



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


_lowercase: Any      			=		get_logger(__name__)


_lowercase: Tuple      			=		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 	_lowercase							:





	"""simple docstring"""




	@add_start_docstrings(lowerCamelCase_		)
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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




class 	_lowercase							:





	"""simple docstring"""




	@add_start_docstrings(lowerCamelCase_		)
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	@add_start_docstrings(lowerCamelCase_		)
	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

						for processor in self:
											a					     =   inspect.signature(processor.__call__		).parameters
											if len(lowerCamelCase_		) > 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(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       **lowerCamelCase_		)
											else:
																a					     =   processor(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

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

						a					     =   temperature




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   scores / self.temperature
						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_ = -float("Inf"		)		,       lowerCamelCase_ = 1		):
						"""simple docstring"""

						if not isinstance(lowerCamelCase_		,       lowerCamelCase_		) 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(lowerCamelCase_		,       lowerCamelCase_		) 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		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a     ,				a					     =   lax.top_k(lowerCamelCase_		,       scores.shape[-1]		)

						a					     =   jnp.full_like(lowerCamelCase_		,       self.filter_value		)
						a					     =   jax.nn.softmax(lowerCamelCase_		,       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(lowerCamelCase_		,       1		)
						score_mask |= score_mask.at[:, 0].set(lowerCamelCase_		)

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

						a					     =   jnp.where(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   jax.lax.sort_key_val(lowerCamelCase_		,       lowerCamelCase_		)[-1]

						return next_scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_ = -float("Inf"		)		,       lowerCamelCase_ = 1		):
						"""simple docstring"""

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

						a					     =   max(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   filter_value




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						a					     =   min(self.top_k		,       scores.shape[-1]		)  # Safety check
						a     ,				a					     =   lax.top_k(lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   jnp.broadcast_to((jnp.arange(lowerCamelCase_		) * 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(lowerCamelCase_		)
						a					     =   next_scores_flat.reshape(lowerCamelCase_		,       lowerCamelCase_		)
						return next_scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   bos_token_id




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

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

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

						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   max_length
						a					     =   eos_token_id




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

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

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

						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						if not isinstance(lowerCamelCase_		,       lowerCamelCase_		) 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		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

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

						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   list(lowerCamelCase_		)
						a					     =   begin_index




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

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

						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   list(lowerCamelCase_		)




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   dict(lowerCamelCase_		)
						# 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(lowerCamelCase_		)
						a					     =   jnp.intaa(lowerCamelCase_		)




	def __call__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						def _force_token(lowerCamelCase_		):
											a					     =   scores.shape[0]
											a					     =   self.force_token_array[generation_idx]

											a					     =   jnp.ones_like(lowerCamelCase_		,       dtype=scores.dtype		) * -float("inf"		)
											a					     =   jnp.zeros((batch_size, 1)		,       dtype=scores.dtype		)
											a					     =   lax.dynamic_update_slice(lowerCamelCase_		,       lowerCamelCase_		,       (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(lowerCamelCase_		)		,       lambda: scores		,       )		,       )
						return scores




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""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(lowerCamelCase_		,       "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		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

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

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

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

						a					     =   jax.vmap(lowerCamelCase_		)(lowerCamelCase_		,       lowerCamelCase_		)

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

						a					     =   self.timestamp_begin + self.max_initial_timestamp_index

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

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

						def handle_cumulative_probs(lowerCamelCase_		,       lowerCamelCase_		):
											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"		)		)		,       lowerCamelCase_		,       )

						a					     =   jax.vmap(lowerCamelCase_		)(lowerCamelCase_		,       lowerCamelCase_		)

						return scores





import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
