




'''simple docstring'''

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

a       :						str								=		TypeVar('''KEY''')
a       :						Tuple								=		TypeVar('''VAL''')



@dataclass(frozen=_UpperCamelCase				,     slots=_UpperCamelCase	)
class 		SCREAMING_SNAKE_CASE__  (			Generic[KEY, VAL]	):
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			42







class 		SCREAMING_SNAKE_CASE__  (			_Item	):





			def __init__(      self						:    Tuple							):



						"""simple docstring"""




						super().__init__(a_      ,		a_							)






			def __bool__(      self						:    List[Any]							):



						"""simple docstring"""




						return False







a       :						Any								=		_DeletedItem()



class 		SCREAMING_SNAKE_CASE__  (			MutableMapping[KEY, VAL]	):





			def __init__(      self						:    Any      ,		a_						:    int = 8      ,		a_						:    float = 0.75							):



						"""simple docstring"""




						__snake_case			=		initial_block_size
						__snake_case			=		[None] * initial_block_size
						assert 0.0 < capacity_factor < 1.0
						__snake_case			=		capacity_factor
						__snake_case			=		0





			def 							A (      self						:    Union[str, Any]      ,		a_						:    KEY							):



						"""simple docstring"""




						return hash(a_							) % len(self._buckets							)





			def 							A (      self						:    Optional[Any]      ,		a_						:    int							):



						"""simple docstring"""




						return (ind + 1) % len(self._buckets							)





			def 							A (      self						:    List[str]      ,		a_						:    int      ,		a_						:    KEY      ,		a_						:    VAL							):



						"""simple docstring"""




						__snake_case			=		self._buckets[ind]
						if not stored:
									__snake_case			=		_Item(a_      ,		a_							)
									self._len += 1
									return True
						elif stored.key == key:
									__snake_case			=		_Item(a_      ,		a_							)
									return True
						else:
									return False





			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		len(self._buckets							) * self._capacity_factor
						return len(self							) >= int(a_							)





			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						if len(self._buckets							) <= self._initial_block_size:
									return False
						__snake_case			=		len(self._buckets							) * self._capacity_factor / 2
						return len(self							) < limit





			def 							A (      self						:    str      ,		a_						:    int							):



						"""simple docstring"""




						__snake_case			=		self._buckets
						__snake_case			=		[None] * new_size
						__snake_case			=		0
						for item in old_buckets:
									if item:
												self._add_item(item.key      ,		item.val							)





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						self._resize(len(self._buckets							) * 2							)





			def 							A (      self						:    int							):



						"""simple docstring"""




						self._resize(len(self._buckets							) // 2							)





			def 							A (      self						:    List[Any]      ,		a_						:    KEY							):



						"""simple docstring"""




						__snake_case			=		self._get_bucket_index(a_							)
						for _ in range(len(self._buckets							)							):
									yield ind
									__snake_case			=		self._get_next_ind(a_							)





			def 							A (      self						:    str      ,		a_						:    KEY      ,		a_						:    VAL							):



						"""simple docstring"""




						for ind in self._iterate_buckets(a_							):
									if self._try_set(a_      ,		a_      ,		a_							):
												break





			def __setitem__(      self						:    Union[str, Any]      ,		a_						:    KEY      ,		a_						:    VAL							):



						"""simple docstring"""




						if self._is_full():
									self._size_up()

						self._add_item(a_      ,		a_							)





			def __delitem__(      self						:    List[str]      ,		a_						:    KEY							):



						"""simple docstring"""




						for ind in self._iterate_buckets(a_							):
									__snake_case			=		self._buckets[ind]
									if item is None:
												raise KeyError(a_							)
									if item is _deleted:
												continue
									if item.key == key:
												__snake_case			=		_deleted
												self._len -= 1
												break
						if self._is_sparse():
									self._size_down()





			def __getitem__(      self						:    Optional[int]      ,		a_						:    KEY							):



						"""simple docstring"""




						for ind in self._iterate_buckets(a_							):
									__snake_case			=		self._buckets[ind]
									if item is None:
												break
									if item is _deleted:
												continue
									if item.key == key:
												return item.val
						raise KeyError(a_							)





			def __len__(      self						:    Dict							):



						"""simple docstring"""




						return self._len





			def __iter__(      self						:    List[str]							):



						"""simple docstring"""




						yield from (item.key for item in self._buckets if item)






			def __repr__(      self						:    int							):



