
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


lowerCamelCase_						     =					get_logger(__name__)


lowerCamelCase_						     =					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      __A:

  """simple docstring"""







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






class      __A:

  """simple docstring"""







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






class      __A(    __lowerCamelCase			):

  """simple docstring"""







  @add_start_docstrings(SCREAMING_SNAKE_CASE_						)
  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    scores / self.temperature
       return scores






class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = -float("""Inf"""						)	,      SCREAMING_SNAKE_CASE_ = 1						):
       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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__		,	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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = -float("""Inf"""						)	,      SCREAMING_SNAKE_CASE_ = 1						):
       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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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(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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    bos_token_id







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    max_length
       UpperCamelCase__				=    eos_token_id







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       # 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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    list(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    begin_index







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       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      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    list(SCREAMING_SNAKE_CASE_						)







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    scores.at[..., self.suppress_tokens].set(-float("""inf"""						)						)

       return scores






class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       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	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       def _force_token(SCREAMING_SNAKE_CASE_						):
            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      __A(    __lowerCamelCase			):

  """simple docstring"""







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

       def handle_pairs(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

    lowerCamelCase_						     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





