'''simple docstring'''






import pickle

import numpy as np
from matplotlib import pyplot as plt




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_=0.2   ,    snake_case_=0.2					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= bp_numa
    __UpperCAmelCase:					Optional[int]		= bp_numa
    __UpperCAmelCase:					List[str]		= bp_numa
    __UpperCAmelCase:					Union[str, Any]		= conva_get[:2]
    __UpperCAmelCase:					Tuple		= conva_get[2]
    __UpperCAmelCase:					Any		= size_pa
    __UpperCAmelCase:					List[Any]		= rate_w
    __UpperCAmelCase:					int		= rate_t
    __UpperCAmelCase:					str		= [
        np.mat(-1 * np.random.rand(self.conva[0]   ,    self.conva[0]					) + 0.5					)
        for i in range(self.conva[1]					)
    ]
    __UpperCAmelCase:					Optional[Any]		= np.mat(-1 * np.random.rand(self.num_bpa   ,    self.num_bpa					) + 0.5					)
    __UpperCAmelCase:					int		= np.mat(-1 * np.random.rand(self.num_bpa   ,    self.num_bpa					) + 0.5					)
    __UpperCAmelCase:					List[Any]		= -2 * np.random.rand(self.conva[1]					) + 1
    __UpperCAmelCase:					Optional[int]		= -2 * np.random.rand(self.num_bpa					) + 1
    __UpperCAmelCase:					Union[str, Any]		= -2 * np.random.rand(self.num_bpa					) + 1







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= {
        """num_bp1""": self.num_bpa,
        """num_bp2""": self.num_bpa,
        """num_bp3""": self.num_bpa,
        """conv1""": self.conva,
        """step_conv1""": self.step_conva,
        """size_pooling1""": self.size_poolinga,
        """rate_weight""": self.rate_weight,
        """rate_thre""": self.rate_thre,
        """w_conv1""": self.w_conva,
        """wkj""": self.wkj,
        """vji""": self.vji,
        """thre_conv1""": self.thre_conva,
        """thre_bp2""": self.thre_bpa,
        """thre_bp3""": self.thre_bpa,
    }
    with open(__lowercase   ,    """wb"""					) as f:
      pickle.dump(__lowercase   ,    __lowercase					)

    print(F'''Model saved： {save_path}'''					)







  @classmethod
  def    lowercase_  (							cls   ,    snake_case_					):

    '''simple docstring'''







    with open(__lowercase   ,    """rb"""					) as f:
      __UpperCAmelCase:					str		= pickle.load(__lowercase					)  # noqa: S301

    __UpperCAmelCase:					List[str]		= model_dic.get("""conv1"""					)
    conv_get.append(model_dic.get("""step_conv1"""					)					)
    __UpperCAmelCase:					str		= model_dic.get("""size_pooling1"""					)
    __UpperCAmelCase:					str		= model_dic.get("""num_bp1"""					)
    __UpperCAmelCase:					Optional[int]		= model_dic.get("""num_bp2"""					)
    __UpperCAmelCase:					List[str]		= model_dic.get("""num_bp3"""					)
    __UpperCAmelCase:					Optional[int]		= model_dic.get("""rate_weight"""					)
    __UpperCAmelCase:					List[Any]		= model_dic.get("""rate_thre"""					)
    # create model instance
    __UpperCAmelCase:					Tuple		= CNN(__lowercase   ,    __lowercase   ,    __lowercase   ,    __lowercase   ,    __lowercase   ,    __lowercase   ,    __lowercase					)
    # modify model parameter
    __UpperCAmelCase:					Any		= model_dic.get("""w_conv1"""					)
    __UpperCAmelCase:					Optional[Any]		= model_dic.get("""wkj"""					)
    __UpperCAmelCase:					Dict		= model_dic.get("""vji"""					)
    __UpperCAmelCase:					str		= model_dic.get("""thre_conv1"""					)
    __UpperCAmelCase:					Any		= model_dic.get("""thre_bp2"""					)
    __UpperCAmelCase:					Optional[int]		= model_dic.get("""thre_bp3"""					)
    return conv_ins







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return 1 / (1 + np.exp(-1 * x					))







