

import tensorflow as tf

from ...tf_utils import shape_list


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






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=1			,					__UpperCamelCase=False			,					**__UpperCamelCase				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)

				snake_case_   		=			vocab_size
				snake_case_   		=			d_embed
				snake_case_   		=			d_proj

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

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

				snake_case_   		=			[]
				snake_case_   		=			[]


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if self.n_clusters > 0:
					snake_case_   		=			self.add_weight(
					    shape=(self.n_clusters, self.d_embed)			,					initializer='zeros'			,					trainable=__UpperCamelCase			,					name='cluster_weight'				)
					snake_case_   		=			self.add_weight(
					    shape=(self.n_clusters,)			,					initializer='zeros'			,					trainable=__UpperCamelCase			,					name='cluster_bias'				)

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

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


			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=None				):

				"""simple docstring"""


				snake_case_   		=			x
				if proj is not None:
					snake_case_   		=			tf.einsum('ibd,ed->ibe'			,					__UpperCamelCase			,					__UpperCamelCase				)
				return tf.einsum('ibd,nd->ibn'			,					__UpperCamelCase			,					__UpperCamelCase				) + b


			@staticmethod
			def       __lowerCAmelCase			(     __UpperCamelCase			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			shape_list(__UpperCamelCase				)
				snake_case_   		=			tf.range(lp_size[0]			,					dtype=target.dtype				)
				snake_case_   		=			tf.stack([r, target]			,					1				)
				return tf.gather_nd(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=True			,					__UpperCamelCase=False				):

				"""simple docstring"""


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

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

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

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

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

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

				return out



A									=  [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages
