




"""simple docstring"""





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


A				=							get_logger(__name__)


A				=							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   a__					:


			@add_start_docstrings(UpperCamelCase_)
			def __call__(   self   :     Dict   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray):


						"""simple docstring"""




						raise NotImplementedError(
						    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")






class   a__					:


			@add_start_docstrings(UpperCamelCase_)
			def __call__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray):


						"""simple docstring"""




						raise NotImplementedError(
						    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called.")






class   a__					(						__magic_name__ ):


			@add_start_docstrings(UpperCamelCase_)
			def __call__(   self   :     Any   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int   ,	**UpperCamelCase_   :     int):


						"""simple docstring"""




						for processor in self:
									__UpperCAmelCase						:			Any												=       inspect.signature(processor.__call__).parameters
									if len(UpperCamelCase_) > 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						:			Union[str, Any]												=       processor(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	**UpperCamelCase_)
									else:
												__UpperCAmelCase						:			Optional[int]												=       processor(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     float):


						"""simple docstring"""




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

						__UpperCAmelCase						:			Optional[Any]												=       temperature


			def __call__(   self   :     Optional[int]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       scores / self.temperature
						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     float   ,	UpperCamelCase_   :     float = -float("Inf")   ,	UpperCamelCase_   :     int = 1):


						"""simple docstring"""




						if not isinstance(UpperCamelCase_   ,	UpperCamelCase_) 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(UpperCamelCase_   ,	UpperCamelCase_) 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						:			Optional[int]												=       top_p
						__UpperCAmelCase						:			List[Any]												=       filter_value
						__UpperCAmelCase						:			Tuple												=       min_tokens_to_keep


			def __call__(   self   :     str   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, Any]												=       lax.top_k(UpperCamelCase_   ,	scores.shape[-1])

						__UpperCAmelCase						:			int												=       jnp.full_like(UpperCamelCase_   ,	self.filter_value)
						__UpperCAmelCase						:			List[Any]												=       jax.nn.softmax(UpperCamelCase_   ,	axis=-1).cumsum(axis=-1)
						__UpperCAmelCase						:			Tuple												=       cumulative_probs < self.top_p

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

						# min tokens to keep
						__UpperCAmelCase						:			List[str]												=       score_mask.at[:, : self.min_tokens_to_keep].set(UpperCamelCase_)

						__UpperCAmelCase						:			Union[str, Any]												=       jnp.where(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       jax.lax.sort_key_val(UpperCamelCase_   ,	UpperCamelCase_)[-1]

						return next_scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[Any]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     float = -float("Inf")   ,	UpperCamelCase_   :     int = 1):


						"""simple docstring"""




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

						__UpperCAmelCase						:			List[str]												=       max(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       filter_value


			def __call__(   self   :     Optional[int]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       scores.shape
						__UpperCAmelCase						:			Union[str, Any]												=       jnp.full(batch_size * vocab_size   ,	self.filter_value)

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

						__UpperCAmelCase						:			str												=       next_scores_flat.at[topk_indices_flat].set(UpperCamelCase_)
						__UpperCAmelCase						:			Any												=       next_scores_flat.reshape(UpperCamelCase_   ,	UpperCamelCase_)
						return next_scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Union[str, Any]   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       bos_token_id


			def __call__(   self   :     int   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       jnp.full(scores.shape   ,	-float("inf"))

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

						__UpperCAmelCase						:			List[Any]												=       jnp.where(UpperCamelCase_   ,	new_scores.at[:, self.bos_token_id].set(0)   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       max_length
						__UpperCAmelCase						:			Optional[Any]												=       eos_token_id


			def __call__(   self   :     int   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       jnp.full(scores.shape   ,	-float("inf"))

						__UpperCAmelCase						:			Dict												=       1 - jnp.bool_(cur_len - self.max_length + 1)

						__UpperCAmelCase						:			Any												=       jnp.where(UpperCamelCase_   ,	new_scores.at[:, self.eos_token_id].set(0)   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     List[str]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




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

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

						__UpperCAmelCase						:			List[Any]												=       min_length
						__UpperCAmelCase						:			Tuple												=       eos_token_id


			def __call__(   self   :     Dict   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       1 - jnp.clip(cur_len - self.min_length   ,	0   ,	1)

						__UpperCAmelCase						:			Optional[int]												=       jnp.where(UpperCamelCase_   ,	scores.at[:, self.eos_token_id].set(-float("inf"))   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     int   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       list(UpperCamelCase_)
						__UpperCAmelCase						:			Tuple												=       begin_index


			def __call__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       1 - jnp.bool_(cur_len - self.begin_index)

						__UpperCAmelCase						:			str												=       jnp.where(UpperCamelCase_   ,	scores.at[:, self.begin_suppress_tokens].set(-float("inf"))   ,	UpperCamelCase_)

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Dict   ,	UpperCamelCase_   :     list):


						"""simple docstring"""




						__UpperCAmelCase						:			str												=       list(UpperCamelCase_)


			def __call__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       scores.at[..., self.suppress_tokens].set(-float("inf"))

						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       dict(UpperCamelCase_)
						# 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						:			int												=       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(UpperCamelCase_)
						__UpperCAmelCase						:			str												=       jnp.intaa(UpperCamelCase_)


			def __call__(   self   :     Any   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     jnp.ndarray   ,	UpperCamelCase_   :     int):


