError(
                f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                '''\'gated-gelu\' or \'relu\'''')

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            __UpperCamelCase					:List[str]        =		'''gelu_new'''

        super().__init__(
            pad_token_id=__lowercase			,			eos_token_id=__lowercase			,			is_encoder_decoder=__lowercase			,			**__lowercase			,			)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @property
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        __UpperCamelCase					:Optional[int]        =		{
            '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
            '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
        }
        if self.use_past:
            __UpperCamelCase					:Optional[Any]        =		'''past_encoder_sequence + sequence'''
            __UpperCamelCase					:Optional[Any]        =		{0: '''batch'''}
            __UpperCamelCase					:List[str]        =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
            __UpperCamelCase					:Optional[int]        =		{0: '''batch''', 1: '''decoder_sequence'''}
            __UpperCamelCase					:Tuple        =		{0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
            self.fill_with_past_key_values_(__lowercase			,			direction='''inputs''')

        return common_inputs







    @property
    def       UpperCamelCase__       (     self)	->     int:
        return 13






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)