						"""simple docstring"""




						__snake_case			=		" ,".join(
						    f'''{item.key}: {item.val}''' for item in self._buckets if item							)
						return f'''HashMap({val_string})'''









"""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	:  Dict            =     get_logger(__name__)


A	:  Dict            =     R"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"
class     _UpperCamelCase    :
							'''simple docstring'''







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







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







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  **__a      ):
										for processor in self:
													__lowerCAmelCase							       =     inspect.signature(processor.__call__      ).parameters
													if len(__a      ) > 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."      )
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a       ,  **__a      )
													else:
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a      )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

										__lowerCAmelCase							       =     temperature



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores / self.temperature
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) 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(__a       ,  __a      ) 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}"      )

										__lowerCAmelCase							       =     top_p
										__lowerCAmelCase							       =     filter_value
										__lowerCAmelCase							       =     min_tokens_to_keep



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  scores.shape[-1]      )

										__lowerCAmelCase							       =     jnp.full_like(__a       ,  self.filter_value      )
										__lowerCAmelCase							       =     jax.nn.softmax(__a       ,  axis=-1      ).cumsum(axis=-1      )
										__lowerCAmelCase							       =     cumulative_probs < self.top_p

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

										# min tokens to keep
										__lowerCAmelCase							       =     score_mask.at[:, : self.min_tokens_to_keep].set(__a      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jax.lax.sort_key_val(__a       ,  __a      )[-1]

										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

										__lowerCAmelCase							       =     max(__a       ,  __a      )
										__lowerCAmelCase							       =     filter_value



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     scores.shape
										__lowerCAmelCase							       =     jnp.full(batch_size * vocab_size       ,  self.filter_value      )

										__lowerCAmelCase							       =     min(self.top_k       ,  scores.shape[-1]      )  # Safety check
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  __a      )
										__lowerCAmelCase							       =     jnp.broadcast_to((jnp.arange(__a      ) * vocab_size)[:, None]       ,  (batch_size, topk)      ).flatten()
										__lowerCAmelCase							       =     topk_scores.flatten()
										__lowerCAmelCase							       =     topk_indices.flatten() + shift

										__lowerCAmelCase							       =     next_scores_flat.at[topk_indices_flat].set(__a      )
										__lowerCAmelCase							       =     next_scores_flat.reshape(__a       ,  __a      )
										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     bos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.bos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     max_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.max_length + 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.eos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







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

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

										__lowerCAmelCase							       =     min_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# create boolean flag to decide if min length penalty should be applied
										__lowerCAmelCase							       =     1 - jnp.clip(cur_len - self.min_length       ,  0       ,  1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     begin_index



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.begin_index      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     dict(__a      )
										# 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.
										__lowerCAmelCase							       =     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:
																__lowerCAmelCase							       =     force_token_array.at[index].set(__a      )
										__lowerCAmelCase							       =     jnp.intaa(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										def _force_token(__a      ):
													__lowerCAmelCase							       =     scores.shape[0]
													__lowerCAmelCase							       =     self.force_token_array[generation_idx]

													__lowerCAmelCase							       =     jnp.ones_like(__a       ,  dtype=scores.dtype      ) * -float("inf"      )
													__lowerCAmelCase							       =     jnp.zeros((batch_size, 1)       ,  dtype=scores.dtype      )
													__lowerCAmelCase							       =     lax.dynamic_update_slice(__a       ,  __a       ,  (0, current_token)      )
													return new_scores

										__lowerCAmelCase							       =     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(__a      )       ,  lambda: scores       ,  )       ,  )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     generate_config.eos_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id + 1

										__lowerCAmelCase							       =     decoder_input_length + 1

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



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										__lowerCAmelCase							       =     scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

										def handle_pairs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) >= 1       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __a       ,  )

													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) < 2       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin       ,  __a       ,  __a       ,  )

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

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										__lowerCAmelCase							       =     jnp.where(cur_len == self.begin_index       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jnp.where(
										    self.max_initial_timestamp_index is not None       ,  True and apply_max_initial_timestamp       ,  __a       ,  )

										__lowerCAmelCase							       =     self.timestamp_begin + self.max_initial_timestamp_index

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

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

										def handle_cumulative_probs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,  axis=-1      )
													__lowerCAmelCase							       =     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"      )      )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										return scores