						"""simple docstring"""




						def _force_token(UpperCamelCase_   :     List[Any]):
									__UpperCAmelCase						:			Optional[Any]												=       scores.shape[0]
									__UpperCAmelCase						:			int												=       self.force_token_array[generation_idx]

									__UpperCAmelCase						:			List[Any]												=       jnp.ones_like(UpperCamelCase_   ,	dtype=scores.dtype) * -float("inf")
									__UpperCAmelCase						:			List[str]												=       jnp.zeros((batch_size, 1)   ,	dtype=scores.dtype)
									__UpperCAmelCase						:			Dict												=       lax.dynamic_update_slice(UpperCamelCase_   ,	UpperCamelCase_   ,	(0, current_token))
									return new_scores

						__UpperCAmelCase						:			List[str]												=       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(UpperCamelCase_)   ,	lambda: scores   ,	)   ,	)
						return scores






class   a__					(						__magic_name__ ):


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     Optional[int]   ,	UpperCamelCase_   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       generate_config.eos_token_id
						__UpperCAmelCase						:			List[str]												=       generate_config.no_timestamps_token_id
						__UpperCAmelCase						:			Dict												=       generate_config.no_timestamps_token_id + 1

						__UpperCAmelCase						:			Tuple												=       decoder_input_length + 1

						if generate_config.is_multilingual:
									# room for language token and task token
									self.begin_index += 2
						if hasattr(UpperCamelCase_   ,	"max_initial_timestamp_index"):
									__UpperCAmelCase						:			Optional[int]												=       generate_config.max_initial_timestamp_index
						else:
									__UpperCAmelCase						:			str												=       model_config.vocab_size
						if self.max_initial_timestamp_index is None:
									__UpperCAmelCase						:			Union[str, Any]												=       model_config.vocab_size


			def __call__(   self   :     Tuple   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     List[str]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       scores.at[:, self.no_timestamps_token_id].set(-float("inf"))

						def handle_pairs(UpperCamelCase_   :     str   ,	UpperCamelCase_   :     List[Any]):
									__UpperCAmelCase						:			Dict												=       jnp.where((cur_len - self.begin_index) >= 1   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			Dict												=       jnp.where(
									    input_ids_k[cur_len - 1] >= self.timestamp_begin   ,	True and last_was_timestamp   ,	UpperCamelCase_   ,	)

									__UpperCAmelCase						:			str												=       jnp.where((cur_len - self.begin_index) < 2   ,	UpperCamelCase_   ,	UpperCamelCase_)
									__UpperCAmelCase						:			List[Any]												=       jnp.where(
									    input_ids_k[cur_len - 2] >= self.timestamp_begin   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	)

									return jnp.where(
									    UpperCamelCase_   ,	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"))   ,	)   ,	UpperCamelCase_   ,	)

						__UpperCAmelCase						:			List[Any]												=       jax.vmap(UpperCamelCase_)(UpperCamelCase_   ,	UpperCamelCase_)

						__UpperCAmelCase						:			Dict												=       jnp.where(cur_len == self.begin_index   ,	UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			Optional[int]												=       jnp.where(
						    self.max_initial_timestamp_index is not None   ,	True and apply_max_initial_timestamp   ,	UpperCamelCase_   ,	)

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

						__UpperCAmelCase						:			str												=       jnp.where(
						    UpperCamelCase_   ,	scores.at[:, last_allowed + 1 :].set(-float("inf"))   ,	UpperCamelCase_   ,	)

						# if sum of probability over timestamps is above any other token, sample timestamp
						__UpperCAmelCase						:			Dict												=       jax.nn.log_softmax(UpperCamelCase_   ,	axis=-1)

						def handle_cumulative_probs(UpperCamelCase_   :     Dict   ,	UpperCamelCase_   :     List[str]):
									__UpperCAmelCase						:			int												=       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,	axis=-1)
									__UpperCAmelCase						:			int												=       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"))   ,	UpperCamelCase_   ,	)

						__UpperCAmelCase						:			Optional[Any]												=       jax.vmap(UpperCamelCase_)(UpperCamelCase_   ,	UpperCamelCase_)

						return scores










def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   str:





    """simple docstring"""

    __A       =						""""""
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   dict[str, str]:





    """simple docstring"""

    __A       =						[chr(i + 6_5     ) for i in range(2_6     )]
    # Remove duplicate characters from key
    __A       =						remove_duplicates(key.upper()     )
    __A       =						len(__lowercase     )
    # First fill cipher with key characters
    __A       =						{alphabet[i]: char for i, char in enumerate(__lowercase     )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(__lowercase     )      ,					2_6     ):
        __A       =						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __A       =						alphabet[i - offset]
        __A       =						char
    return cipher_alphabet



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    return "".join(cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    __A       =						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





    """simple docstring"""

    __A       =						input("""Enter message to encode or decode: """     ).strip()
    __A       =						input("""Enter keyword: """     ).strip()
    __A       =						input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
    try:
        __A       =						{"""e""": encipher, """d""": decipher}[option]
    except KeyError:
        raise KeyError("""invalid input option"""     )
    __A       =						create_cipher_map(__lowercase     )
    print(func(__lowercase      ,					__lowercase     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




