
"""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,
)


snake_case					  =						logging.getLogger(__name__)




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__=None						,      lowercase__=None     )       ->       int:
									"""simple docstring"""







									if current_layer == 0:
														SCREAMING_SNAKE_CASE           =      self.embedding_hidden_mapping_in(lowercase__     )
									else:
														SCREAMING_SNAKE_CASE           =      hidden_states[0]

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

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

									SCREAMING_SNAKE_CASE           =      self.albert_layer_groups[group_idx](
									    lowercase__						,      lowercase__						,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]						,      )
									SCREAMING_SNAKE_CASE           =      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."    , __magic_name__    , )
class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__     )       ->       Union[str, Any]:
									"""simple docstring"""







									super().__init__(lowercase__     )

									SCREAMING_SNAKE_CASE           =      AlbertTransformerWithPabee(lowercase__     )

									self.init_weights()
									SCREAMING_SNAKE_CASE           =      0
									SCREAMING_SNAKE_CASE           =      0
									SCREAMING_SNAKE_CASE           =      0

									SCREAMING_SNAKE_CASE           =      0


				def 				A   (							self						,      lowercase__     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      threshold


				def 				A   (							self						,      lowercase__     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      patience


				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      0
									SCREAMING_SNAKE_CASE           =      0


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.inference_layers_num / self.inference_instances_num
									SCREAMING_SNAKE_CASE           =      (
									    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 				A   (							self						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=False						,      )       ->       Any:
									"""simple docstring"""







									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:
														SCREAMING_SNAKE_CASE           =      input_ids.size()
									elif inputs_embeds is not None:
														SCREAMING_SNAKE_CASE           =      inputs_embeds.size()[:-1]
									else:
														raise ValueError('You have to specify either input_ids or inputs_embeds'     )

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

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

									SCREAMING_SNAKE_CASE           =      attention_mask.unsqueeze(1     ).unsqueeze(2     )
									SCREAMING_SNAKE_CASE           =      extended_attention_mask.to(dtype=self.dtype     )  # fp16 compatibility
									SCREAMING_SNAKE_CASE           =      (1.0 - extended_attention_mask) * -10_000.0
									SCREAMING_SNAKE_CASE           =      self.get_head_mask(lowercase__						,      self.config.num_hidden_layers     )

									SCREAMING_SNAKE_CASE           =      self.embeddings(
									    lowercase__						,      position_ids=lowercase__						,      token_type_ids=lowercase__						,      inputs_embeds=lowercase__     )
									SCREAMING_SNAKE_CASE           =      embedding_output

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

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

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

																			SCREAMING_SNAKE_CASE           =      logits
																			if patient_counter == self.patience:
																								break
														SCREAMING_SNAKE_CASE           =      [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. "    , __magic_name__    , )
class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__     )       ->       int:
									"""simple docstring"""







									super().__init__(lowercase__     )
									SCREAMING_SNAKE_CASE           =      config.num_labels

									SCREAMING_SNAKE_CASE           =      AlbertModelWithPabee(lowercase__     )
									SCREAMING_SNAKE_CASE           =      nn.Dropout(config.classifier_dropout_prob     )
									SCREAMING_SNAKE_CASE           =      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 				A   (							self						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      lowercase__=None						,      )       ->       List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      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						,      )

									SCREAMING_SNAKE_CASE           =      (logits[-1],)

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

									return outputs


"""simple docstring"""




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


snake_case					  =						[nn.LayerNorm]

snake_case					  =						logging.get_logger(__name__)

snake_case					  =						version.parse(version.parse(torch.__version__).base_version)

snake_case					  =						parsed_torch_version_base >= version.parse('2.0')
snake_case					  =						parsed_torch_version_base >= version.parse('1.12')
snake_case					  =						parsed_torch_version_base >= version.parse('1.11')
snake_case					  =						parsed_torch_version_base < version.parse('1.11')

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	self     ):
					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self     )
					else:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self.dtype     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
										else:
															SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      nn.Linear(new_size[1],	new_size[0],	bias=layer.bias is not None     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					if layer.bias is not None:
										SCREAMING_SNAKE_CASE           =      False
										new_layer.bias.copy_(b.contiguous()     )
										SCREAMING_SNAKE_CASE           =      True
					return new_layer




class 	UpperCamelCase      (			nn.Module ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =      nf
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.empty(lowercase__						,      lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.zeros(lowercase__     )     )
									nn.init.normal_(self.weight						,      std=0.02     )




				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      x.size()[:-1] + (self.nf,)
									SCREAMING_SNAKE_CASE           =      torch.addmm(self.bias						,      x.view(-1						,      x.size(-1     )     )						,      self.weight     )
									SCREAMING_SNAKE_CASE           =      x.view(lowercase__     )
									return x






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 1     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if dim == 0:
										SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
					else:
										SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      ConvaD(new_size[1],	new_size[0]     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					SCREAMING_SNAKE_CASE           =      False
					new_layer.bias.copy_(b.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					return new_layer

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					if isinstance(SCREAMING_SNAKE_CASE_,	nn.Linear     ):
										return prune_linear_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=0 if dim is None else dim     )
					elif isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										return prune_convad_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=1 if dim is None else dim     )
					else:
										raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	*SCREAMING_SNAKE_CASE_     ):
					assert len(SCREAMING_SNAKE_CASE_     ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					SCREAMING_SNAKE_CASE           =      len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
					if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(
										    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '''
										    'tensors are given'     )

					if chunk_size > 0:
										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
																				    f'''found shape {input_tensor.shape[chunk_dim]}'''     )

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
															    f'''size {chunk_size}'''     )

										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										SCREAMING_SNAKE_CASE           =      tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     ) for input_tensor in input_tensors     )
										# apply forward fn to every tuple
										SCREAMING_SNAKE_CASE           =      tuple(forward_fn(*SCREAMING_SNAKE_CASE_     ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE_     )     )
										# concatenate output at same dimension
										return torch.cat(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     )

					return forward_fn(*SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      torch.ones(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      set(SCREAMING_SNAKE_CASE_     ) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										SCREAMING_SNAKE_CASE           =      head - sum(1 if h < head else 0 for h in already_pruned_heads     )
										SCREAMING_SNAKE_CASE           =      0
					SCREAMING_SNAKE_CASE           =      mask.view(-1     ).contiguous().eq(1     )
					SCREAMING_SNAKE_CASE           =      torch.arange(len(SCREAMING_SNAKE_CASE_     )     )[mask].long()
					return heads, index

def     UpperCamelCase_    (						*SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,	indexing=SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )
