



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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_	):
			lowercase__       	=					torch.exp(SCREAMING_SNAKE_CASE_	)
			lowercase__       	=					torch.sum(SCREAMING_SNAKE_CASE_    ,	dim=1	)  # sum of exp(x_i)
			lowercase__       	=					torch.sum(x * exp_x    ,	dim=1	)  # sum of x_i * exp(x_i)
			return torch.log(SCREAMING_SNAKE_CASE_	) - B / A
class 					_snake_case      (       nn.Module):




				def __init__(    self       :    List[str],       __lowercase       :    Union[str, Any]    ):
							super().__init__()
							lowercase__       	=					config.output_attentions
							lowercase__       	=					config.output_hidden_states
							lowercase__       	=					nn.ModuleList([BertLayer(__lowerCamelCase    ) for _ in range(config.num_hidden_layers    )]    )
							lowercase__       	=					nn.ModuleList([BertHighway(__lowerCamelCase    ) for _ in range(config.num_hidden_layers    )]    )

							lowercase__       	=					[-1 for _ in range(config.num_hidden_layers    )]




				def 				A__   (    self       :    Dict,       __lowercase       :    List[Any]    ):




							if (type(__lowerCamelCase    ) is float) or (type(__lowerCamelCase    ) is int):
										for i in range(len(self.early_exit_entropy    )    ):
													lowercase__       	=					x
							else:
										lowercase__       	=					x




				def 				A__   (    self       :    List[str],       __lowercase       :    List[Any]    ):
							lowercase__       	=					pooler.state_dict()
							for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name]    )




				def 				A__   (    self       :    Any,       __lowercase       :    Union[str, Any],       __lowercase       :    Optional[Any]=None,       __lowercase       :    Tuple=None,       __lowercase       :    Tuple=None,       __lowercase       :    Tuple=None,       ):
							lowercase__       	=					()
							lowercase__       	=					()
							lowercase__       	=					()
							for i, layer_module in enumerate(self.layer    ):
										if self.output_hidden_states:
													lowercase__       	=					all_hidden_states + (hidden_states,)

										lowercase__       	=					layer_module(
										    __lowerCamelCase,       __lowerCamelCase,       head_mask[i],       __lowerCamelCase,       __lowerCamelCase    )
										lowercase__       	=					layer_outputs[0]

										if self.output_attentions:
													lowercase__       	=					all_attentions + (layer_outputs[1],)

										lowercase__       	=					(hidden_states,)
										if self.output_hidden_states:
													lowercase__       	=					current_outputs + (all_hidden_states,)
										if self.output_attentions:
													lowercase__       	=					current_outputs + (all_attentions,)

										lowercase__       	=					self.highway[i](__lowerCamelCase    )
										# logits, pooled_output

										if not self.training:
													lowercase__       	=					highway_exit[0]
													lowercase__       	=					entropy(__lowerCamelCase    )
													lowercase__       	=					highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													lowercase__       	=					all_highway_exits + (highway_exit,)

													if highway_entropy < self.early_exit_entropy[i]:
																lowercase__       	=					(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																raise HighwayException(__lowerCamelCase,       i + 1    )
										else:
													lowercase__       	=					all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
										lowercase__       	=					all_hidden_states + (hidden_states,)

							lowercase__       	=					(hidden_states,)
							if self.output_hidden_states:
										lowercase__       	=					outputs + (all_hidden_states,)
							if self.output_attentions:
										lowercase__       	=					outputs + (all_attentions,)

							lowercase__       	=					outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
    """The Bert Model transformer with early exiting (DeeBERT). """     ,						UpperCAmelCase_     ,						)
class 					_snake_case      (       UpperCAmelCase_):




				def __init__(    self       :    int,       __lowercase       :    Optional[int]    ):
							super().__init__(__lowerCamelCase    )
							lowercase__       	=					config

