



'''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__  :	List[str]								=	get_logger(__name__)


a__  :	str								=	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__     :



 @add_start_docstrings(SCREAMING_SNAKE_CASE_	)
 def __call__(     self						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  raise NotImplementedError(
      f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)


class 				UpperCAmelCase__     :



 @add_start_docstrings(SCREAMING_SNAKE_CASE_	)
 def __call__(     self						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  raise NotImplementedError(
      f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 @add_start_docstrings(SCREAMING_SNAKE_CASE_	)
 def __call__(     self						,		lowercase						,		lowercase						,		lowercase						,		**lowercase	)     ->							jnp.ndarray:
  for processor in self:
   __UpperCamelCase 					=     inspect.signature(processor.__call__	).parameters
   if len(SCREAMING_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."	)
    __UpperCamelCase 					=     processor(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		**SCREAMING_SNAKE_CASE_	)
   else:
    __UpperCamelCase 					=     processor(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	)
  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase	)     ->							List[Any]:
  if not isinstance(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	) or not (temperature > 0):
   raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"	)

  __UpperCamelCase 					=     temperature




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  __UpperCamelCase 					=     scores / self.temperature
  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase						,		lowercase = -float("""Inf"""	)						,		lowercase = 1	)     ->							Union[str, Any]:
  if not isinstance(SCREAMING_SNAKE_CASE_						,		SCREAMING_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(SCREAMING_SNAKE_CASE_						,		SCREAMING_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}"	)

  __UpperCamelCase 					=     top_p
  __UpperCamelCase 					=     filter_value
  __UpperCamelCase 					=     min_tokens_to_keep




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  __UpperCamelCase 					=     lax.top_k(SCREAMING_SNAKE_CASE_						,		scores.shape[-1]	)

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

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

  __UpperCamelCase 					=     jnp.where(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	)
  __UpperCamelCase 					=     jax.lax.sort_key_val(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	)[-1]

  return next_scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



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

  __UpperCamelCase 					=     max(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	)
  __UpperCamelCase 					=     filter_value




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  __UpperCamelCase 					=     scores.shape
  __UpperCamelCase 					=     jnp.full(batch_size * vocab_size						,		self.filter_value	)

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


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase	)     ->							Union[str, Any]:
  __UpperCamelCase 					=     bos_token_id




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  __UpperCamelCase 					=     jnp.full(scores.shape						,		-float("""inf"""	)	)

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

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

  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase						,		lowercase	)     ->							Dict:
  __UpperCamelCase 					=     max_length
  __UpperCamelCase 					=     eos_token_id




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  __UpperCamelCase 					=     jnp.full(scores.shape						,		-float("""inf"""	)	)

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

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

  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase						,		lowercase	)     ->							List[str]:
  if not isinstance(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	) or min_length < 0:
   raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}"	)

  if not isinstance(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	) 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						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  # create boolean flag to decide if min length penalty should be applied
  __UpperCamelCase 					=     1 - jnp.clip(cur_len - self.min_length						,		0						,		1	)

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

  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase						,		lowercase	)     ->							Dict:
  __UpperCamelCase 					=     list(SCREAMING_SNAKE_CASE_	)
  __UpperCamelCase 					=     begin_index




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							Any:
  __UpperCamelCase 					=     1 - jnp.bool_(cur_len - self.begin_index	)

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

  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase	)     ->							List[Any]:
  __UpperCamelCase 					=     list(SCREAMING_SNAKE_CASE_	)




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  __UpperCamelCase 					=     scores.at[..., self.suppress_tokens].set(-float("""inf"""	)	)

  return scores


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase	)     ->							Union[str, Any]:
  __UpperCamelCase 					=     dict(SCREAMING_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.
  __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(SCREAMING_SNAKE_CASE_	)
  __UpperCamelCase 					=     jnp.intaa(SCREAMING_SNAKE_CASE_	)




 def __call__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							jnp.ndarray:
  def _force_token(lowercase	):
   __UpperCamelCase 					=     scores.shape[0]
   __UpperCamelCase 					=     self.force_token_array[generation_idx]

