import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


__lowercase										= get_logger(__name__)


__lowercase										= r'''
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

'''


class      lowerCamelCase_					:






    '''simple docstring'''






    @add_start_docstrings(lowercase_)
    def __call__(     self			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        raise NotImplementedError(
            f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""")


class      lowerCamelCase_					:






    '''simple docstring'''






    @add_start_docstrings(lowercase_)
    def __call__(     self			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        raise NotImplementedError(
            f"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""")


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @add_start_docstrings(lowercase_)
    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase)	->     jnp.ndarray:







        for processor in self:
            __UpperCamelCase					:List[str]        =		inspect.signature(processor.__call__).parameters
            if len(lowercase_) > 3:
                if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
                    raise ValueError(
                        f"""Make sure that all the required parameters: {list(function_args.keys())} for """
                        f"""{processor.__class__} are passed to the logits processor.""")
                __UpperCamelCase					:Tuple        =		processor(lowercase_			,			lowercase_			,			lowercase_			,			**lowercase_)
            else:
                __UpperCamelCase					:List[str]        =		processor(lowercase_			,			lowercase_			,			lowercase_)
        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase)	->     Any:







        if not isinstance(lowercase_			,			lowercase_) or not (temperature > 0):
            raise ValueError(f"""`temperature` has to be a strictly positive float, but is {temperature}""")

        __UpperCamelCase					:List[Any]        =		temperature







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase					:List[Any]        =		scores / self.temperature
        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase = -float('''Inf''')			,			__lowercase = 1)	->     int:







        if not isinstance(lowercase_			,			lowercase_) or (top_p < 0 or top_p > 1.0):
            raise ValueError(f"""`top_p` has to be a float > 0 and < 1, but is {top_p}""")
        if not isinstance(lowercase_			,			lowercase_) or (min_tokens_to_keep < 1):
            raise ValueError(f"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""")

        __UpperCamelCase					:str        =		top_p
        __UpperCamelCase					:str        =		filter_value
        __UpperCamelCase					:str        =		min_tokens_to_keep







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase  ,	__UpperCamelCase					:Any        =		lax.top_k(lowercase_			,			scores.shape[-1])

        __UpperCamelCase					:Union[str, Any]        =		jnp.full_like(lowercase_			,			self.filter_value)
        __UpperCamelCase					:List[str]        =		jax.nn.softmax(lowercase_			,			axis=-1).cumsum(axis=-1)
        __UpperCamelCase					:int        =		cumulative_probs < self.top_p

        # include the token that is higher than top_p as well
        __UpperCamelCase					:Any        =		jnp.roll(lowercase_			,			1)
        score_mask |= score_mask.at[:, 0].set(lowercase_)

        # min tokens to keep
        __UpperCamelCase					:Tuple        =		score_mask.at[:, : self.min_tokens_to_keep].set(lowercase_)

        __UpperCamelCase					:str        =		jnp.where(lowercase_			,			lowercase_			,			lowercase_)
        __UpperCamelCase					:str        =		jax.lax.sort_key_val(lowercase_			,			lowercase_)[-1]

        return next_scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase = -float('''Inf''')			,			__lowercase = 1)	->     List[str]:







        if not isinstance(lowercase_			,			lowercase_) or top_k <= 0:
            raise ValueError(f"""`top_k` has to be a strictly positive integer, but is {top_k}""")

        __UpperCamelCase					:Any        =		max(lowercase_			,			lowercase_)
        __UpperCamelCase					:str        =		filter_value







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase  ,	__UpperCamelCase					:Any        =		scores.shape
        __UpperCamelCase					:int        =		jnp.full(batch_size * vocab_size			,			self.filter_value)

        __UpperCamelCase					:Tuple        =		min(self.top_k			,			scores.shape[-1])  # Safety check
        __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		lax.top_k(lowercase_			,			lowercase_)
        __UpperCamelCase					:List[str]        =		jnp.broadcast_to((jnp.arange(lowercase_) * vocab_size)[:, None]			,			(batch_size, topk)).flatten()
        __UpperCamelCase					:List[Any]        =		topk_scores.flatten()
        __UpperCamelCase					:Optional[int]        =		topk_indices.flatten() + shift

