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 							__UpperCAmelCase     (							UpperCAmelCase							)->							Dict:



			"""simple docstring"""







			lowercase       		=		torch.exp(_lowerCAmelCase							)
			lowercase       		=		torch.sum(_lowerCAmelCase,		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(_lowerCAmelCase							) - B / A






class   __lowercase       (     nn.Module     ):

						def __init__(						self					:   Dict				,				__lowerCamelCase					:   int )				->		Optional[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase       		=		config.output_attentions
									lowercase       		=		config.output_hidden_states
									lowercase       		=		nn.ModuleList([BertLayer(snake_case_ ) for _ in range(config.num_hidden_layers )] )
									lowercase       		=		nn.ModuleList([BertHighway(snake_case_ ) for _ in range(config.num_hidden_layers )] )

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

						def 							__a		(						self					:   Optional[Any]				,				__lowerCamelCase					:   Tuple )				->		List[Any]:


									'''simple docstring'''

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

						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   Any )				->		Dict:


									'''simple docstring'''

									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				,				__lowerCamelCase					:   List[str]				,				__lowerCamelCase					:   str=None				,				__lowerCamelCase					:   Dict=None				,				__lowerCamelCase					:   Optional[Any]=None				,				__lowerCamelCase					:   Union[str, Any]=None				,				)				->		List[str]:


									'''simple docstring'''

									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(
												    snake_case_				,				snake_case_				,				head_mask[i]				,				snake_case_				,				snake_case_ )
												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](snake_case_ )
												# logits, pooled_output

												if not self.training:
															lowercase       		=		highway_exit[0]
															lowercase       		=		entropy(snake_case_ )
															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(snake_case_				,				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). '							,  A__							,  )
class   __lowercase       (     A__     ):

						def __init__(						self					:   Optional[int]				,				__lowerCamelCase					:   Optional[Any] )				->		Tuple:


									'''simple docstring'''

									super().__init__(snake_case_ )
									lowercase       		=		config

									lowercase       		=		BertEmbeddings(snake_case_ )
									lowercase       		=		DeeBertEncoder(snake_case_ )
									lowercase       		=		BertPooler(snake_case_ )

									self.init_weights()

						def 							__a		(						self					:   Optional[int] )				->		Dict:


									'''simple docstring'''

									self.encoder.init_highway_pooler(self.pooler )

						def 							__a		(						self					:   int )				->		List[Any]:


									'''simple docstring'''

									return self.embeddings.word_embeddings

						def 							__a		(						self					:   str				,				__lowerCamelCase					:   Any )				->		Tuple:


									'''simple docstring'''

									lowercase       		=		value

						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   int )				->		List[str]:


									'''simple docstring'''

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



						@add_start_docstrings_to_model_forward(snake_case_ )
						def 							__a		(						self					:   Union[str, Any]				,				__lowerCamelCase					:   Tuple=None				,				__lowerCamelCase					:   Tuple=None				,				__lowerCamelCase					:   str=None				,				__lowerCamelCase					:   Union[str, Any]=None				,				__lowerCamelCase					:   List[str]=None				,				__lowerCamelCase					:   List[str]=None				,				__lowerCamelCase					:   int=None				,				__lowerCamelCase					:   Union[str, Any]=None				,				)				->		Optional[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:
												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(snake_case_				,				device=snake_case_ )
									if encoder_attention_mask is None:
												lowercase       		=		torch.ones(snake_case_				,				device=snake_case_ )
									if token_type_ids is None:
												lowercase       		=		torch.zeros(snake_case_				,				dtype=torch.long				,				device=snake_case_ )

									# 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(snake_case_				,				snake_case_				,				snake_case_ )

									# 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) * -1_00_00.0

									# 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(snake_case_				,				self.config.num_hidden_layers )

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

									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   __lowercase       (     A__     ):

						def __init__(						self					:   Any				,				__lowerCamelCase					:   Union[str, Any]				,				__lowerCamelCase					:   Optional[Any] )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		message
									lowercase       		=		exit_layer  # start from 1!






