


import tensorflow as tf

from ...tf_utils import shape_list

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




							"""simple docstring"""


							def __init__(				self			:    List[Any]       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Tuple=1       ,      lowerCamelCase			:    int=False       ,      **lowerCamelCase			:    Union[str, Any]							)      ->       Optional[int]:
														super().__init__(**_A							)

														__snake_case						:     List[str]									=  vocab_size
														__snake_case						:     Optional[Any]									=  d_embed
														__snake_case						:     List[Any]									=  d_proj

														__snake_case						:     List[Any]									=  cutoffs + [vocab_size]
														__snake_case						:     Any									=  [0] + self.cutoffs
														__snake_case						:     Union[str, Any]									=  div_val

														__snake_case						:     Dict									=  self.cutoffs[0]
														__snake_case						:     Any									=  len(self.cutoffs							) - 1
														__snake_case						:     Optional[Any]									=  self.shortlist_size + self.n_clusters
														__snake_case						:     int									=  keep_order

														__snake_case						:     Any									=  []
														__snake_case						:     Optional[int]									=  []

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Tuple							)      ->       Optional[int]:







														if self.n_clusters > 0:
																					__snake_case						:     int									=  self.add_weight(
																					    shape=(self.n_clusters, self.d_embed)       ,      initializer="zeros"       ,      trainable=_A       ,      name="cluster_weight"							)
																					__snake_case						:     Optional[int]									=  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:
																																			__snake_case						:     Optional[int]									=  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							)
																												__snake_case						:     List[str]									=  self.add_weight(
																												    shape=(self.vocab_size, self.d_embed)       ,      initializer="zeros"       ,      trainable=_A       ,      name=F'out_layers_._{i}_._weight'       ,      )
																												__snake_case						:     int									=  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							)							):
																												__snake_case						:     Optional[Any]									=  self.cutoff_ends[i], self.cutoff_ends[i + 1]
																												__snake_case						:     int									=  self.d_embed // (self.div_val**i)

																												__snake_case						:     Any									=  self.add_weight(
																												    shape=(d_emb_i, self.d_proj)       ,      initializer="zeros"       ,      trainable=_A       ,      name=F'out_projs_._{i}'							)
																												self.out_projs.append(_A							)
																												__snake_case						:     int									=  self.add_weight(
																												    shape=(r_idx - l_idx, d_emb_i)       ,      initializer="zeros"       ,      trainable=_A       ,      name=F'out_layers_._{i}_._weight'       ,      )
																												__snake_case						:     Optional[Any]									=  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   (				lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Dict=None							)      ->       Any:
														__snake_case						:     List[str]									=  x
														if proj is not None:
																					__snake_case						:     List[str]									=  tf.einsum("ibd,ed->ibe"       ,      _A       ,      _A							)
														return tf.einsum("ibd,nd->ibn"       ,      _A       ,      _A							) + b

							@staticmethod
							def        __snake_case   (				lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Optional[int]							)      ->       str:
														__snake_case						:     Optional[Any]									=  shape_list(_A							)
														__snake_case						:     Any									=  tf.range(lp_size[0]       ,      dtype=target.dtype							)
														__snake_case						:     str									=  tf.stack([r, target]       ,      1							)
														return tf.gather_nd(_A       ,      _A							)






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

																												if self.div_val == 1:
																																			__snake_case						:     str									=  self.out_layers[0][0][l_idx:r_idx]
																																			__snake_case						:     Union[str, Any]									=  self.out_layers[0][1][l_idx:r_idx]
																												else:
																																			__snake_case						:     Tuple									=  self.out_layers[i][0]
																																			__snake_case						:     Optional[Any]									=  self.out_layers[i][1]

																												if i == 0:
																																			__snake_case						:     str									=  tf.concat([cur_W, self.cluster_weight]       ,      0							)
																																			__snake_case						:     List[Any]									=  tf.concat([cur_b, self.cluster_bias]       ,      0							)

																																			__snake_case						:     Optional[int]									=  self._logit(_A       ,      _A       ,      _A       ,      self.out_projs[0]							)
																																			__snake_case						:     Optional[int]									=  tf.nn.log_softmax(_A							)
																																			out.append(head_logprob[..., : self.cutoffs[0]]							)
																																			if target is not None:
																																										__snake_case						:     Dict									=  tf.boolean_mask(_A       ,      _A							)
																																										__snake_case						:     Optional[Any]									=  self._gather_logprob(_A       ,      _A							)
																												else:
																																			__snake_case						:     Tuple									=  self._logit(_A       ,      _A       ,      _A       ,      self.out_projs[i]							)
																																			__snake_case						:     str									=  tf.nn.log_softmax(_A							)
																																			__snake_case						:     List[str]									=  self.cutoffs[0] + i - 1  # No probability for the head cluster
																																			__snake_case						:     Tuple									=  head_logprob[..., cluster_prob_idx, None] + tail_logprob
																																			out.append(_A							)
																																			if target is not None:
																																										__snake_case						:     List[Any]									=  tf.boolean_mask(_A       ,      _A							)
																																										__snake_case						:     int									=  tf.boolean_mask(_A       ,      _A							)
																																										__snake_case						:     Optional[Any]									=  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							)							)
																					__snake_case						:     int									=  tf.concat(_A       ,      axis=-1							)

														if target is not None:
																					if return_mean:
																												__snake_case						:     Union[str, Any]									=  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








"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



