from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				"""marian"""
    a__     :						str      =				["""past_key_values"""]
    a__     :						List[Any]      =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__(     self			,			__lowercase=58_101			,			__lowercase=None			,			__lowercase=1_024			,			__lowercase=12			,			__lowercase=4_096			,			__lowercase=16			,			__lowercase=12			,			__lowercase=4_096			,			__lowercase=16			,			__lowercase=0.0			,			__lowercase=0.0			,			__lowercase=True			,			__lowercase=True			,			__lowercase="gelu"			,			__lowercase=1_024			,			__lowercase=0.1			,			__lowercase=0.0			,			__lowercase=0.0			,			__lowercase=0.02			,			__lowercase=58_100			,			__lowercase=False			,			__lowercase=58_100			,			__lowercase=0			,			__lowercase=0			,			__lowercase=True			,			**__lowercase			,			)	->     Union[str, Any]:
        __UpperCamelCase					:str        =		vocab_size
        __UpperCamelCase					:Dict        =		decoder_vocab_size or vocab_size
        __UpperCamelCase					:int        =		max_position_embeddings
        __UpperCamelCase					:Dict        =		d_model
        __UpperCamelCase					:Any        =		encoder_ffn_dim
        __UpperCamelCase					:List[str]        =		encoder_layers
        __UpperCamelCase					:List[str]        =		encoder_attention_heads
        __UpperCamelCase					:str        =		decoder_ffn_dim
        __UpperCamelCase					:Any        =		decoder_layers
        __UpperCamelCase					:List[str]        =		decoder_attention_heads
        __UpperCamelCase					:Optional[int]        =		dropout
        __UpperCamelCase					:Dict        =		attention_dropout
        __UpperCamelCase					:List[str]        =		activation_dropout
        __UpperCamelCase					:Optional[Any]        =		activation_function
        __UpperCamelCase					:Union[str, Any]        =		init_std
        __UpperCamelCase					:Tuple        =		encoder_layerdrop
        __UpperCamelCase					:Dict        =		decoder_layerdrop
        __UpperCamelCase					:Optional[Any]        =		use_cache
        __UpperCamelCase					:int        =		encoder_layers
        __UpperCamelCase					:Dict        =		scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCamelCase					:Optional[Any]        =		share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=__lowercase			,			eos_token_id=__lowercase			,			is_encoder_decoder=__lowercase			,			decoder_start_token_id=__lowercase			,			forced_eos_token_id=__lowercase			,			**__lowercase			,			)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
            __UpperCamelCase					:Dict        =		OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ])

            if self.use_past:
                __UpperCamelCase					:Optional[int]        =		{0: '''batch'''}
                __UpperCamelCase					:Optional[int]        =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            else:
                __UpperCamelCase					:Tuple        =		{0: '''batch''', 1: '''decoder_sequence'''}
                __UpperCamelCase					:List[Any]        =		{0: '''batch''', 1: '''decoder_sequence'''}

            if self.use_past:
                self.fill_with_past_key_values_(__lowercase			,			direction='''inputs''')
        elif self.task == "causal-lm":
            # TODO: figure this case out.
            __UpperCamelCase					:int        =		OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ])
            if self.use_past:
                __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		self.num_layers
                for i in range(__lowercase):
                    __UpperCamelCase					:List[Any]        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                    __UpperCamelCase					:List[Any]        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
            __UpperCamelCase					:Tuple        =		OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                    ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                ])

        return common_inputs







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
            __UpperCamelCase					:Any        =		super().outputs
        else:
            __UpperCamelCase					:Tuple        =		super(__lowercase			,			self).outputs
            if self.use_past:
                __UpperCamelCase  ,	__UpperCamelCase					:str        =		self.num_layers
                for i in range(__lowercase):
                    __UpperCamelCase					:Union[str, Any]        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                    __UpperCamelCase					:Tuple        =		{0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = -1			,			__lowercase = -1			,			__lowercase = False			,			__lowercase = None			,			)	->     Mapping[str, Any]:
        __UpperCamelCase					:Optional[int]        =		self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        # Generate decoder inputs
        __UpperCamelCase					:Dict        =		seq_length if not self.use_past else 1
        __UpperCamelCase					:str        =		self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)
        __UpperCamelCase					:Any        =		{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        __UpperCamelCase					:List[str]        =		dict(**__lowercase			,			**__lowercase)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
            else:
                import torch
            __UpperCamelCase  ,	__UpperCamelCase					:str        =		common_inputs['''input_ids'''].shape
            __UpperCamelCase					:str        =		common_inputs['''decoder_input_ids'''].shape[1]
            __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		self.num_attention_heads
            __UpperCamelCase					:Optional[Any]        =		(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            __UpperCamelCase					:List[Any]        =		decoder_seq_length + 3
            __UpperCamelCase					:Any        =		(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            __UpperCamelCase					:List[Any]        =		torch.cat(
                [common_inputs['''decoder_attention_mask'''], torch.ones(__lowercase			,			__lowercase)]			,			dim=1)