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    return round(__lowercase   ,    3					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= convs[0]
    __UpperCAmelCase:					Dict		= convs[1]
    __UpperCAmelCase:					Tuple		= np.shape(__lowercase					)[0]
    # get the data slice of original image data, data_focus
    __UpperCAmelCase:					List[str]		= []
    for i_focus in range(0   ,    size_data - size_conv + 1   ,    __lowercase					):
      for j_focus in range(0   ,    size_data - size_conv + 1   ,    __lowercase					):
        __UpperCAmelCase:					Optional[int]		= data[
            i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
        ]
        data_focus.append(__lowercase					)
        # calculate the feature map of every single kernel, and saved as list of matrix
    __UpperCAmelCase:					Optional[Any]		= []
    __UpperCAmelCase:					Optional[Any]		= int((size_data - size_conv) / conv_step + 1					)
    for i_map in range(__lowercase					):
      __UpperCAmelCase:					List[str]		= []
      for i_focus in range(len(__lowercase					)					):
        __UpperCAmelCase:					Optional[Any]		= (
            np.sum(np.multiply(data_focus[i_focus]   ,    w_convs[i_map]					)					)
            - thre_convs[i_map]
        )
        featuremap.append(self.sig(__lowercase					)					)
      __UpperCAmelCase:					Any		= np.asmatrix(__lowercase					).reshape(
          __lowercase   ,    __lowercase					)
      data_featuremap.append(__lowercase					)

    # expanding the data slice to One dimenssion
    __UpperCAmelCase:					Optional[int]		= []
    for each_focus in data_focus:
      focusa_list.extend(self.Expand_Mat(__lowercase					)					)
    __UpperCAmelCase:					List[Any]		= np.asarray(__lowercase					)
    return focus_list, data_featuremap







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_="average_pool"					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= len(featuremaps[0]					)
    __UpperCAmelCase:					str		= int(size_map / size_pooling					)
    __UpperCAmelCase:					Tuple		= []
    for i_map in range(len(__lowercase					)					):
      __UpperCAmelCase:					Optional[int]		= featuremaps[i_map]
      __UpperCAmelCase:					Any		= []
      for i_focus in range(0   ,    __lowercase   ,    __lowercase					):
        for j_focus in range(0   ,    __lowercase   ,    __lowercase					):
          __UpperCAmelCase:					Tuple		= feature_map[
              i_focus : i_focus + size_pooling,
              j_focus : j_focus + size_pooling,
          ]
          if pooling_type == "average_pool":
            # average pooling
            map_pooled.append(np.average(__lowercase					)					)
          elif pooling_type == "max_pooling":
            # max pooling
            map_pooled.append(np.max(__lowercase					)					)
      __UpperCAmelCase:					Tuple		= np.asmatrix(__lowercase					).reshape(__lowercase   ,    __lowercase					)
      featuremap_pooled.append(__lowercase					)
    return featuremap_pooled







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= []
    for i in range(len(__lowercase					)					):
      __UpperCAmelCase:					Optional[Any]		= np.shape(data[i]					)
      __UpperCAmelCase:					Any		= data[i].reshape(1   ,    shapes[0] * shapes[1]					)
      __UpperCAmelCase:					Union[str, Any]		= data_listed.getA().tolist()[0]
      data_expanded.extend(__lowercase					)
    __UpperCAmelCase:					Dict		= np.asarray(__lowercase					)
    return data_expanded







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= np.asarray(__lowercase					)
    __UpperCAmelCase:					str		= np.shape(__lowercase					)
    __UpperCAmelCase:					List[str]		= data_mat.reshape(1   ,    shapes[0] * shapes[1]					)
    return data_expanded







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= []
    __UpperCAmelCase:					Any		= 0
    for i_map in range(__lowercase					):
      __UpperCAmelCase:					List[Any]		= np.ones((size_map, size_map)					)
      for i in range(0   ,    __lowercase   ,    __lowercase					):
        for j in range(0   ,    __lowercase   ,    __lowercase					):
          __UpperCAmelCase:					List[Any]		= pd_pool[
              i_pool
          ]
          __UpperCAmelCase:					int		= i_pool + 1
      __UpperCAmelCase:					Optional[int]		= np.multiply(
          __lowercase   ,    np.multiply(out_map[i_map]   ,    (1 - out_map[i_map])					)					)
      pd_all.append(__lowercase					)
    return pd_all







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_=bool					):

