





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				:    List[Any]       =   get_logger(__name__)


__A				:    List[Any]       =   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 	A_       :
	@add_start_docstrings(_A		)
	def __call__(    self							,					_A							,					_A		):






				'''simple docstring'''







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

class 	A_       :
	@add_start_docstrings(_A		)
	def __call__(    self							,					_A							,					_A		):






				'''simple docstring'''







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

class 	A_       (a_			):
	@add_start_docstrings(_A		)
	def __call__(    self							,					_A							,					_A							,					_A							,					**_A		):






				'''simple docstring'''







				for processor in self:
							UpperCAmelCase   		=				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."""		)
										UpperCAmelCase   		=				processor(_A							,					_A							,					_A							,					**_A		)
							else:
										UpperCAmelCase   		=				processor(_A							,					_A							,					_A		)
				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A		):






				'''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}"""		)

				UpperCAmelCase   		=				temperature







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				scores / self.temperature
				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A = -float('''Inf'''		)							,					_A = 1		):






				'''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}"""		)

				UpperCAmelCase   		=				top_p
				UpperCAmelCase   		=				filter_value
				UpperCAmelCase   		=				min_tokens_to_keep







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







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

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

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

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

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

				return next_scores

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A = -float('''Inf'''		)							,					_A = 1		):






				'''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}"""		)

				UpperCAmelCase   		=				max(_A							,					_A		)
				UpperCAmelCase   		=				filter_value







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







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

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

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

class 	A_       (a_			):
	def __init__(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				bos_token_id







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				jnp.full(scores.shape							,					-float('''inf'''		)		)

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

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

				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				max_length
				UpperCAmelCase   		=				eos_token_id







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				jnp.full(scores.shape							,					-float('''inf'''		)		)

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

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

				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A		):






				'''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}"""		)

				UpperCAmelCase   		=				min_length
				UpperCAmelCase   		=				eos_token_id







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







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

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

				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				list(_A		)
				UpperCAmelCase   		=				begin_index







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







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

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

				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				list(_A		)







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				scores.at[..., self.suppress_tokens].set(-float('''inf'''		)		)

				return scores

class 	A_       (a_			):
	def __init__(    self							,					_A		):






				'''simple docstring'''







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







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				def _force_token(_A		):
							UpperCAmelCase   		=				scores.shape[0]
							UpperCAmelCase   		=				self.force_token_array[generation_idx]

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

				UpperCAmelCase   		=				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_       (a_			):
	def __init__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				generate_config.eos_token_id
				UpperCAmelCase   		=				generate_config.no_timestamps_token_id
				UpperCAmelCase   		=				generate_config.no_timestamps_token_id + 1

				UpperCAmelCase   		=				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'''		):
							UpperCAmelCase   		=				generate_config.max_initial_timestamp_index
				else:
							UpperCAmelCase   		=				model_config.vocab_size
				if self.max_initial_timestamp_index is None:
							UpperCAmelCase   		=				model_config.vocab_size







	def __call__(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







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

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

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

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

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

				UpperCAmelCase   		=				self.timestamp_begin + self.max_initial_timestamp_index

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

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

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

				return scores










def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			exponent // 2			,			UpperCamelCase__ ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase__			,			exponent - 1			,			UpperCamelCase__ )) % modulo_value





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ = 1777			,			UpperCamelCase__ = 1855			,			UpperCamelCase__ = 8 )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				base
			for _ in range(1			,			UpperCamelCase__ ):
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			UpperCamelCase__			,			10**digits )

			return result


if __name__ == "__main__":
				print(F'{solution() = }')



