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


UpperCamelCase__											=					get_logger(__name__)


UpperCamelCase__											=					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(__UpperCAmelCase		)
   def __call__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray		)						->				jnp.ndarray:
         """simple docstring"""



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





class      A							:


   @add_start_docstrings(__UpperCAmelCase		)
   def __call__(self     :						Tuple       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray		)						->				jnp.ndarray:
         """simple docstring"""



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





class      A							(				UpperCAmelCase_     ):


   @add_start_docstrings(__UpperCAmelCase		)
   def __call__(self     :						Tuple       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int       ,  **__UpperCAmelCase     :						str		)						->				jnp.ndarray:
         """simple docstring"""



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





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						float		)						->				Union[str, Any]:
         """simple docstring"""



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

         UpperCAmelCase__								=  temperature





   def __call__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  scores / self.temperature
         return scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						float       ,  __UpperCAmelCase     :						float = -float("Inf"		)       ,  __UpperCAmelCase     :						int = 1		)						->				str:
         """simple docstring"""



         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) 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(__UpperCAmelCase       ,  __UpperCAmelCase		) 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     :						Any       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



         UpperCAmelCase__		,      UpperCAmelCase__								=  lax.top_k(__UpperCAmelCase       ,  scores.shape[-1]		)

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

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

         UpperCAmelCase__								=  jnp.where(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  jax.lax.sort_key_val(__UpperCAmelCase       ,  __UpperCAmelCase		)[-1]

         return next_scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						float = -float("Inf"		)       ,  __UpperCAmelCase     :						int = 1		)						->				str:
         """simple docstring"""



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

         UpperCAmelCase__								=  max(__UpperCAmelCase       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  filter_value





   def __call__(self     :						Any       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """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(__UpperCAmelCase       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  jnp.broadcast_to((jnp.arange(__UpperCAmelCase		) * 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(__UpperCAmelCase		)
         UpperCAmelCase__								=  next_scores_flat.reshape(__UpperCAmelCase       ,  __UpperCAmelCase		)
         return next_scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						int		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  bos_token_id





   def __call__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



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

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

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

         return scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  max_length
         UpperCAmelCase__								=  eos_token_id





   def __call__(self     :						Optional[int]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



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

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

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

         return scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Optional[int]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				Tuple:
         """simple docstring"""



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

         if not isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) 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     :						Optional[int]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



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

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

         return scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Tuple		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  list(__UpperCAmelCase		)
         UpperCAmelCase__								=  begin_index





   def __call__(self     :						Any       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



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

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

         return scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						list		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  list(__UpperCAmelCase		)





   def __call__(self     :						List[str]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



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

         return scores





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						List[str]		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  dict(__UpperCAmelCase		)
         # 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(__UpperCAmelCase		)
         UpperCAmelCase__								=  jnp.intaa(__UpperCAmelCase		)





   def __call__(self     :						List[str]       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						jnp.ndarray       ,  __UpperCAmelCase     :						int		)						->				jnp.ndarray:
         """simple docstring"""



         def _force_token(__UpperCAmelCase     :						List[Any]		):
               UpperCAmelCase__								=  scores.shape[0]
               UpperCAmelCase__								=  self.force_token_array[generation_idx]

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





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						int		)						->				Optional[int]:
         """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(__UpperCAmelCase       ,  "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     :						int       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						str		)						->				Union[str, Any]:
         """simple docstring"""



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

         def handle_pairs(__UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Optional[int]		):
               UpperCAmelCase__								=  jnp.where((cur_len - self.begin_index) >= 1       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
               UpperCAmelCase__								=  jnp.where(
                   input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __UpperCAmelCase       ,  )

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

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

         UpperCAmelCase__								=  jax.vmap(__UpperCAmelCase		)(__UpperCAmelCase       ,  __UpperCAmelCase		)

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

         UpperCAmelCase__								=  self.timestamp_begin + self.max_initial_timestamp_index

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

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

         def handle_cumulative_probs(__UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Any		):
               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"		)		)       ,  __UpperCAmelCase       ,  )

         UpperCAmelCase__								=  jax.vmap(__UpperCAmelCase		)(__UpperCAmelCase       ,  __UpperCAmelCase		)

         return scores




from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__											=					[
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed
            elif pkg == "accelerate":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_accelerate_available

                  # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
                  # Transformers with PyTorch
                  if not is_accelerate_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')




def    lowerCAmelCase_ (     __A,  __A=None							)     ->				Dict:







      '''simple docstring'''


      require_version(deps[pkg],  __A							)