            __UpperCamelCase					:Optional[Any]        =		[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            __UpperCamelCase  ,	__UpperCamelCase					:int        =		self.num_layers
            __UpperCamelCase					:Optional[Any]        =		min(__lowercase			,			__lowercase)
            __UpperCamelCase					:str        =		max(__lowercase			,			__lowercase) - min_num_layers
            __UpperCamelCase					:Dict        =		'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

            for _ in range(__lowercase):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__lowercase),
                        torch.zeros(__lowercase),
                        torch.zeros(__lowercase),
                        torch.zeros(__lowercase),
                    ))
            # TODO: test this.
            __UpperCamelCase					:Optional[Any]        =		encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
            for _ in range(__lowercase			,			__lowercase):
                common_inputs["past_key_values"].append((torch.zeros(__lowercase), torch.zeros(__lowercase)))
        return common_inputs







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = -1			,			__lowercase = -1			,			__lowercase = False			,			__lowercase = None			,			)	->     Mapping[str, Any]:
        __UpperCamelCase					:str        =		self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''')
            else:
                import torch
            __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		common_inputs['''input_ids'''].shape
            # Not using the same length for past_key_values
            __UpperCamelCase					:Optional[int]        =		seqlen + 2
            __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self.num_layers
            __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		self.num_attention_heads
            __UpperCamelCase					:Optional[Any]        =		(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            __UpperCamelCase					:Dict        =		common_inputs['''attention_mask'''].dtype
            __UpperCamelCase					:List[str]        =		torch.cat(
                [common_inputs['''attention_mask'''], torch.ones(__lowercase			,			__lowercase			,			dtype=__lowercase)]			,			dim=1)
            __UpperCamelCase					:Any        =		[
                (torch.zeros(__lowercase), torch.zeros(__lowercase)) for _ in range(__lowercase)
            ]
        return common_inputs







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = -1			,			__lowercase = -1			,			__lowercase = False			,			__lowercase = None			,			)	->     Mapping[str, Any]:
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        __UpperCamelCase					:Tuple        =		compute_effective_axis_dimension(
            __lowercase			,			fixed_dimension=OnnxConfig.default_fixed_batch			,			num_token_to_add=0)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        __UpperCamelCase					:List[str]        =		tokenizer.num_special_tokens_to_add(__lowercase)
        __UpperCamelCase					:Any        =		compute_effective_axis_dimension(
            __lowercase			,			fixed_dimension=OnnxConfig.default_fixed_sequence			,			num_token_to_add=__lowercase)

        # Generate dummy inputs according to compute batch and sequence
        __UpperCamelCase					:Optional[Any]        =		[''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
        __UpperCamelCase					:str        =		dict(tokenizer(__lowercase			,			return_tensors=__lowercase))
        return common_inputs







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = -1			,			__lowercase = -1			,			__lowercase = False			,			__lowercase = None			,			)	->     Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
            __UpperCamelCase					:Optional[Any]        =		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                __lowercase			,			batch_size=__lowercase			,			seq_length=__lowercase			,			is_pair=__lowercase			,			framework=__lowercase)

        else:
            __UpperCamelCase					:Any        =		self._generate_dummy_inputs_for_causal_lm(
                __lowercase			,			batch_size=__lowercase			,			seq_length=__lowercase			,			is_pair=__lowercase			,			framework=__lowercase)

        return common_inputs







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[Any]:
        if self.task in ["default", "seq2seq-lm"]:
            __UpperCamelCase					:Tuple        =		super()._flatten_past_key_values_(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)
        else:
            __UpperCamelCase					:Any        =		super(__lowercase			,			self)._flatten_past_key_values_(
                __lowercase			,			__lowercase			,			__lowercase			,			__lowercase)







    @property
    def       UpperCamelCase__       (     self)	->     float:
        return 1E-4






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)





