



"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__a           =							logging.get_logger(__name__)

__a           =							{
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Optional[Any]		=	"""longt5"""
					_A						:     Tuple		=	["""past_key_values"""]
					_A						:     Dict		=	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






					def __init__(			self:				Union[str, Any]		,	snake_case:				List[Any]=32_128		,	snake_case:				Tuple=512		,	snake_case:				Union[str, Any]=64		,	snake_case:				str=2_048		,	snake_case:				Optional[int]=6		,	snake_case:				int=None		,	snake_case:				int=8		,	snake_case:				str=127		,	snake_case:				Optional[Any]=16		,	snake_case:				List[Any]=32		,	snake_case:				Union[str, Any]=128		,	snake_case:				int=0.1		,	snake_case:				Union[str, Any]=1E-6		,	snake_case:				str=1.0		,	snake_case:				str="relu"		,	snake_case:				Any=True		,	snake_case:				Optional[Any]="local"		,	snake_case:				Any=True		,	snake_case:				Dict=0		,	snake_case:				List[Any]=1		,	**snake_case:				Any		,	)  ->      Any:
											snake_case_					:int		=     vocab_size
											snake_case_					:str		=     d_model
											snake_case_					:str		=     d_kv
											snake_case_					:List[str]		=     d_ff
											snake_case_					:str		=     num_layers
											# default = symmetry
											snake_case_					:Optional[Any]		=     num_decoder_layers if num_decoder_layers is not None else self.num_layers
											snake_case_					:Tuple		=     num_heads
											snake_case_					:str		=     local_radius
											snake_case_					:List[Any]		=     global_block_size
											snake_case_					:Any		=     relative_attention_num_buckets
											snake_case_					:str		=     relative_attention_max_distance
											snake_case_					:List[Any]		=     dropout_rate
											snake_case_					:Optional[int]		=     layer_norm_epsilon
											snake_case_					:int		=     initializer_factor
											snake_case_					:Dict		=     feed_forward_proj
											snake_case_					:List[str]		=     encoder_attention_type
											snake_case_					:Any		=     use_cache

											snake_case_					:int		=     self.feed_forward_proj.split("""-"""		)
											snake_case_					:str		=     act_info[-1]
											snake_case_					:Union[str, Any]		=     act_info[0] == """gated"""

											if len(snake_case		) > 1 and act_info[0] != "gated" or len(snake_case		) > 2:
																	raise ValueError(
																	    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																	    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
																	    """'gated-gelu' or 'relu'"""		)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																	snake_case_					:Tuple		=     """gelu_new"""

											super().__init__(
											    pad_token_id=snake_case		,	eos_token_id=snake_case		,	is_encoder_decoder=snake_case		,	**snake_case		,	)

class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					@property
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Mapping[str, Mapping[int, str]]:
											snake_case_					:Any		=     {
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																	snake_case_					:Optional[int]		=     """past_encoder_sequence + sequence"""
																	snake_case_					:int		=     {0: """batch"""}
																	snake_case_					:Optional[int]		=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																	snake_case_					:List[str]		=     {0: """batch""", 1: """decoder_sequence"""}
																	snake_case_					:List[Any]		=     {0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
																	self.fill_with_past_key_values_(snake_case		,	direction="""inputs"""		)

											return common_inputs






					@property
					def   lowerCAmelCase_							(			self:				int		)  ->      int:
											return 13




'''simple docstring'''

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


_UpperCAmelCase					:			List[str]				     =					logging.getLogger(__name__)


class    a__					(			__A   ):







				"""simple docstring"""



				def        _snake_case	(self							,      __lowercase							,      __lowercase							,      __lowercase=None							,      __lowercase=None		):
										if current_layer == 0:
																__lowerCAmelCase											=					self.embedding_hidden_mapping_in(__lowercase		)
										else:
																__lowerCAmelCase											=					hidden_states[0]

										__lowerCAmelCase											=					int(self.config.num_hidden_layers / self.config.num_hidden_groups		)

										# Index of the hidden group
										__lowerCAmelCase											=					int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)		)

										__lowerCAmelCase											=					self.albert_layer_groups[group_idx](
										    __lowercase							,      __lowercase							,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]							,      )
										__lowerCAmelCase											=					layer_group_output[0]

										return (hidden_states,)


@add_start_docstrings(
    'The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)

										__lowerCAmelCase											=					AlbertTransformerWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0

										__lowerCAmelCase											=					0




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					threshold




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					patience




				def        _snake_case	(self		):
										__lowerCAmelCase											=					0
										__lowerCAmelCase											=					0




