"""simple docstring"""




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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      List[Any]:
  _lowerCAmelCase						:       Optional[int]  			=     torch.exp(__lowercase	)
  _lowerCAmelCase						:       Union[str, Any]  			=     torch.sum(__lowercase			,dim=1	)  # sum of exp(x_i)
  _lowerCAmelCase						:       Tuple  			=     torch.sum(x * exp_x			,dim=1	)  # sum of x_i * exp(x_i)
  return torch.log(__lowercase	) - B / A
class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Any  			=     config.output_attentions
       _lowerCAmelCase						:       Any  			=     config.output_hidden_states
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.ModuleList([BertLayer(__A  ) for _ in range(config.num_hidden_layers  )]  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.ModuleList([BertHighway(__A  ) for _ in range(config.num_hidden_layers  )]  )

       _lowerCAmelCase						:       Any  			=     [-1 for _ in range(config.num_hidden_layers  )]






     def     __A  (		self				,     a__  ):
       if (type(__A  ) is float) or (type(__A  ) is int):
         for i in range(len(self.early_exit_entropy  )  ):
           _lowerCAmelCase						:       Union[str, Any]  			=     x
       else:
         _lowerCAmelCase						:       List[Any]  			=     x






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     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				,     a__				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     ):
       _lowerCAmelCase						:       Any  			=     ()
       _lowerCAmelCase						:       Tuple  			=     ()
       _lowerCAmelCase						:       List[Any]  			=     ()
       for i, layer_module in enumerate(self.layer  ):
         if self.output_hidden_states:
           _lowerCAmelCase						:       Any  			=     all_hidden_states + (hidden_states,)

         _lowerCAmelCase						:       Tuple  			=     layer_module(
             __A				,     __A				,     head_mask[i]				,     __A				,     __A  )
         _lowerCAmelCase						:       Optional[int]  			=     layer_outputs[0]

         if self.output_attentions:
           _lowerCAmelCase						:       Union[str, Any]  			=     all_attentions + (layer_outputs[1],)

         _lowerCAmelCase						:       Tuple  			=     (hidden_states,)
         if self.output_hidden_states:
           _lowerCAmelCase						:       List[Any]  			=     current_outputs + (all_hidden_states,)
         if self.output_attentions:
           _lowerCAmelCase						:       str  			=     current_outputs + (all_attentions,)

         _lowerCAmelCase						:       Tuple  			=     self.highway[i](__A  )
         # logits, pooled_output

         if not self.training:
           _lowerCAmelCase						:       Union[str, Any]  			=     highway_exit[0]
           _lowerCAmelCase						:       int  			=     entropy(__A  )
           _lowerCAmelCase						:       Optional[int]  			=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
           _lowerCAmelCase						:       Union[str, Any]  			=     all_highway_exits + (highway_exit,)

           if highway_entropy < self.early_exit_entropy[i]:
             _lowerCAmelCase						:       List[Any]  			=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
             raise HighwayException(__A				,     i + 1  )
         else:
           _lowerCAmelCase						:       str  			=     all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
         _lowerCAmelCase						:       Optional[int]  			=     all_hidden_states + (hidden_states,)

       _lowerCAmelCase						:       Optional[int]  			=     (hidden_states,)
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[str]  			=     outputs + (all_hidden_states,)
       if self.output_attentions:
         _lowerCAmelCase						:       List[Any]  			=     outputs + (all_attentions,)

       _lowerCAmelCase						:       Dict  			=     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 						__A							( __lowerCamelCase		):






     def __init__(		self				,     a__  ):
       super().__init__(__A  )
       _lowerCAmelCase						:       str  			=     config

       _lowerCAmelCase						:       Tuple  			=     BertEmbeddings(__A  )
       _lowerCAmelCase						:       Optional[Any]  			=     DeeBertEncoder(__A  )
       _lowerCAmelCase						:       Optional[int]  			=     BertPooler(__A  )

       self.init_weights()






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






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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     value






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






     @add_start_docstrings_to_model_forward(__A  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=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:
         _lowerCAmelCase						:       str  			=     input_ids.size()
       elif inputs_embeds is not None:
         _lowerCAmelCase						:       str  			=     inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

       _lowerCAmelCase						:       Union[str, Any]  			=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
         _lowerCAmelCase						:       Dict  			=     torch.ones(__A				,     device=__A  )
       if encoder_attention_mask is None:
         _lowerCAmelCase						:       List[str]  			=     torch.ones(__A				,     device=__A  )
       if token_type_ids is None:
         _lowerCAmelCase						:       List[str]  			=     torch.zeros(__A				,     dtype=torch.long				,     device=__A  )

       # 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.
       _lowerCAmelCase						:       Dict  			=     self.get_extended_attention_mask(__A				,     __A				,     __A  )

       # 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:
         _lowerCAmelCase						:       Any  			=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
         _lowerCAmelCase						:       List[str]  			=     encoder_attention_mask[:, None, None, :]

       _lowerCAmelCase						:       List[str]  			=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
       _lowerCAmelCase						:       Optional[int]  			=     (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]
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_head_mask(__A				,     self.config.num_hidden_layers  )

       _lowerCAmelCase						:       List[str]  			=     self.embeddings(
           input_ids=__A				,     position_ids=__A				,     token_type_ids=__A				,     inputs_embeds=__A  )
       _lowerCAmelCase						:       Optional[Any]  			=     self.encoder(
           __A				,     attention_mask=__A				,     head_mask=__A				,     encoder_hidden_states=__A				,     encoder_attention_mask=__A				,     )
       _lowerCAmelCase						:       int  			=     encoder_outputs[0]
       _lowerCAmelCase						:       Optional[int]  			=     self.pooler(__A  )

       _lowerCAmelCase						:       Tuple  			=     (
           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 						__A							( __lowerCamelCase		):






     def __init__(		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     message
       _lowerCAmelCase						:       Optional[int]  			=     exit_layer  # start from 1!







