




'''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


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    __lowercase						:







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





class    __lowercase						:







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





class    __lowercase						(  __magic_name__ ):







   @add_start_docstrings(UpperCamelCase      )
   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    jnp.ndarray:
     for processor in self:
       __a			=					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."      )
         __a			=					processor(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )
       else:
         __a			=					processor(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )
     return scores





class    __lowercase						(  __magic_name__ ):







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

     __a			=					temperature







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





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase = -float('Inf'      )      ,							UpperCamelCase = 1      )				->    Optional[Any]:
     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}"      )

     __a			=					top_p
     __a			=					filter_value
     __a			=					min_tokens_to_keep







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

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

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

     __a			=					jnp.where(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )
     __a			=					jax.lax.sort_key_val(UpperCamelCase      ,							UpperCamelCase      )[-1]

     return next_scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase = -float('Inf'      )      ,							UpperCamelCase = 1      )				->    Tuple:
     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}"      )

     __a			=					max(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					filter_value







   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    jnp.ndarray:
     __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(UpperCamelCase      ,							UpperCamelCase      )
     __a			=					jnp.broadcast_to((jnp.arange(UpperCamelCase      ) * 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(UpperCamelCase      )
     __a			=					next_scores_flat.reshape(UpperCamelCase      ,							UpperCamelCase      )
     return next_scores





class    __lowercase						(  __magic_name__ ):







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







   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    jnp.ndarray:
     __a			=					jnp.full(scores.shape      ,							-float('inf'      )      )

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

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

     return scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    List[Any]:
     __a			=					max_length
     __a			=					eos_token_id







   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    jnp.ndarray:
     __a			=					jnp.full(scores.shape      ,							-float('inf'      )      )

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

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

     return scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Dict:
     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}"      )

     __a			=					min_length
     __a			=					eos_token_id







   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    jnp.ndarray:
     # create boolean flag to decide if min length penalty should be applied
     __a			=					1 - jnp.clip(cur_len - self.min_length      ,							0      ,							1      )

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

     return scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Any:
     __a			=					list(UpperCamelCase      )
     __a			=					begin_index







   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Dict:
     __a			=					1 - jnp.bool_(cur_len - self.begin_index      )

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

     return scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      )				->    List[Any]:
     __a			=					list(UpperCamelCase      )







   def __call__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    jnp.ndarray:
     __a			=					scores.at[..., self.suppress_tokens].set(-float('inf'      )      )

     return scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     __a			=					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.
     __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(UpperCamelCase      )
     __a			=					jnp.intaa(UpperCamelCase      )







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

       __a			=					jnp.ones_like(UpperCamelCase      ,							dtype=scores.dtype      ) * -float('inf'      )
       __a			=					jnp.zeros((batch_size, 1)      ,							dtype=scores.dtype      )
       __a			=					lax.dynamic_update_slice(UpperCamelCase      ,							UpperCamelCase      ,							(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(UpperCamelCase      )      ,							lambda: scores      ,							)      ,							)
     return scores





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Union[str, Any]:
     __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(UpperCamelCase      ,							'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      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Dict:
     # suppress <|notimestamps|> which is handled by without_timestamps
     __a			=					scores.at[:, self.no_timestamps_token_id].set(-float('inf'      )      )

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

       __a			=					jnp.where((cur_len - self.begin_index) < 2      ,							UpperCamelCase      ,							UpperCamelCase      )
       __a			=					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      ,							)

     __a			=					jax.vmap(UpperCamelCase      )(UpperCamelCase      ,							UpperCamelCase      )

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

     __a			=					self.timestamp_begin + self.max_initial_timestamp_index

     __a			=					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
     __a			=					jax.nn.log_softmax(UpperCamelCase      ,							axis=-1      )

     def handle_cumulative_probs(UpperCamelCase      ,							UpperCamelCase      ):
       __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'      )      )      ,							UpperCamelCase      ,							)

     __a			=					jax.vmap(UpperCamelCase      )(UpperCamelCase      ,							UpperCamelCase      )

     return scores









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ 		=      {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      ["RoFormerTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoFormerForCausalLM",
      "RoFormerForMaskedLM",
      "RoFormerForMultipleChoice",
      "RoFormerForQuestionAnswering",
      "RoFormerForSequenceClassification",
      "RoFormerForTokenClassification",
      "RoFormerLayer",
      "RoFormerModel",
      "RoFormerPreTrainedModel",
      "load_tf_weights_in_roformer",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRoFormerForCausalLM",
      "TFRoFormerForMaskedLM",
      "TFRoFormerForMultipleChoice",
      "TFRoFormerForQuestionAnswering",
      "TFRoFormerForSequenceClassification",
      "TFRoFormerForTokenClassification",
      "TFRoFormerLayer",
      "TFRoFormerModel",
      "TFRoFormerPreTrainedModel",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlaxRoFormerForMaskedLM",
      "FlaxRoFormerForMultipleChoice",
      "FlaxRoFormerForQuestionAnswering",
      "FlaxRoFormerForSequenceClassification",
      "FlaxRoFormerForTokenClassification",
      "FlaxRoFormerModel",
      "FlaxRoFormerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_ 		=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