							lowercase__       	=					BertEmbeddings(__lowerCamelCase    )
							lowercase__       	=					DeeBertEncoder(__lowerCamelCase    )
							lowercase__       	=					BertPooler(__lowerCamelCase    )

							self.init_weights()




				def 				A__   (    self       :    Tuple    ):
							self.encoder.init_highway_pooler(self.pooler    )




				def 				A__   (    self       :    int    ):
							return self.embeddings.word_embeddings




				def 				A__   (    self       :    int,       __lowercase       :    Optional[Any]    ):
							lowercase__       	=					value




				def 				A__   (    self       :    Optional[int],       __lowercase       :    Dict    ):




							for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(__lowerCamelCase    )




				@add_start_docstrings_to_model_forward(__lowerCamelCase    )
				def 				A__   (    self       :    Dict,       __lowercase       :    Dict=None,       __lowercase       :    Dict=None,       __lowercase       :    List[str]=None,       __lowercase       :    Optional[int]=None,       __lowercase       :    Optional[int]=None,       __lowercase       :    Tuple=None,       __lowercase       :    Dict=None,       __lowercase       :    List[str]=None,       ):




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

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

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

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							lowercase__       	=					self.get_extended_attention_mask(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase    )

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
										lowercase__       	=					encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										lowercase__       	=					encoder_attention_mask[:, None, None, :]

							lowercase__       	=					encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()    ).dtype    )  # fp16 compatibility
							lowercase__       	=					(1.0 - encoder_extended_attention_mask) * -10000.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							lowercase__       	=					self.get_head_mask(__lowerCamelCase,       self.config.num_hidden_layers    )

							lowercase__       	=					self.embeddings(
							    input_ids=__lowerCamelCase,       position_ids=__lowerCamelCase,       token_type_ids=__lowerCamelCase,       inputs_embeds=__lowerCamelCase    )
							lowercase__       	=					self.encoder(
							    __lowerCamelCase,       attention_mask=__lowerCamelCase,       head_mask=__lowerCamelCase,       encoder_hidden_states=__lowerCamelCase,       encoder_attention_mask=__lowerCamelCase,       )
							lowercase__       	=					encoder_outputs[0]
							lowercase__       	=					self.pooler(__lowerCamelCase    )

							lowercase__       	=					(
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class 					_snake_case      (       UpperCAmelCase_):




				def __init__(    self       :    List[str],       __lowercase       :    Union[str, Any],       __lowercase       :    List[Any]    ):
							lowercase__       	=					message
							lowercase__       	=					exit_layer  # start from 1!
class 					_snake_case      (       nn.Module):




				def __init__(    self       :    Optional[Any],       __lowercase       :    str    ):
							super().__init__()
							lowercase__       	=					BertPooler(__lowerCamelCase    )
							lowercase__       	=					nn.Dropout(config.hidden_dropout_prob    )
							lowercase__       	=					nn.Linear(config.hidden_size,       config.num_labels    )




				def 				A__   (    self       :    Dict,       __lowercase       :    Any    ):
							lowercase__       	=					encoder_outputs[0]
							lowercase__       	=					self.pooler(__lowerCamelCase    )
							# "return" pooler_output

							# BertModel
							lowercase__       	=					(pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							lowercase__       	=					bmodel_output[1]

							lowercase__       	=					self.dropout(__lowerCamelCase    )
							lowercase__       	=					self.classifier(__lowerCamelCase    )

							return logits, pooled_output
@add_start_docstrings(
    """Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. """     ,						UpperCAmelCase_     ,						)
class 					_snake_case      (       UpperCAmelCase_):




				def __init__(    self       :    List[str],       __lowercase       :    str    ):
							super().__init__(__lowerCamelCase    )
							lowercase__       	=					config.num_labels
							lowercase__       	=					config.num_hidden_layers

							lowercase__       	=					DeeBertModel(__lowerCamelCase    )
							lowercase__       	=					nn.Dropout(config.hidden_dropout_prob    )
							lowercase__       	=					nn.Linear(config.hidden_size,       self.config.num_labels    )

							self.init_weights()




