


from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase      =   logging.get_logger(__name__)






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      try:
            with open(SCREAMING_SNAKE_CASE , '''rb''' ) as flax_state_f:
                  snake_case_	=				from_bytes(SCREAMING_SNAKE_CASE , flax_state_f.read() )
      except UnpicklingError as e:
            try:
                  with open(SCREAMING_SNAKE_CASE ) as f:
                        if f.read().startswith('''version''' ):
                              raise OSError(
                                  '''You seem to have cloned a repository without having git-lfs installed. Please'''
                                  ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                                  ''' folder you cloned.''' )
                        else:
                              raise ValueError from e
            except (UnicodeDecodeError, ValueError):
                  raise EnvironmentError(f'''Unable to convert {model_file} to Flax deserializable object. ''' )

      return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

      try:
            import torch  # noqa: F401
      except ImportError:
            logger.error(
                '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
                ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
                ''' instructions.''' )
            raise

      # check if we have bf16 weights
      snake_case_	=				flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE : x.dtype == jnp.bfloataa , SCREAMING_SNAKE_CASE ) ).values()
      if any(SCREAMING_SNAKE_CASE ):
            # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

            # and bf16 is not fully supported in PT yet.
            logger.warning(
                '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
                '''before loading those in PyTorch model.''' )
            snake_case_	=				jax.tree_util.tree_map(
                lambda SCREAMING_SNAKE_CASE : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , SCREAMING_SNAKE_CASE )

      snake_case_	=				''''''

      snake_case_	=				flatten_dict(SCREAMING_SNAKE_CASE , sep='''.''' )
      snake_case_	=				pt_model.state_dict()

      # keep track of unexpected & missing keys
      snake_case_	=				[]
      snake_case_	=				set(pt_model_dict.keys() )

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            snake_case_	=				flax_key_tuple.split('''.''' )

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  snake_case_	=				flax_key_tuple_array[:-1] + ['''weight''']
                  snake_case_	=				jnp.transpose(SCREAMING_SNAKE_CASE , (3, 2, 0, 1) )
            elif flax_key_tuple_array[-1] == "kernel":
                  snake_case_	=				flax_key_tuple_array[:-1] + ['''weight''']
                  snake_case_	=				flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  snake_case_	=				flax_key_tuple_array[:-1] + ['''weight''']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE ):
                        snake_case_	=				(
                            flax_key_tuple_string.replace('''_0''' , '''.0''' )
                            .replace('''_1''' , '''.1''' )
                            .replace('''_2''' , '''.2''' )
                            .replace('''_3''' , '''.3''' )
                            .replace('''_4''' , '''.4''' )
                            .replace('''_5''' , '''.5''' )
                            .replace('''_6''' , '''.6''' )
                            .replace('''_7''' , '''.7''' )
                            .replace('''_8''' , '''.8''' )
                            .replace('''_9''' , '''.9''' )
                        )

            snake_case_	=				'''.'''.join(SCREAMING_SNAKE_CASE )

            if flax_key in pt_model_dict:
                  if flax_tensor.shape != pt_model_dict[flax_key].shape:
                        raise ValueError(
                            f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
                            f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.''' )
                  else:
                        # add weight to pytorch dict
                        snake_case_	=				np.asarray(SCREAMING_SNAKE_CASE ) if not isinstance(SCREAMING_SNAKE_CASE , np.ndarray ) else flax_tensor
                        snake_case_	=				torch.from_numpy(SCREAMING_SNAKE_CASE )
                        # remove from missing keys
                        missing_keys.remove(SCREAMING_SNAKE_CASE )
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(SCREAMING_SNAKE_CASE )

      pt_model.load_state_dict(SCREAMING_SNAKE_CASE )

      # re-transform missing_keys to list
      snake_case_	=				list(SCREAMING_SNAKE_CASE )

      if len(SCREAMING_SNAKE_CASE ) > 0:
            logger.warning(
                '''Some weights of the Flax model were not used when initializing the PyTorch model'''
                f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
                f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
                ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
                f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
                ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
                ''' FlaxBertForSequenceClassification model).''' )
      if len(SCREAMING_SNAKE_CASE ) > 0:
            logger.warning(
                f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
                f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
                ''' use it for predictions and inference.''' )

      return pt_model



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 )->  List[Any]:




      """simple docstring"""

      snake_case_	=				torch.exp(SCREAMING_SNAKE_CASE )
      snake_case_	=				torch.sum(SCREAMING_SNAKE_CASE , dim=1 )  # sum of exp(x_i)
      snake_case_	=				torch.sum(x * exp_x , dim=1 )  # sum of x_i * exp(x_i)
      return torch.log(SCREAMING_SNAKE_CASE ) - B / A







class lowerCAmelCase_						(			nn.Module	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__()
       snake_case_	=				config.output_attentions
       snake_case_	=				config.output_hidden_states
       snake_case_	=				nn.ModuleList([BertLayer(_UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )
       snake_case_	=				nn.ModuleList([BertHighway(_UpperCAmelCase ) for _ in range(config.num_hidden_layers )] )

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



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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				pooler.state_dict()
       for highway in self.highway:
             for name, param in highway.pooler.state_dict().items():
                   param.copy_(loaded_model[name] )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				):
       snake_case_	=				()
       snake_case_	=				()
       snake_case_	=				()
       for i, layer_module in enumerate(self.layer ):
             if self.output_hidden_states:
                   snake_case_	=				all_hidden_states + (hidden_states,)

             snake_case_	=				layer_module(
                 _UpperCAmelCase		,				_UpperCAmelCase		,				head_mask[i]		,				_UpperCAmelCase		,				_UpperCAmelCase )
             snake_case_	=				layer_outputs[0]

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

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

             snake_case_	=				self.highway[i](_UpperCAmelCase )
             # logits, pooled_output

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

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

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

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

       snake_case_	=				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). " ,						lowerCamelCase__ ,						)
class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__(_UpperCAmelCase )
       snake_case_	=				config

       snake_case_	=				BertEmbeddings(_UpperCAmelCase )
       snake_case_	=				DeeBertEncoder(_UpperCAmelCase )
       snake_case_	=				BertPooler(_UpperCAmelCase )

       self.init_weights()



 def 						UpperCamelCase__					(     self ):
       self.encoder.init_highway_pooler(self.pooler )



 def 						UpperCamelCase__					(     self ):
       return self.embeddings.word_embeddings



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				value



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





 @add_start_docstrings_to_model_forward(_UpperCAmelCase )
 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=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:
             snake_case_	=				input_ids.size()
       elif inputs_embeds is not None:
             snake_case_	=				inputs_embeds.size()[:-1]
       else:
             raise ValueError('''You have to specify either input_ids or inputs_embeds''' )

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

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

       # 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.
       snake_case_	=				self.get_extended_attention_mask(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # 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:
             snake_case_	=				encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
             snake_case_	=				encoder_attention_mask[:, None, None, :]

       snake_case_	=				encoder_extended_attention_mask.to(
           dtype=next(self.parameters() ).dtype )  # fp16 compatibility
       snake_case_	=				(1.0 - encoder_extended_attention_mask) * -10_000.0

       # 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]
       snake_case_	=				self.get_head_mask(_UpperCAmelCase		,				self.config.num_hidden_layers )

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

       snake_case_	=				(
           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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				message
       snake_case_	=				exit_layer  # start from 1!







class lowerCAmelCase_						(			nn.Module	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__()
       snake_case_	=				BertPooler(_UpperCAmelCase )
       snake_case_	=				nn.Dropout(config.hidden_dropout_prob )
       snake_case_	=				nn.Linear(config.hidden_size		,				config.num_labels )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       # Pooler
       snake_case_	=				encoder_outputs[0]
       snake_case_	=				self.pooler(_UpperCAmelCase )
       # "return" pooler_output

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

       # Dropout and classification
       snake_case_	=				bmodel_output[1]

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

       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. " ,						lowerCamelCase__ ,						)
class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase ):
       super().__init__(_UpperCAmelCase )
       snake_case_	=				config.num_labels
       snake_case_	=				config.num_hidden_layers

       snake_case_	=				DeeBertModel(_UpperCAmelCase )
       snake_case_	=				nn.Dropout(config.hidden_dropout_prob )
       snake_case_	=				nn.Linear(config.hidden_size		,				self.config.num_labels )

       self.init_weights()





 @add_start_docstrings_to_model_forward(_UpperCAmelCase )
 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=-1		,				_UpperCAmelCase=False		,				):
       snake_case_	=				self.num_layers
       try:
             snake_case_	=				self.bert(
                 _UpperCAmelCase		,				attention_mask=_UpperCAmelCase		,				token_type_ids=_UpperCAmelCase		,				position_ids=_UpperCAmelCase		,				head_mask=_UpperCAmelCase		,				inputs_embeds=_UpperCAmelCase		,				)
             # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

             snake_case_	=				outputs[1]

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

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

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

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