        __UpperCamelCase					:Optional[int]        =		next_scores_flat.at[topk_indices_flat].set(lowercase_)
        __UpperCamelCase					:Optional[int]        =		next_scores_flat.reshape(lowercase_			,			lowercase_)
        return next_scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase)	->     Dict:
        __UpperCamelCase					:Optional[int]        =		bos_token_id







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase					:Optional[int]        =		jnp.full(scores.shape			,			-float('''inf'''))

        __UpperCamelCase					:Optional[int]        =		1 - jnp.bool_(cur_len - 1)

        __UpperCamelCase					:Tuple        =		jnp.where(lowercase_			,			new_scores.at[:, self.bos_token_id].set(0)			,			lowercase_)

        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase)	->     Optional[Any]:
        __UpperCamelCase					:Optional[int]        =		max_length
        __UpperCamelCase					:Tuple        =		eos_token_id







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase					:List[str]        =		jnp.full(scores.shape			,			-float('''inf'''))

        __UpperCamelCase					:Optional[Any]        =		1 - jnp.bool_(cur_len - self.max_length + 1)

        __UpperCamelCase					:Union[str, Any]        =		jnp.where(lowercase_			,			new_scores.at[:, self.eos_token_id].set(0)			,			lowercase_)

        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase)	->     List[Any]:







        if not isinstance(lowercase_			,			lowercase_) or min_length < 0:
            raise ValueError(f"""`min_length` has to be a positive integer, but is {min_length}""")

        if not isinstance(lowercase_			,			lowercase_) or eos_token_id < 0:
            raise ValueError(f"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""")

        __UpperCamelCase					:Optional[Any]        =		min_length
        __UpperCamelCase					:Optional[Any]        =		eos_token_id







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase					:Optional[int]        =		1 - jnp.clip(cur_len - self.min_length			,			0			,			1)

        __UpperCamelCase					:Union[str, Any]        =		jnp.where(lowercase_			,			scores.at[:, self.eos_token_id].set(-float('''inf'''))			,			lowercase_)

        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase)	->     Union[str, Any]:
        __UpperCamelCase					:Optional[Any]        =		list(lowercase_)
        __UpperCamelCase					:Dict        =		begin_index







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:List[str]        =		1 - jnp.bool_(cur_len - self.begin_index)

        __UpperCamelCase					:str        =		jnp.where(lowercase_			,			scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''))			,			lowercase_)

        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase)	->     Any:
        __UpperCamelCase					:int        =		list(lowercase_)







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:
        __UpperCamelCase					:Optional[Any]        =		scores.at[..., self.suppress_tokens].set(-float('''inf'''))

        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase)	->     int:
        __UpperCamelCase					:Union[str, Any]        =		dict(lowercase_)
        # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
        # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
        # Indexes without forced tokens will have a negative value.
        __UpperCamelCase					:Any        =		jnp.ones((max(force_token_map.keys()) + 1)			,			dtype=jnp.intaa) * -1
        for index, token in force_token_map.items():
            if token is not None:
                __UpperCamelCase					:Dict        =		force_token_array.at[index].set(lowercase_)
        __UpperCamelCase					:Optional[int]        =		jnp.intaa(lowercase_)







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     jnp.ndarray:







        def _force_token(__lowercase):
            __UpperCamelCase					:Tuple        =		scores.shape[0]
            __UpperCamelCase					:Union[str, Any]        =		self.force_token_array[generation_idx]

            __UpperCamelCase					:int        =		jnp.ones_like(lowercase_			,			dtype=scores.dtype) * -float('''inf''')
            __UpperCamelCase					:Optional[Any]        =		jnp.zeros((batch_size, 1)			,			dtype=scores.dtype)
            __UpperCamelCase					:Optional[Any]        =		lax.dynamic_update_slice(lowercase_			,			lowercase_			,			(0, current_token))
            return new_scores