				@add_start_docstrings_to_model_forward(__lowerCamelCase    )
				def 				A__   (    self       :    Any,       __lowercase       :    Any=None,       __lowercase       :    Any=None,       __lowercase       :    List[str]=None,       __lowercase       :    Optional[int]=None,       __lowercase       :    Optional[int]=None,       __lowercase       :    Any=None,       __lowercase       :    List[Any]=None,       __lowercase       :    List[str]=-1,       __lowercase       :    List[str]=False,       ):
							lowercase__       	=					self.num_layers
							try:
										lowercase__       	=					self.bert(
										    __lowerCamelCase,       attention_mask=__lowerCamelCase,       token_type_ids=__lowerCamelCase,       position_ids=__lowerCamelCase,       head_mask=__lowerCamelCase,       inputs_embeds=__lowerCamelCase,       )
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										lowercase__       	=					outputs[1]

										lowercase__       	=					self.dropout(__lowerCamelCase    )
										lowercase__       	=					self.classifier(__lowerCamelCase    )
										lowercase__       	=					(logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
										lowercase__       	=					e.message
										lowercase__       	=					e.exit_layer
										lowercase__       	=					outputs[0]

							if not self.training:
										lowercase__       	=					entropy(__lowerCamelCase    )
										lowercase__       	=					[]
										lowercase__       	=					[]
							if labels is not None:
										if self.num_labels == 1:
													#  We are doing regression
													lowercase__       	=					MSELoss()
													lowercase__       	=					loss_fct(logits.view(-1    ),       labels.view(-1    )    )
										else:
													lowercase__       	=					CrossEntropyLoss()
													lowercase__       	=					loss_fct(logits.view(-1,       self.num_labels    ),       labels.view(-1    )    )

										# work with highway exits
										lowercase__       	=					[]
										for highway_exit in outputs[-1]:
													lowercase__       	=					highway_exit[0]
													if not self.training:
																highway_logits_all.append(__lowerCamelCase    )
																highway_entropy.append(highway_exit[2]    )
													if self.num_labels == 1:
																#  We are doing regression
																lowercase__       	=					MSELoss()
																lowercase__       	=					loss_fct(highway_logits.view(-1    ),       labels.view(-1    )    )
													else:
																lowercase__       	=					CrossEntropyLoss()
																lowercase__       	=					loss_fct(highway_logits.view(-1,       self.num_labels    ),       labels.view(-1    )    )
													highway_losses.append(__lowerCamelCase    )

										if train_highway:
													lowercase__       	=					(sum(highway_losses[:-1]    ),) + outputs
													# exclude the final highway, of course
										else:
													lowercase__       	=					(loss,) + outputs
							if not self.training:
										lowercase__       	=					outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													lowercase__       	=					(
													    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
													)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''rwkv'''
	__UpperCamelCase   =		{'''max_position_embeddings''': '''context_length'''}




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Tuple=50_277					,      __lowerCamelCase							:     List[str]=1_024					,      __lowerCamelCase							:     List[Any]=4_096					,      __lowerCamelCase							:     Tuple=32					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Optional[Any]=1E-5					,      __lowerCamelCase							:     Tuple=0					,      __lowerCamelCase							:     int=0					,      __lowerCamelCase							:     Dict=6					,      __lowerCamelCase							:     List[Any]=False					,      __lowerCamelCase							:     Tuple=True					,      **__lowerCamelCase							:     Optional[int]					,      )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     context_length
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     attention_hidden_size if attention_hidden_size is not None else hidden_size
			__lowercase											=     intermediate_size if intermediate_size is not None else 4 * hidden_size
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     rescale_every
			__lowercase											=     use_cache

			__lowercase											=     bos_token_id
			__lowercase											=     eos_token_id

			super().__init__(
			    tie_word_embeddings=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase   )