    '''simple docstring'''







    print("""----------------------Start Training-------------------------"""					)
    print((""" - - Shape: Train_Data  """, np.shape(__lowercase					))					)
    print((""" - - Shape: Teach_Data  """, np.shape(__lowercase					))					)
    __UpperCAmelCase:					int		= 0
    __UpperCAmelCase:					List[str]		= []
    __UpperCAmelCase:					Tuple		= 1_0000
    while rp < n_repeat and mse >= error_accuracy:
      __UpperCAmelCase:					int		= 0
      print(F'''-------------Learning Time {rp}--------------'''					)
      for p in range(len(__lowercase					)					):
        # print('------------Learning Image: %d--------------'%p)
        __UpperCAmelCase:					Optional[Any]		= np.asmatrix(datas_train[p]					)
        __UpperCAmelCase:					Optional[int]		= np.asarray(datas_teach[p]					)
        __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= self.convolute(
            __lowercase   ,    self.conva   ,    self.w_conva   ,    self.thre_conva   ,    conv_step=self.step_conva   ,    )
        __UpperCAmelCase:					int		= self.pooling(__lowercase   ,    self.size_poolinga					)
        __UpperCAmelCase:					Tuple		= np.shape(__lowercase					)
        __UpperCAmelCase:					Union[str, Any]		= self._expand(__lowercase					)
        __UpperCAmelCase:					List[Any]		= data_bp_input

        __UpperCAmelCase:					List[Any]		= np.dot(__lowercase   ,    self.vji.T					) - self.thre_bpa
        __UpperCAmelCase:					Dict		= self.sig(__lowercase					)
        __UpperCAmelCase:					Optional[Any]		= np.dot(__lowercase   ,    self.wkj.T					) - self.thre_bpa
        __UpperCAmelCase:					List[str]		= self.sig(__lowercase					)

        # --------------Model Leaning ------------------------
        # calculate error and gradient---------------
        __UpperCAmelCase:					str		= np.multiply(
            (data_teach - bp_outa)   ,    np.multiply(__lowercase   ,    (1 - bp_outa)					)					)
        __UpperCAmelCase:					Dict		= np.multiply(
            np.dot(__lowercase   ,    self.wkj					)   ,    np.multiply(__lowercase   ,    (1 - bp_outa)					)					)
        __UpperCAmelCase:					Union[str, Any]		= np.dot(__lowercase   ,    self.vji					)

        __UpperCAmelCase:					List[Any]		= pd_i_all / (self.size_poolinga * self.size_poolinga)
        __UpperCAmelCase:					List[Any]		= pd_conva_pooled.T.getA().tolist()
        __UpperCAmelCase:					List[str]		= self._calculate_gradient_from_pool(
            __lowercase   ,    __lowercase   ,    shape_featuremapa[0]   ,    shape_featuremapa[1]   ,    self.size_poolinga   ,    )
        # weight and threshold learning process---------
        # convolution layer
        for k_conv in range(self.conva[1]					):
          __UpperCAmelCase:					List[str]		= self._expand_mat(pd_conva_all[k_conv]					)
          __UpperCAmelCase:					Optional[Any]		= self.rate_weight * np.dot(__lowercase   ,    __lowercase					)

          __UpperCAmelCase:					int		= self.w_conva[k_conv] + delta_w.reshape(
              (self.conva[0], self.conva[0])					)

          __UpperCAmelCase:					List[str]		= (
              self.thre_conva[k_conv]
              - np.sum(pd_conva_all[k_conv]					) * self.rate_thre
          )
        # all connected layer
        __UpperCAmelCase:					List[Any]		= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
        __UpperCAmelCase:					Dict		= self.vji + pd_j_all.T * bp_outa * self.rate_weight
        __UpperCAmelCase:					Any		= self.thre_bpa - pd_k_all * self.rate_thre
        __UpperCAmelCase:					Optional[Any]		= self.thre_bpa - pd_j_all * self.rate_thre
        # calculate the sum error of all single image
        __UpperCAmelCase:					Union[str, Any]		= np.sum(abs(data_teach - bp_outa					)					)
        error_count += errors
        # print('   ----Teach      ',data_teach)
        # print('   ----BP_output  ',bp_out3)
      __UpperCAmelCase:					Any		= rp + 1
      __UpperCAmelCase:					Tuple		= error_count / patterns
      all_mse.append(__lowercase					)

    def draw_error():
      __UpperCAmelCase:					str		= [error_accuracy for i in range(int(n_repeat * 1.2					)					)]
      plt.plot(__lowercase   ,    """+-"""					)
      plt.plot(__lowercase   ,    """r--"""					)
      plt.xlabel("""Learning Times"""					)
      plt.ylabel("""All_mse"""					)
      plt.grid(__lowercase   ,    alpha=0.5					)
      plt.show()

    print("""------------------Training Complished---------------------"""					)
    print((""" - - Training epoch: """, rp, F'''     - - Mse: {mse:.6f}''')					)
    if draw_e:
      draw_error()
    return mse