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase											=					(
										    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
										    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
										)
										print(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=False							,      ):

										if input_ids is not None and inputs_embeds is not None:
																raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''		)
										elif input_ids is not None:
																__lowerCAmelCase											=					input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase											=					inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

										__lowerCAmelCase											=					input_ids.device if input_ids is not None else inputs_embeds.device

										if attention_mask is None:
																__lowerCAmelCase											=					torch.ones(__lowercase							,      device=__lowercase		)
										if token_type_ids is None:
																__lowerCAmelCase											=					torch.zeros(__lowercase							,      dtype=torch.long							,      device=__lowercase		)

										__lowerCAmelCase											=					attention_mask.unsqueeze(1		).unsqueeze(2		)
										__lowerCAmelCase											=					extended_attention_mask.to(dtype=self.dtype		)  # fp16 compatibility
										__lowerCAmelCase											=					(1.0 - extended_attention_mask) * -1_0_0_0_0.0
										__lowerCAmelCase											=					self.get_head_mask(__lowercase							,      self.config.num_hidden_layers		)

										__lowerCAmelCase											=					self.embeddings(
										    __lowercase							,      position_ids=__lowercase							,      token_type_ids=__lowercase							,      inputs_embeds=__lowercase		)
										__lowerCAmelCase											=					embedding_output

										if self.training:
																__lowerCAmelCase											=					[]
																for i in range(self.config.num_hidden_layers		):
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](output_dropout(__lowercase		)		)
																						res.append(__lowercase		)
										elif self.patience == 0:  # Use all layers for inference
																__lowerCAmelCase											=					self.encoder(__lowercase							,      __lowercase							,      head_mask=__lowercase		)
																__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																__lowerCAmelCase											=					[output_layers[self.config.num_hidden_layers - 1](__lowercase		)]
										else:
																__lowerCAmelCase											=					0
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for i in range(self.config.num_hidden_layers		):
																						calculated_layer_num += 1
																						__lowerCAmelCase											=					self.encoder.adaptive_forward(
																						    __lowercase							,      current_layer=__lowercase							,      attention_mask=__lowercase							,      head_mask=__lowercase							,      )

																						__lowerCAmelCase											=					self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]		)		)
																						__lowerCAmelCase											=					output_layers[i](__lowercase		)
																						if regression:
																												__lowerCAmelCase											=					logits.detach()
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach()
																												if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0
																						else:
																												__lowerCAmelCase											=					logits.detach().argmax(dim=1		)
																												if patient_result is not None:
																																		__lowerCAmelCase											=					patient_result.detach().argmax(dim=1		)
																												if (patient_result is not None) and torch.all(labels.eq(__lowercase		)		):
																																		patient_counter += 1
																												else:
																																		__lowerCAmelCase											=					0

																						__lowerCAmelCase											=					logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase											=					[patient_result]
																self.inference_layers_num += calculated_layer_num
																self.inference_instances_num += 1

										return res


@add_start_docstrings(
    'Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase											=					config.num_labels

										__lowerCAmelCase											=					AlbertModelWithPabee(__lowercase		)
										__lowerCAmelCase											=					nn.Dropout(config.classifier_dropout_prob		)
										__lowerCAmelCase											=					nn.ModuleList(
										    [nn.Linear(config.hidden_size							,      self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

										self.init_weights()




				@add_start_docstrings_to_model_forward(__lowercase		)
				def        _snake_case	(self							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      __lowercase=None							,      ):
										__lowerCAmelCase											=					self.albert(
										    input_ids=__lowercase							,      attention_mask=__lowercase							,      token_type_ids=__lowercase							,      position_ids=__lowercase							,      head_mask=__lowercase							,      inputs_embeds=__lowercase							,      output_dropout=self.dropout							,      output_layers=self.classifiers							,      regression=self.num_labels == 1							,      )

										__lowerCAmelCase											=					(logits[-1],)

										if labels is not None:
																__lowerCAmelCase											=					None
																__lowerCAmelCase											=					0
																for ix, logits_item in enumerate(__lowercase		):
																						if self.num_labels == 1:
																												#  We are doing regression
																												__lowerCAmelCase											=					MSELoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1		)							,      labels.view(-1		)		)
																						else:
																												__lowerCAmelCase											=					CrossEntropyLoss()
																												__lowerCAmelCase											=					loss_fct(logits_item.view(-1							,      self.num_labels		)							,      labels.view(-1		)		)
																						if total_loss is None:
																												__lowerCAmelCase											=					loss
																						else:
																												total_loss += loss * (ix + 1)
																						total_weights += ix + 1
																__lowerCAmelCase											=					(total_loss / total_weights,) + outputs

										return outputs