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


class 				UpperCAmelCase__     (			_UpperCAmelCase):



 def __init__(     self						,		lowercase						,		lowercase						,		lowercase	)     ->							Any:
  __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(SCREAMING_SNAKE_CASE_						,		"""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						,		lowercase						,		lowercase						,		lowercase	)     ->							Any:
  # suppress <|notimestamps|> which is handled by without_timestamps
  __UpperCamelCase 					=     scores.at[:, self.no_timestamps_token_id].set(-float("""inf"""	)	)

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

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

   return jnp.where(
       SCREAMING_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"""	)	)						,		)						,		SCREAMING_SNAKE_CASE_						,		)

  __UpperCamelCase 					=     jax.vmap(SCREAMING_SNAKE_CASE_	)(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	)

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

  __UpperCamelCase 					=     self.timestamp_begin + self.max_initial_timestamp_index

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

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

  def handle_cumulative_probs(lowercase						,		lowercase	):
   __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"""	)	)						,		SCREAMING_SNAKE_CASE_						,		)

  __UpperCamelCase 					=     jax.vmap(SCREAMING_SNAKE_CASE_	)(SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_	)

  return scores









'''simple docstring'''



A__		:					Optional[int]												=  """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				bytes							)     ->		bytes:
     # Make sure the supplied data is a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		F'a bytes-like object is required, not \'{data.__class__.__name__}\''
          raise TypeError(UpperCAmelCase_							)

     __lowerCamelCase   :	Optional[int]								=		''.join(bin(UpperCAmelCase_							)[2:].zfill(8							) for byte in data							)

     __lowerCamelCase   :	Any								=		len(UpperCAmelCase_							) % 6 != 0

     if padding_needed:
          # The padding that will be added later
          __lowerCamelCase   :	int								=		B'=' * ((6 - len(UpperCAmelCase_							) % 6) // 2)

          # Append binary_stream with arbitrary binary digits (0's by default) to make its
          # length a multiple of 6.
          binary_stream += "0" * (6 - len(UpperCAmelCase_							) % 6)
     else:
          __lowerCamelCase   :	str								=		B''

     # Encode every 6 binary digits to their corresponding Base64 character
     return (
         "".join(
             B64_CHARSET[int(binary_stream[index : index + 6]      ,    2							)]
             for index in range(0      ,    len(UpperCAmelCase_							)      ,    6							)							).encode()
         + padding
     )



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str							)     ->		bytes:
     # Make sure encoded_data is either a string or a bytes-like object
     if not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							) and not isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          __lowerCamelCase   :	Dict								=		(
              'argument should be a bytes-like object or ASCII string, '
              F'not \'{encoded_data.__class__.__name__}\''
          )
          raise TypeError(UpperCAmelCase_							)

     # In case encoded_data is a bytes-like object, make sure it contains only
     # ASCII characters so we convert it to a string object
     if isinstance(UpperCAmelCase_      ,    UpperCAmelCase_							):
          try:
               __lowerCamelCase   :	int								=		encoded_data.decode('utf-8'							)
          except UnicodeDecodeError:
               raise ValueError('base64 encoded data should only contain ASCII characters'							)

     __lowerCamelCase   :	Union[str, Any]								=		encoded_data.count('='							)

     # Check if the encoded string contains non base64 characters
     if padding:
          assert all(
              char in B64_CHARSET for char in encoded_data[:-padding]							), "Invalid base64 character(s) found."
     else:
          assert all(
              char in B64_CHARSET for char in encoded_data							), "Invalid base64 character(s) found."

     # Check the padding
     assert len(UpperCAmelCase_							) % 4 == 0 and padding < 3, "Incorrect padding"

     if padding:
          # Remove padding if there is one
          __lowerCamelCase   :	Any								=		encoded_data[:-padding]

          __lowerCamelCase   :	Optional[Any]								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)[: -padding * 2]
     else:
          __lowerCamelCase   :	Any								=		''.join(
              bin(B64_CHARSET.index(UpperCAmelCase_							)							)[2:].zfill(6							) for char in encoded_data							)

     __lowerCamelCase   :	str								=		[
         int(binary_stream[index : index + 8]      ,    2							)
         for index in range(0      ,    len(UpperCAmelCase_							)      ,    8							)
     ]

     return bytes(UpperCAmelCase_							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