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= []
    print("""-------------------Start Testing-------------------------"""					)
    print((""" - - Shape: Test_Data  """, np.shape(__lowercase					))					)
    for p in range(len(__lowercase					)					):
      __UpperCAmelCase:					Union[str, Any]		= np.asmatrix(datas_test[p]					)
      __UpperCAmelCase, __UpperCAmelCase:					int		= self.convolute(
          __lowercase   ,    self.conva   ,    self.w_conva   ,    self.thre_conva   ,    conv_step=self.step_conva   ,    )
      __UpperCAmelCase:					int		= self.pooling(__lowercase   ,    self.size_poolinga					)
      __UpperCAmelCase:					Dict		= self._expand(__lowercase					)

      __UpperCAmelCase:					Optional[int]		= data_bp_input
      __UpperCAmelCase:					Tuple		= bp_outa * self.vji.T - self.thre_bpa
      __UpperCAmelCase:					str		= self.sig(__lowercase					)
      __UpperCAmelCase:					Union[str, Any]		= bp_outa * self.wkj.T - self.thre_bpa
      __UpperCAmelCase:					List[Any]		= self.sig(__lowercase					)
      produce_out.extend(bp_outa.getA().tolist()					)
    __UpperCAmelCase:					Any		= [list(map(self.do_round   ,    __lowercase					)					) for each in produce_out]
    return np.asarray(__lowercase					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= np.asmatrix(__lowercase					)
    __UpperCAmelCase, __UpperCAmelCase:					Any		= self.convolute(
        __lowercase   ,    self.conva   ,    self.w_conva   ,    self.thre_conva   ,    conv_step=self.step_conva   ,    )
    __UpperCAmelCase:					int		= self.pooling(__lowercase   ,    self.size_poolinga					)

    return data_conveda, data_pooleda


if __name__ == "__main__":
    pass





'''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


SCREAMING_SNAKE_CASE_	   =			get_logger(__name__)


SCREAMING_SNAKE_CASE_	   =			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 :



  """simple docstring"""







  @add_start_docstrings(snake_case_					)
  def __call__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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




class    a :



  """simple docstring"""







  @add_start_docstrings(snake_case_					)
  def __call__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    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(snake_case_					)
  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    for processor in self:
      __UpperCAmelCase:					str		= inspect.signature(processor.__call__					).parameters
      if len(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:					List[Any]		= processor(snake_case_   ,    snake_case_   ,    snake_case_   ,    **snake_case_					)
      else:
        __UpperCAmelCase:					Optional[Any]		= processor(snake_case_   ,    snake_case_   ,    snake_case_					)
    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







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

    __UpperCAmelCase:					Any		= temperature







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= scores / self.temperature
    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_ = -float("""Inf"""					)   ,    snake_case_ = 1					):

    '''simple docstring'''







    if not isinstance(snake_case_   ,    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(snake_case_   ,    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:					int		= top_p
    __UpperCAmelCase:					str		= filter_value
    __UpperCAmelCase:					List[Any]		= min_tokens_to_keep







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= lax.top_k(snake_case_   ,    scores.shape[-1]					)

    __UpperCAmelCase:					Tuple		= jnp.full_like(snake_case_   ,    self.filter_value					)
    __UpperCAmelCase:					Optional[int]		= jax.nn.softmax(snake_case_   ,    axis=-1					).cumsum(axis=-1					)
    __UpperCAmelCase:					List[str]		= cumulative_probs < self.top_p

    # include the token that is higher than top_p as well
    __UpperCAmelCase:					str		= jnp.roll(snake_case_   ,    1					)
    score_mask |= score_mask.at[:, 0].set(snake_case_					)

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

    __UpperCAmelCase:					List[Any]		= jnp.where(snake_case_   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= jax.lax.sort_key_val(snake_case_   ,    snake_case_					)[-1]

    return next_scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_ = -float("""Inf"""					)   ,    snake_case_ = 1					):

    '''simple docstring'''







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

    __UpperCAmelCase:					Optional[Any]		= max(snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					List[str]		= filter_value







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= scores.shape
    __UpperCAmelCase:					List[Any]		= jnp.full(batch_size * vocab_size   ,    self.filter_value					)