class   __lowercase       (     nn.Module     ):

						def __init__(						self					:   Tuple				,				__lowerCamelCase					:   str )				->		List[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase       		=		BertPooler(snake_case_ )
									lowercase       		=		nn.Dropout(config.hidden_dropout_prob )
									lowercase       		=		nn.Linear(config.hidden_size				,				config.num_labels )



						def 							__a		(						self					:   Optional[Any]				,				__lowerCamelCase					:   List[str] )				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		encoder_outputs[0]
									lowercase       		=		self.pooler(snake_case_ )
									# "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(snake_case_ )
									lowercase       		=		self.classifier(snake_case_ )

									return logits, pooled_output






@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '							,  A__							,  )
class   __lowercase       (     A__     ):

						def __init__(						self					:   List[Any]				,				__lowerCamelCase					:   Tuple )				->		Optional[Any]:


									'''simple docstring'''

									super().__init__(snake_case_ )
									lowercase       		=		config.num_labels
									lowercase       		=		config.num_hidden_layers

									lowercase       		=		DeeBertModel(snake_case_ )
									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(snake_case_ )
						def 							__a		(						self					:   str				,				__lowerCamelCase					:   Optional[Any]=None				,				__lowerCamelCase					:   Dict=None				,				__lowerCamelCase					:   Tuple=None				,				__lowerCamelCase					:   Dict=None				,				__lowerCamelCase					:   Any=None				,				__lowerCamelCase					:   int=None				,				__lowerCamelCase					:   Union[str, Any]=None				,				__lowerCamelCase					:   Optional[int]=-1				,				__lowerCamelCase					:   List[Any]=False				,				)				->		Optional[int]:


									'''simple docstring'''

									lowercase       		=		self.num_layers
									try:
												lowercase       		=		self.bert(
												    snake_case_				,				attention_mask=snake_case_				,				token_type_ids=snake_case_				,				position_ids=snake_case_				,				head_mask=snake_case_				,				inputs_embeds=snake_case_				,				)
												# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

												lowercase       		=		outputs[1]

												lowercase       		=		self.dropout(snake_case_ )
												lowercase       		=		self.classifier(snake_case_ )
												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(snake_case_ )
												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(snake_case_ )
																		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(snake_case_ )

												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)







"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)

						set_param(
						    torch_layer.self_attention.query_key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  np.asarray(weights[3]						)

						set_param(
						    torch_layer.self_attention.query ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  weights[0][1]
						if len(_lowerCAmelCase						) < 4:
												set_layer_weights_in_torch_lsh(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)
						else:
												set_layer_weights_in_torch_local(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)

						# intermediate weighs
						__lowerCAmelCase     =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 4:
												__lowerCAmelCase     =  intermediate_weights[2]

						# layernorm 2
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][1]						)
						set_param(
						    torch_block.feed_forward.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][1]						)
						set_param(
						    torch_block.feed_forward.dense.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate out
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][1]						)
						set_param(
						    torch_block.feed_forward.output.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

						# word embeds
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						set_param(
						    torch_model_reformer.embeddings.word_embeddings ,    torch.tensor(_lowerCAmelCase						) ,    )

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights						)						):
																		__lowerCAmelCase     =  np.asarray(weights[3][emb_idx][0]						)
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), f"""{position_embeddings[emb_idx]} emb does not match"""
																		__lowerCAmelCase     =  nn.Parameter(torch.tensor(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  weights[5]
						assert len(torch_model_reformer.encoder.layers						) * 4 == len(
						    _lowerCAmelCase						), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# output layer norm
						__lowerCAmelCase     =  np.asarray(weights[7][0]						)
						__lowerCAmelCase     =  np.asarray(weights[7][1]						)
						set_param(
						    torch_model_reformer.encoder.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# output embeddings
						__lowerCAmelCase     =  np.asarray(weights[9][0]						)
						__lowerCAmelCase     =  np.asarray(weights[9][1]						)
						set_param(
						    torch_model.lm_head.decoder ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  pickle.load(_lowerCAmelCase						)["""weights"""]

						set_model_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    config.hidden_size						)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
						torch.save(model.state_dict() ,    _lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
