


"""simple docstring"""


from manim import *
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    Rectangle(height=0.5					,     width=0.5			)
										__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0			)

										__lowerCAmelCase       					=    Rectangle(height=0.2_5					,     width=0.2_5			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("CPU"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										cpu.move_to([-2.5, -0.5, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(4			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("GPU"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										gpu.move_to([-1, -1, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)

										__lowerCAmelCase       					=    Text("Model"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										model.move_to([3, -1.0, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

										for i, rect in enumerate(__a			):
													__lowerCAmelCase       					=    fill.copy().set_fill(__a					,     opacity=0.8			)
													target.move_to(__a			)
													model_arr.append(__a			)

													__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0.0			).set_fill(__a					,     opacity=0.8			)
													cpu_target.move_to(cpu_left_col_base[i]			)
													model_cpu_arr.append(__a			)

										self.add(*__a					,     *__a			)

										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("Disk"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										disk.move_to([-4, -1.2_5, 0]			)
										self.add(__a					,     __a			)

										__lowerCAmelCase       					=    Square(side_length=2.2			)
										key.move_to([-5, 2, 0]			)

										__lowerCAmelCase       					=    MarkupText(
										    f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"					,     font_size=18					,     )

										key_text.move_to([-5, 2.4, 0]			)

										self.add(__a					,     __a			)

										__lowerCAmelCase       					=    MarkupText(
										    f"<span fgcolor='{BLUE}'>●</span> Checkpoint"					,     font_size=18					,     )

										blue_text.next_to(__a					,     DOWN * 2.4					,     aligned_edge=key_text.get_left()			)
										self.add(__a			)

										__lowerCAmelCase       					=    MarkupText(
										    f"Now watch as an input is passed through the model\nand how the memory is utilized and handled."					,     font_size=24					,     )
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a			)			)

										__lowerCAmelCase       					=    Square(0.3			)
										input.set_fill(__a					,     opacity=1.0			)
										input.set_stroke(width=0.0			)
										input.next_to(model_base[0]					,     __a					,     buff=0.5			)

										self.play(Write(__a			)			)

										input.generate_target()
										input.target.next_to(model_arr[0]					,     direction=__a					,     buff=0.0_2			)
										self.play(MoveToTarget(__a			)			)

										self.play(FadeOut(__a			)			)

										__lowerCAmelCase       					=    Arrow(start=__a					,     end=__a					,     color=__a					,     buff=0.5			)
										a.next_to(model_arr[0].get_left()					,     __a					,     buff=0.2			)

										model_cpu_arr[0].generate_target()
										model_cpu_arr[0].target.move_to(gpu_rect[0]			)

										__lowerCAmelCase       					=    MarkupText(
										    f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."					,     font_size=24					,     )
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a					,     run_time=3			)			)

										__lowerCAmelCase       					=    {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.0_2}

										self.play(
										    Write(__a			)					,     Circumscribe(model_arr[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_cpu_arr[0]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     )
										self.play(MoveToTarget(model_cpu_arr[0]			)			)

										__lowerCAmelCase       					=    a.copy()
										for i in range(6			):
													a_c.next_to(model_arr[i].get_right() + 0.0_2					,     __a					,     buff=0.2			)

													input.generate_target()
													input.target.move_to(model_arr[i].get_right() + 0.0_2			)

													__lowerCAmelCase       					=    AnimationGroup(
													    FadeOut(__a					,     run_time=0.5			)					,     MoveToTarget(__a					,     run_time=0.5			)					,     FadeIn(__a					,     run_time=0.5			)					,     lag_ratio=0.2			)

													self.play(__a			)

													model_cpu_arr[i].generate_target()
													model_cpu_arr[i].target.move_to(cpu_left_col_base[i]			)

													if i < 5:
																model_cpu_arr[i + 1].generate_target()
																model_cpu_arr[i + 1].target.move_to(gpu_rect[0]			)
																if i >= 1:
																			__lowerCAmelCase       					=    0.7

																self.play(
																    Circumscribe(model_arr[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i + 1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_arr[i + 1]					,     color=__a					,     **__a			)					,     )
																if i < 1:
																			self.play(
																			    MoveToTarget(model_cpu_arr[i]			)					,     MoveToTarget(model_cpu_arr[i + 1]			)					,     )
																else:
																			self.play(
																			    MoveToTarget(model_cpu_arr[i]					,     run_time=0.7			)					,     MoveToTarget(model_cpu_arr[i + 1]					,     run_time=0.7			)					,     )
													else:
																model_cpu_arr[i].generate_target()
																model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]			)
																input.generate_target()
																input.target.next_to(model_arr[-1].get_right()					,     RIGHT + 0.0_2					,     buff=0.2			)

																self.play(
																    Circumscribe(model_arr[-1]					,     color=__a					,     **__a			)					,     Circumscribe(cpu_left_col_base[-1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     )

																self.play(MoveToTarget(model_cpu_arr[i]			)			)