    __UpperCAmelCase:					Any		= min(self.top_k   ,    scores.shape[-1]					)  # Safety check
    __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= lax.top_k(snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= jnp.broadcast_to((jnp.arange(snake_case_					) * vocab_size)[:, None]   ,    (batch_size, topk)					).flatten()
    __UpperCAmelCase:					List[Any]		= topk_scores.flatten()
    __UpperCAmelCase:					str		= topk_indices.flatten() + shift

    __UpperCAmelCase:					List[Any]		= next_scores_flat.at[topk_indices_flat].set(snake_case_					)
    __UpperCAmelCase:					Any		= next_scores_flat.reshape(snake_case_   ,    snake_case_					)
    return next_scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= bos_token_id







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= jnp.full(scores.shape   ,    -float("""inf"""					)					)

    __UpperCAmelCase:					Optional[Any]		= 1 - jnp.bool_(cur_len - 1					)

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

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= max_length
    __UpperCAmelCase:					List[str]		= eos_token_id







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= jnp.full(scores.shape   ,    -float("""inf"""					)					)

    __UpperCAmelCase:					List[str]		= 1 - jnp.bool_(cur_len - self.max_length + 1					)

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

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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

    if not isinstance(snake_case_   ,    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:					List[Any]		= min_length
    __UpperCAmelCase:					Optional[int]		= eos_token_id







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= 1 - jnp.clip(cur_len - self.min_length   ,    0   ,    1					)

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

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= list(snake_case_					)
    __UpperCAmelCase:					Optional[Any]		= begin_index







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= 1 - jnp.bool_(cur_len - self.begin_index					)

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

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= list(snake_case_					)







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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

    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= dict(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:					Optional[Any]		= 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:					Union[str, Any]		= force_token_array.at[index].set(snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= jnp.intaa(snake_case_					)







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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

      __UpperCAmelCase:					Union[str, Any]		= jnp.ones_like(snake_case_   ,    dtype=scores.dtype					) * -float("""inf"""					)
      __UpperCAmelCase:					List[Any]		= jnp.zeros((batch_size, 1)   ,    dtype=scores.dtype					)
      __UpperCAmelCase:					List[Any]		= lax.dynamic_update_slice(snake_case_   ,    snake_case_   ,    (0, current_token)					)
      return new_scores

    __UpperCAmelCase:					Optional[int]		= 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(snake_case_					)   ,    lambda: scores   ,    )   ,    )
    return scores




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= generate_config.eos_token_id
    __UpperCAmelCase:					Union[str, Any]		= generate_config.no_timestamps_token_id
    __UpperCAmelCase:					Tuple		= generate_config.no_timestamps_token_id + 1

    __UpperCAmelCase:					Any		= decoder_input_length + 1

    if generate_config.is_multilingual:
      # room for language token and task token
      self.begin_index += 2
    if hasattr(snake_case_   ,    """max_initial_timestamp_index"""					):
      __UpperCAmelCase:					Optional[int]		= generate_config.max_initial_timestamp_index
    else:
      __UpperCAmelCase:					List[str]		= model_config.vocab_size
    if self.max_initial_timestamp_index is None:
      __UpperCAmelCase:					Any		= model_config.vocab_size







  def __call__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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

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

      __UpperCAmelCase:					Union[str, Any]		= jnp.where((cur_len - self.begin_index) < 2   ,    snake_case_   ,    snake_case_					)
      __UpperCAmelCase:					List[str]		= jnp.where(
          input_ids_k[cur_len - 2] >= self.timestamp_begin   ,    snake_case_   ,    snake_case_   ,    )

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

    __UpperCAmelCase:					Dict		= jax.vmap(snake_case_					)(snake_case_   ,    snake_case_					)

    __UpperCAmelCase:					Dict		= jnp.where(cur_len == self.begin_index   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					List[Any]		= jnp.where(
        self.max_initial_timestamp_index is not None   ,    True and apply_max_initial_timestamp   ,    snake_case_   ,    )

    __UpperCAmelCase:					List[str]		= self.timestamp_begin + self.max_initial_timestamp_index

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

    # if sum of probability over timestamps is above any other token, sample timestamp
    __UpperCAmelCase:					Union[str, Any]		= jax.nn.log_softmax(snake_case_   ,    axis=-1					)

    def handle_cumulative_probs(snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					int		= jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,    axis=-1					)
      __UpperCAmelCase:					Tuple		= 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"""					)					)   ,    snake_case_   ,    )

    __UpperCAmelCase:					Tuple		= jax.vmap(snake_case_					)(snake_case_   ,    snake_case_					)

    return scores