

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


lowerCAmelCase__	   = get_logger(__name__)


lowerCAmelCase__	   = 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__						:
			"""simple docstring"""







			@add_start_docstrings(_A       )
			def __call__(    self      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






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

class 					a__						:
			"""simple docstring"""







			@add_start_docstrings(_A       )
			def __call__(    self      ,      lowercase      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






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

class 					a__						(					snake_case__			):
			"""simple docstring"""







			@add_start_docstrings(_A       )
			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      **lowercase       )  ->						str:
							'''simple docstring'''






							for processor in self:
											A__        =			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.'       )
															A__        =			processor(_A      ,      _A      ,      _A      ,      **_A       )
											else:
															A__        =			processor(_A      ,      _A      ,      _A       )
							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






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

							A__        =			temperature


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			scores / self.temperature
							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase = -float("Inf"       )      ,      lowercase = 1       )  ->						Optional[int]:
							'''simple docstring'''






							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}'       )

							A__        =			top_p
							A__        =			filter_value
							A__        =			min_tokens_to_keep


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			lax.top_k(_A      ,      scores.shape[-1]       )

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

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

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

							A__        =			jnp.where(_A      ,      _A      ,      _A       )
							A__        =			jax.lax.sort_key_val(_A      ,      _A       )[-1]

							return next_scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase = -float("Inf"       )      ,      lowercase = 1       )  ->						Optional[int]:
							'''simple docstring'''






							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}'       )

							A__        =			max(_A      ,      _A       )
							A__        =			filter_value


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			scores.shape
							A__        =			jnp.full(batch_size * vocab_size      ,      self.filter_value       )

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

							A__        =			next_scores_flat.at[topk_indices_flat].set(_A       )
							A__        =			next_scores_flat.reshape(_A      ,      _A       )
							return next_scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			bos_token_id


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Dict:
							'''simple docstring'''






							A__        =			jnp.full(scores.shape      ,      -float("inf"       )       )

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

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

							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			max_length
							A__        =			eos_token_id


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			jnp.full(scores.shape      ,      -float("inf"       )       )

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

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

							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Any:
							'''simple docstring'''






							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}'       )

							A__        =			min_length
							A__        =			eos_token_id


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			1 - jnp.clip(cur_len - self.min_length      ,      0      ,      1       )

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

							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			list(_A       )
							A__        =			begin_index


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			1 - jnp.bool_(cur_len - self.begin_index       )

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

							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			list(_A       )


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[int]:
							'''simple docstring'''






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

							return scores

class 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						Any:
							'''simple docstring'''






							A__        =			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.
							A__        =			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:
															A__        =			force_token_array.at[index].set(_A       )
							A__        =			jnp.intaa(_A       )


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						int:
							'''simple docstring'''






							def _force_token(lowercase       ):
											A__        =			scores.shape[0]
											A__        =			self.force_token_array[generation_idx]

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

							A__        =			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 					a__						(					snake_case__			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			generate_config.eos_token_id
							A__        =			generate_config.no_timestamps_token_id
							A__        =			generate_config.no_timestamps_token_id + 1

							A__        =			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"       ):
											A__        =			generate_config.max_initial_timestamp_index
							else:
											A__        =			model_config.vocab_size
							if self.max_initial_timestamp_index is None:
											A__        =			model_config.vocab_size


			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






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

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

											A__        =			jnp.where((cur_len - self.begin_index) < 2      ,      _A      ,      _A       )
											A__        =			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      ,      )

							A__        =			jax.vmap(_A       )(_A      ,      _A       )

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

							A__        =			self.timestamp_begin + self.max_initial_timestamp_index

							A__        =			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
							A__        =			jax.nn.log_softmax(_A      ,      axis=-1       )

							def handle_cumulative_probs(lowercase      ,      lowercase       ):
											A__        =			jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]      ,      axis=-1       )
											A__        =			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      ,      )

							A__        =			jax.vmap(_A       )(_A      ,      _A       )

							return scores




from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							list[str] | None = None      )			->      list[list[str]]:




				'''simple docstring'''





				A__        =			word_bank or []
				# create a table
				A__        =			len(SCREAMING_SNAKE_CASE_      ) + 1

				A__        =			[]
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								table.append([]      )
				# seed value
				A__        =			[[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(SCREAMING_SNAKE_CASE_      ):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(SCREAMING_SNAKE_CASE_      )] == word:
																				A__        =			[
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(SCREAMING_SNAKE_CASE_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(SCREAMING_SNAKE_CASE_      )]:
								combination.reverse()

				return table[len(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
	print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
	print(
	    all_construct(
	        """hexagonosaurus""",
	        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
	    )
	)