													__lowerCAmelCase       					=    a_c
													__lowerCAmelCase       					=    a_c.copy()

										input.generate_target()
										input.target.next_to(model_base[-1]					,     RIGHT + 0.0_2					,     buff=0.5			)
										self.play(
										    FadeOut(__a			)					,     FadeOut(__a					,     run_time=0.5			)					,     )

										__lowerCAmelCase       					=    MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed."					,     font_size=24			)
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a					,     run_time=3			)					,     MoveToTarget(__a			)			)

										self.wait()








"""simple docstring"""


import tensorflow as tf

from ...tf_utils import shape_list
class 						_UpperCamelCase						(     tf.keras.layers.Layer	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a					,     __a					,     __a					,     __a=1					,     __a=False					,     **__a			):
										super().__init__(**__a			)

										__lowerCAmelCase       					=    vocab_size
										__lowerCAmelCase       					=    d_embed
										__lowerCAmelCase       					=    d_proj

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

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

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

							def     snake_case       (  self					,     __a			):
										if self.n_clusters > 0:
													__lowerCAmelCase       					=    self.add_weight(
													    shape=(self.n_clusters, self.d_embed)					,     initializer="zeros"					,     trainable=__a					,     name="cluster_weight"			)
													__lowerCAmelCase       					=    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:
																			__lowerCAmelCase       					=    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			)
																__lowerCAmelCase       					=    self.add_weight(
																    shape=(self.vocab_size, self.d_embed)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_layers_._{i}_._weight"					,     )
																__lowerCAmelCase       					=    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			)			):
																__lowerCAmelCase					,			__lowerCAmelCase       					=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																__lowerCAmelCase       					=    self.d_embed // (self.div_val**i)

																__lowerCAmelCase       					=    self.add_weight(
																    shape=(d_emb_i, self.d_proj)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_projs_._{i}"			)
																self.out_projs.append(__a			)
																__lowerCAmelCase       					=    self.add_weight(
																    shape=(r_idx - l_idx, d_emb_i)					,     initializer="zeros"					,     trainable=__a					,     name=f"out_layers_._{i}_._weight"					,     )
																__lowerCAmelCase       					=    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     snake_case       (  __a					,     __a					,     __a					,     __a=None			):
										__lowerCAmelCase       					=    x
										if proj is not None:
													__lowerCAmelCase       					=    tf.einsum("ibd,ed->ibe"					,     __a					,     __a			)
										return tf.einsum("ibd,nd->ibn"					,     __a					,     __a			) + b

							@staticmethod
							def     snake_case       (  __a					,     __a			):
										__lowerCAmelCase       					=    shape_list(__a			)
										__lowerCAmelCase       					=    tf.range(lp_size[0]					,     dtype=target.dtype			)
										__lowerCAmelCase       					=    tf.stack([r, target]					,     1			)
										return tf.gather_nd(__a					,     __a			)



							def     snake_case       (  self					,     __a					,     __a					,     __a=True					,     __a=False			):
										__lowerCAmelCase       					=    0
										if self.n_clusters == 0:
													__lowerCAmelCase       					=    self._logit(__a					,     self.out_layers[0][0]					,     self.out_layers[0][1]					,     self.out_projs[0]			)
													if target is not None:
																__lowerCAmelCase       					=    tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__a					,     logits=__a			)
													__lowerCAmelCase       					=    tf.nn.log_softmax(__a					,     axis=-1			)
										else:
													__lowerCAmelCase       					=    shape_list(__a			)
													__lowerCAmelCase       					=    []
													__lowerCAmelCase       					=    tf.zeros(hidden_sizes[:2]			)
													for i in range(len(self.cutoffs			)			):
																__lowerCAmelCase					,			__lowerCAmelCase       					=    self.cutoff_ends[i], self.cutoff_ends[i + 1]
																if target is not None:
																			__lowerCAmelCase       					=    (target >= l_idx) & (target < r_idx)
																			__lowerCAmelCase       					=    tf.where(__a			)
																			__lowerCAmelCase       					=    tf.boolean_mask(__a					,     __a			) - l_idx

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

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

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

										if target is not None:
													if return_mean:
																__lowerCAmelCase       					=    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




