



'''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      :       int      			=      get_logger(__name__)


__UpperCamelCase      :       int      			=      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 							__SCREAMING_SNAKE_CASE       :




      @add_start_docstrings(a_      )
      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



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







class 							__SCREAMING_SNAKE_CASE       :




      @add_start_docstrings(a_      )
      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Dict:



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







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      @add_start_docstrings(a_      )
      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         for processor in self:
            __a				     =    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."""      )
               __a				     =    processor(a_ ,   a_ ,   a_ ,   **a_      )
            else:
               __a				     =    processor(a_ ,   a_ ,   a_      )
         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase      )							->List[str]:



         '''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}"""      )

         __a				     =    temperature






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         __a				     =    scores / self.temperature
         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase = -float('Inf'      ) ,   lowerCamelCase = 1      )							->List[Any]:



         '''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}"""      )

         __a				     =    top_p
         __a				     =    filter_value
         __a				     =    min_tokens_to_keep






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         __a				     =    lax.top_k(a_ ,   scores.shape[-1]      )

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

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

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

         return next_scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase = -float('Inf'      ) ,   lowerCamelCase = 1      )							->Tuple:



         '''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}"""      )

         __a				     =    max(a_ ,   a_      )
         __a				     =    filter_value






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    scores.shape
         __a				     =    jnp.full(batch_size * vocab_size ,   self.filter_value      )

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







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    bos_token_id






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    jnp.full(scores.shape ,   -float('inf'      )      )

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

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

         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         __a				     =    max_length
         __a				     =    eos_token_id






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         __a				     =    jnp.full(scores.shape ,   -float('inf'      )      )

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

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

         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''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}"""      )

         __a				     =    min_length
         __a				     =    eos_token_id






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         # 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(a_ ,   scores.at[:, self.eos_token_id].set(-float('inf'      )      ) ,   a_      )

         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         __a				     =    list(a_      )
         __a				     =    begin_index






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         __a				     =    1 - jnp.bool_(cur_len - self.begin_index      )

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

         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    list(a_      )






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Union[str, Any]:



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

         return scores







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         __a				     =    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.
         __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(a_      )
         __a				     =    jnp.intaa(a_      )






      def __call__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         def _force_token(lowerCamelCase      ):
            __a				     =    scores.shape[0]
            __a				     =    self.force_token_array[generation_idx]

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







class 							__SCREAMING_SNAKE_CASE       (							__lowerCamelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Dict:



         '''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(a_ ,   '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      )							->Optional[Any]:



         '''simple docstring'''
         # suppress <|notimestamps|> which is handled by without_timestamps
         __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 ,   a_ ,   a_      )
            __a				     =    jnp.where(
                input_ids_k[cur_len - 1] >= self.timestamp_begin ,   True and last_was_timestamp ,   a_ ,   )

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

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

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

         __a				     =    self.timestamp_begin + self.max_initial_timestamp_index

         __a				     =    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
         __a				     =    jax.nn.log_softmax(a_ ,   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'      )      ) ,   a_ ,   )

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

         return scores







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Union[str, Any]      			=      ["""Speech2TextTokenizer"""]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      ["""Speech2TextFeatureExtractor"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       List[Any]      			=      [
     """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFSpeech2TextForConditionalGeneration""",
     """TFSpeech2TextModel""",
     """TFSpeech2TextPreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Speech2TextForConditionalGeneration""",
     """Speech2TextModel""",
     """Speech2TextPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 __UpperCamelCase      :       List[str]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)