





'''simple docstring'''




import tensorflow as tf

from ...tf_utils import shape_list







class   A_	(  tf.keras.layers.Layer			):




 def __init__(					self       :    Dict	,			snake_case_       :    int	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    str	,			snake_case_       :    List[str]	,			snake_case_       :    Optional[int]=1	,			snake_case_       :    int=False	,			**snake_case_       :    Optional[Any]    ):
      super().__init__(**A_    )

      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   d_embed
      _UpperCAmelCase     							=   d_proj

      _UpperCAmelCase     							=   cutoffs + [vocab_size]
      _UpperCAmelCase     							=   [0] + self.cutoffs
      _UpperCAmelCase     							=   div_val

      _UpperCAmelCase     							=   self.cutoffs[0]
      _UpperCAmelCase     							=   len(self.cutoffs    ) - 1
      _UpperCAmelCase     							=   self.shortlist_size + self.n_clusters
      _UpperCAmelCase     							=   keep_order

      _UpperCAmelCase     							=   []
      _UpperCAmelCase     							=   []




 def      lowercase	(					self       :    str	,			snake_case_       :    Tuple    ):
      if self.n_clusters > 0:
           _UpperCAmelCase     							=   self.add_weight(
               shape=(self.n_clusters, self.d_embed)	,			initializer="zeros"	,			trainable=A_	,			name="cluster_weight"    )
           _UpperCAmelCase     							=   self.add_weight(
               shape=(self.n_clusters,)	,			initializer="zeros"	,			trainable=A_	,			name="cluster_bias"    )

      if self.div_val == 1:
           for i in range(len(self.cutoffs    )    ):
                if self.d_proj != self.d_embed:
                     _UpperCAmelCase     							=   self.add_weight(
                         shape=(self.d_embed, self.d_proj)	,			initializer="zeros"	,			trainable=A_	,			name=f'out_projs_._{i}'	,			)
                     self.out_projs.append(A_    )
                else:
                     self.out_projs.append(A_    )
                _UpperCAmelCase     							=   self.add_weight(
                    shape=(self.vocab_size, self.d_embed)	,			initializer="zeros"	,			trainable=A_	,			name=f'out_layers_._{i}_._weight'	,			)
                _UpperCAmelCase     							=   self.add_weight(
                    shape=(self.vocab_size,)	,			initializer="zeros"	,			trainable=A_	,			name=f'out_layers_._{i}_._bias'	,			)
                self.out_layers.append((weight, bias)    )
      else:
           for i in range(len(self.cutoffs    )    ):
                _UpperCAmelCase							,  _UpperCAmelCase     							=   self.cutoff_ends[i], self.cutoff_ends[i + 1]
                _UpperCAmelCase     							=   self.d_embed // (self.div_val**i)

                _UpperCAmelCase     							=   self.add_weight(
                    shape=(d_emb_i, self.d_proj)	,			initializer="zeros"	,			trainable=A_	,			name=f'out_projs_._{i}'    )
                self.out_projs.append(A_    )
                _UpperCAmelCase     							=   self.add_weight(
                    shape=(r_idx - l_idx, d_emb_i)	,			initializer="zeros"	,			trainable=A_	,			name=f'out_layers_._{i}_._weight'	,			)
                _UpperCAmelCase     							=   self.add_weight(
                    shape=(r_idx - l_idx,)	,			initializer="zeros"	,			trainable=A_	,			name=f'out_layers_._{i}_._bias'	,			)
                self.out_layers.append((weight, bias)    )
      super().build(A_    )




 @staticmethod
 def      lowercase	(					snake_case_       :    Tuple	,			snake_case_       :    Optional[Any]	,			snake_case_       :    List[Any]	,			snake_case_       :    Dict=None    ):
      _UpperCAmelCase     							=   x
      if proj is not None:
           _UpperCAmelCase     							=   tf.einsum("ibd,ed->ibe"	,			A_	,			A_    )
      return tf.einsum("ibd,nd->ibn"	,			A_	,			A_    ) + b




