






"""simple docstring"""







import string
from math import logaa





def 						lowercase__(A ,		A						)    ->int:




						"""simple docstring"""



						lowercase__    :				List[Any]=	document.translate(
						    str.maketrans("" ,		"" ,		string.punctuation						)						).replace("\n" ,		""						)
						lowercase__    :				int=	document_without_punctuation.split(" "						)  # word tokenization
						return len([word for word in tokenize_document if word.lower() == term.lower()]						)





def 						lowercase__(A ,		A						)    ->tuple[int, int]:




						"""simple docstring"""



						lowercase__    :				Any=	corpus.lower().translate(
						    str.maketrans("" ,		"" ,		string.punctuation						)						)  # strip all punctuation and replace it with ''
						lowercase__    :				Optional[int]=	corpus_without_punctuation.split("\n"						)
						lowercase__    :				List[str]=	term.lower()
						return (len([doc for doc in docs if term in doc]						), len(A						))





def 						lowercase__(A ,		A ,		A=False						)    ->float:




						"""simple docstring"""



						if smoothing:
												if n == 0:
																		raise ValueError("log10(0) is undefined."						)
												return round(1 + logaa(n / (1 + df)						) ,		3						)

						if df == 0:
												raise ZeroDivisionError("df must be > 0"						)
						elif n == 0:
												raise ValueError("log10(0) is undefined."						)
						return round(logaa(n / df						) ,		3						)







def 						lowercase__(A ,		A						)    ->float:




						"""simple docstring"""



						return round(tf * idf ,		3						)














"""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			:    Any				 =		get_logger(__name__)


a			:    Any				 =		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    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase:




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												raise NotImplementedError(
												    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												for processor in self:
																		lowercase__    :				Optional[Any]=	inspect.signature(processor.__call__      ).parameters
																		if len(snake_case__      ) > 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.'''      )
																								lowercase__    :				Union[str, Any]=	processor(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      )
																		else:
																								lowercase__    :				Dict=	processor(snake_case__	,  snake_case__	,  snake_case__      )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or not (temperature > 0):
																		raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''      )

												lowercase__    :				Any=	temperature




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	scores / self.temperature
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) 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(snake_case__	,  snake_case__      ) 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}'''      )

												lowercase__    :				int=	top_p
												lowercase__    :				Optional[int]=	filter_value
												lowercase__    :				Tuple=	min_tokens_to_keep




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Dict=	lax.top_k(snake_case__	,  scores.shape[-1]      )

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

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

												# min tokens to keep
												lowercase__    :				Optional[int]=	score_mask.at[:, : self.min_tokens_to_keep].set(snake_case__      )

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

												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or top_k <= 0:
																		raise ValueError(F'''`top_k` has to be a strictly positive integer, but is {top_k}'''      )

												lowercase__    :				List[Any]=	max(snake_case__	,  snake_case__      )
												lowercase__    :				Dict=	filter_value




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Optional[Any]=	scores.shape
												lowercase__    :				int=	jnp.full(batch_size * vocab_size	,  self.filter_value      )

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

												lowercase__    :				Optional[Any]=	next_scores_flat.at[topk_indices_flat].set(snake_case__      )
												lowercase__    :				str=	next_scores_flat.reshape(snake_case__	,  snake_case__      )
												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	bos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				int=	1 - jnp.bool_(cur_len - 1      )

												lowercase__    :				int=	jnp.where(snake_case__	,  new_scores.at[:, self.bos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	max_length
												lowercase__    :				str=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				Any=	1 - jnp.bool_(cur_len - self.max_length + 1      )

												lowercase__    :				Optional[int]=	jnp.where(snake_case__	,  new_scores.at[:, self.eos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) or min_length < 0:
																		raise ValueError(F'''`min_length` has to be a positive integer, but is {min_length}'''      )

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

												lowercase__    :				List[str]=	min_length
												lowercase__    :				Dict=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# create boolean flag to decide if min length penalty should be applied
												lowercase__    :				Tuple=	1 - jnp.clip(cur_len - self.min_length	,  0	,  1      )

												lowercase__    :				Dict=	jnp.where(snake_case__	,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	list(snake_case__      )
												lowercase__    :				List[Any]=	begin_index




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	1 - jnp.bool_(cur_len - self.begin_index      )

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

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	list(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	dict(snake_case__      )
												# 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.
												lowercase__    :				List[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:
																								lowercase__    :				List[Any]=	force_token_array.at[index].set(snake_case__      )
												lowercase__    :				int=	jnp.intaa(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												def _force_token(snake_case__      ):
																		lowercase__    :				Dict=	scores.shape[0]
																		lowercase__    :				Any=	self.force_token_array[generation_idx]

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

												lowercase__    :				Dict=	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(snake_case__      )	,  lambda: scores	,  )	,  )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	generate_config.eos_token_id
												lowercase__    :				Optional[int]=	generate_config.no_timestamps_token_id
												lowercase__    :				Dict=	generate_config.no_timestamps_token_id + 1

												lowercase__    :				List[Any]=	decoder_input_length + 1

												if generate_config.is_multilingual:
																		# room for language token and task token
																		self.begin_index += 2
												if hasattr(snake_case__	,  "max_initial_timestamp_index"      ):
																		lowercase__    :				int=	generate_config.max_initial_timestamp_index
												else:
																		lowercase__    :				Dict=	model_config.vocab_size
												if self.max_initial_timestamp_index is None:
																		lowercase__    :				str=	model_config.vocab_size




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# suppress <|notimestamps|> which is handled by without_timestamps
												lowercase__    :				int=	scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

												def handle_pairs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Union[str, Any]=	jnp.where((cur_len - self.begin_index) >= 1	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Tuple=	jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  snake_case__	,  )

																		lowercase__    :				int=	jnp.where((cur_len - self.begin_index) < 2	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Optional[int]=	jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  snake_case__	,  snake_case__	,  )

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

												lowercase__    :				List[str]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

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

												lowercase__    :				Any=	self.timestamp_begin + self.max_initial_timestamp_index

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

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

												def handle_cumulative_probs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Dict=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1      )
																		lowercase__    :				Union[str, Any]=	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"      )      )	,  snake_case__	,  )

												lowercase__    :				Optional[int]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												return scores