        __UpperCamelCase					:Optional[Any]        =		lax.cond(
            cur_len >= self.force_token_array.shape[0]			,			lambda: scores			,			lambda: lax.cond(
                self.force_token_array[cur_len] >= 0			,			lambda: _force_token(lowercase_)			,			lambda: scores			,			)			,			)
        return scores


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		generate_config.eos_token_id
        __UpperCamelCase					:Union[str, Any]        =		generate_config.no_timestamps_token_id
        __UpperCamelCase					:Optional[int]        =		generate_config.no_timestamps_token_id + 1

        __UpperCamelCase					:Dict        =		decoder_input_length + 1

        if generate_config.is_multilingual:
            # room for language token and task token
            self.begin_index += 2
        if hasattr(lowercase_			,			'''max_initial_timestamp_index'''):
            __UpperCamelCase					:List[str]        =		generate_config.max_initial_timestamp_index
        else:
            __UpperCamelCase					:List[Any]        =		model_config.vocab_size
        if self.max_initial_timestamp_index is None:
            __UpperCamelCase					:str        =		model_config.vocab_size







    def __call__(     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Dict:
        __UpperCamelCase					:Tuple        =		scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''))

        def handle_pairs(__lowercase			,			__lowercase):
            __UpperCamelCase					:Optional[int]        =		jnp.where((cur_len - self.begin_index) >= 1			,			lowercase_			,			lowercase_)
            __UpperCamelCase					:str        =		jnp.where(
                input_ids_k[cur_len - 1] >= self.timestamp_begin			,			True and last_was_timestamp			,			lowercase_			,			)

            __UpperCamelCase					:str        =		jnp.where((cur_len - self.begin_index) < 2			,			lowercase_			,			lowercase_)
            __UpperCamelCase					:Tuple        =		jnp.where(
                input_ids_k[cur_len - 2] >= self.timestamp_begin			,			lowercase_			,			lowercase_			,			)

            return jnp.where(
                lowercase_			,			jnp.where(
                    penultimate_was_timestamp > 0			,			scores_k.at[self.timestamp_begin :].set(-float('''inf'''))			,			scores_k.at[: self.eos_token_id].set(-float('''inf'''))			,			)			,			lowercase_			,			)

        __UpperCamelCase					:List[Any]        =		jax.vmap(lowercase_)(lowercase_			,			lowercase_)

        __UpperCamelCase					:str        =		jnp.where(cur_len == self.begin_index			,			lowercase_			,			lowercase_)
        __UpperCamelCase					:List[str]        =		jnp.where(
            self.max_initial_timestamp_index is not None			,			True and apply_max_initial_timestamp			,			lowercase_			,			)

        __UpperCamelCase					:Optional[Any]        =		self.timestamp_begin + self.max_initial_timestamp_index

        __UpperCamelCase					:Optional[int]        =		jnp.where(
            lowercase_			,			scores.at[:, last_allowed + 1 :].set(-float('''inf'''))			,			lowercase_			,			)

        # if sum of probability over timestamps is above any other token, sample timestamp
        __UpperCamelCase					:Optional[Any]        =		jax.nn.log_softmax(lowercase_			,			axis=-1)

        def handle_cumulative_probs(__lowercase			,			__lowercase):
            __UpperCamelCase					:List[Any]        =		jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]			,			axis=-1)
            __UpperCamelCase					:List[str]        =		jnp.max(logprobs_k[: self.timestamp_begin])
            return jnp.where(
                timestamp_logprob > max_text_token_logprob			,			scores_k.at[: self.timestamp_begin].set(-float('''inf'''))			,			lowercase_			,			)

        __UpperCamelCase					:Any        =		jax.vmap(lowercase_)(lowercase_			,			lowercase_)

        return scores






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)