 @staticmethod
 def      lowercase	(					snake_case_       :    Union[str, Any]	,			snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   shape_list(A_    )
      _UpperCAmelCase     							=   tf.range(lp_size[0]	,			dtype=target.dtype    )
      _UpperCAmelCase     							=   tf.stack([r, target]	,			1    )
      return tf.gather_nd(A_	,			A_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    Dict=True	,			snake_case_       :    Union[str, Any]=False    ):
      _UpperCAmelCase     							=   0
      if self.n_clusters == 0:
           _UpperCAmelCase     							=   self._logit(A_	,			self.out_layers[0][0]	,			self.out_layers[0][1]	,			self.out_projs[0]    )
           if target is not None:
                _UpperCAmelCase     							=   tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A_	,			logits=A_    )
           _UpperCAmelCase     							=   tf.nn.log_softmax(A_	,			axis=-1    )
      else:
           _UpperCAmelCase     							=   shape_list(A_    )
           _UpperCAmelCase     							=   []
           _UpperCAmelCase     							=   tf.zeros(hidden_sizes[:2]    )
           for i in range(len(self.cutoffs    )    ):
                _UpperCAmelCase							,  _UpperCAmelCase     							=   self.cutoff_ends[i], self.cutoff_ends[i + 1]
                if target is not None:
                     _UpperCAmelCase     							=   (target >= l_idx) & (target < r_idx)
                     _UpperCAmelCase     							=   tf.where(A_    )
                     _UpperCAmelCase     							=   tf.boolean_mask(A_	,			A_    ) - l_idx

                if self.div_val == 1:
                     _UpperCAmelCase     							=   self.out_layers[0][0][l_idx:r_idx]
                     _UpperCAmelCase     							=   self.out_layers[0][1][l_idx:r_idx]
                else:
                     _UpperCAmelCase     							=   self.out_layers[i][0]
                     _UpperCAmelCase     							=   self.out_layers[i][1]

                if i == 0:
                     _UpperCAmelCase     							=   tf.concat([cur_W, self.cluster_weight]	,			0    )
                     _UpperCAmelCase     							=   tf.concat([cur_b, self.cluster_bias]	,			0    )

                     _UpperCAmelCase     							=   self._logit(A_	,			A_	,			A_	,			self.out_projs[0]    )
                     _UpperCAmelCase     							=   tf.nn.log_softmax(A_    )
                     out.append(head_logprob[..., : self.cutoffs[0]]    )
                     if target is not None:
                          _UpperCAmelCase     							=   tf.boolean_mask(A_	,			A_    )
                          _UpperCAmelCase     							=   self._gather_logprob(A_	,			A_    )
                else:
                     _UpperCAmelCase     							=   self._logit(A_	,			A_	,			A_	,			self.out_projs[i]    )
                     _UpperCAmelCase     							=   tf.nn.log_softmax(A_    )
                     _UpperCAmelCase     							=   self.cutoffs[0] + i - 1  # No probability for the head cluster
                     _UpperCAmelCase     							=   head_logprob[..., cluster_prob_idx, None] + tail_logprob
                     out.append(A_    )
                     if target is not None:
                          _UpperCAmelCase     							=   tf.boolean_mask(A_	,			A_    )
                          _UpperCAmelCase     							=   tf.boolean_mask(A_	,			A_    )
                          _UpperCAmelCase     							=   self._gather_logprob(A_	,			A_    )
                          cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
                if target is not None:
                     loss += tf.scatter_nd(A_	,			-cur_logprob	,			shape_list(A_    )    )
           _UpperCAmelCase     							=   tf.concat(A_	,			axis=-1    )

      if target is not None:
           if return_mean:
                _UpperCAmelCase     							=   tf.reduce_mean(A_    )
           # Add the training-time loss value to the layer using `self.add_loss()`.
           self.add_loss(A_    )

           # Log the loss as a metric (we could log arbitrary metrics,
           # including different metrics for training and inference.
           self.add_metric(A_	,			name=self.name	,			aggregation="mean" if return_mean else ""    )

      return out








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def _a					(			self					,   A_    )  ->       float:
         return 0.0

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     __UpperCamelCase    							=max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.abs(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)
     __UpperCamelCase    							=20 * np.logaa(SCREAMING_SNAKE_CASE__				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     # Display within reasonable bounds
     __UpperCamelCase    							=get_bounds(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     plt.ylim(max([-80, bounds[0]]				)			,     min([80, bounds[1]]				)				)
     plt.ylabel('Gain (dB)'				)

     plt.plot(SCREAMING_SNAKE_CASE__				)
     plt.show()





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.angle(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     plt.ylim(-2 * pi			,     2 * pi				)
     plt.ylabel('Phase shift (Radians)'				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE__			,     -2 * pi				)				)
     plt.show()