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       List[Any]  			=     BertPooler(__A  )
       _lowerCAmelCase						:       Optional[int]  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       Any  			=     nn.Linear(config.hidden_size				,     config.num_labels  )






     def     __A  (		self				,     a__  ):
       # Pooler
       _lowerCAmelCase						:       Tuple  			=     encoder_outputs[0]
       _lowerCAmelCase						:       Optional[int]  			=     self.pooler(__A  )
       # "return" pooler_output

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

       # Dropout and classification
       _lowerCAmelCase						:       str  			=     bmodel_output[1]

       _lowerCAmelCase						:       List[str]  			=     self.dropout(__A  )
       _lowerCAmelCase						:       Dict  			=     self.classifier(__A  )

       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 						__A							( __lowerCamelCase		):






     def __init__(		self				,     a__  ):
       super().__init__(__A  )
       _lowerCAmelCase						:       Dict  			=     config.num_labels
       _lowerCAmelCase						:       Tuple  			=     config.num_hidden_layers

       _lowerCAmelCase						:       Tuple  			=     DeeBertModel(__A  )
       _lowerCAmelCase						:       Optional[int]  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Linear(config.hidden_size				,     self.config.num_labels  )

       self.init_weights()






     @add_start_docstrings_to_model_forward(__A  )
     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     a__=-1				,     a__=False				,     ):
       _lowerCAmelCase						:       Dict  			=     self.num_layers
       try:
         _lowerCAmelCase						:       Tuple  			=     self.bert(
             __A				,     attention_mask=__A				,     token_type_ids=__A				,     position_ids=__A				,     head_mask=__A				,     inputs_embeds=__A				,     )
         # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

         _lowerCAmelCase						:       int  			=     outputs[1]

         _lowerCAmelCase						:       Any  			=     self.dropout(__A  )
         _lowerCAmelCase						:       int  			=     self.classifier(__A  )
         _lowerCAmelCase						:       Optional[int]  			=     (logits,) + outputs[2:]  # add hidden states and attention if they are here
       except HighwayException as e:
         _lowerCAmelCase						:       Optional[Any]  			=     e.message
         _lowerCAmelCase						:       List[str]  			=     e.exit_layer
         _lowerCAmelCase						:       Dict  			=     outputs[0]

       if not self.training:
         _lowerCAmelCase						:       List[str]  			=     entropy(__A  )
         _lowerCAmelCase						:       Union[str, Any]  			=     []
         _lowerCAmelCase						:       List[str]  			=     []
       if labels is not None:
         if self.num_labels == 1:
           #  We are doing regression
           _lowerCAmelCase						:       Tuple  			=     MSELoss()
           _lowerCAmelCase						:       int  			=     loss_fct(logits.view(-1  )				,     labels.view(-1  )  )
         else:
           _lowerCAmelCase						:       List[Any]  			=     CrossEntropyLoss()
           _lowerCAmelCase						:       Any  			=     loss_fct(logits.view(-1				,     self.num_labels  )				,     labels.view(-1  )  )

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

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

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Tuple				,  __lowercase						:  Tuple     ) ->      Tuple:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  List[str]="attention"     ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  k_tmp.reshape(k_tmp.shape[0]				,  k_tmp.shape[1] * k_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,  o_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  q_tmp.reshape(q_tmp.shape[0]				,  q_tmp.shape[1] * q_tmp.shape[2]     )
       __UpperCamelCase       =  np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
       __UpperCamelCase       =  v_tmp.reshape(v_tmp.shape[0]				,  v_tmp.shape[1] * v_tmp.shape[2]     )
       return k, o, q, v
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict				,  __lowercase						:  int				,  __lowercase						:  List[Any]=False     ) ->      Optional[Any]:



       """simple docstring"""






       if split_mlp_wi:
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
              __UpperCamelCase       =  (wi_a, wi_a)
       else:
              __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

       __UpperCamelCase       =  params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
       return wi, wo
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[Any]				,  __lowercase						:  List[str]				,  __lowercase						:  Optional[int]     ) ->      str:



       """simple docstring"""






       return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def 				lowercase__   (   __lowercase						:  dict				,  *, __lowercase						:  int				,  __lowercase						:  bool				,  __lowercase						:  bool = False     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  traverse_util.flatten_dict(variables['target']     )
       __UpperCamelCase       =  {'/'.join(__lowercase     ): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       __UpperCamelCase       =  'encoder/encoder/mlp/wi_0/kernel' in old
       print('Split MLP:'				,  __lowercase     )

       __UpperCamelCase       =  collections.OrderedDict()

       # Shared embeddings.
       __UpperCamelCase       =  old['token_embedder/embedding']

       # Encoder.
       for i in range(__lowercase     ):
              # Block i, layer 0 (Self Attention).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_attention_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'attention'     )
              __UpperCamelCase       =  layer_norm
              __UpperCamelCase       =  k.T
              __UpperCamelCase       =  o.T
              __UpperCamelCase       =  q.T
              __UpperCamelCase       =  v.T

              # Block i, layer 1 (MLP).
              __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'encoder'				,  'pre_mlp_layer_norm'     )
              __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'encoder'				,  __lowercase     )
              __UpperCamelCase       =  layer_norm
              if split_mlp_wi:
                     __UpperCamelCase       =  wi[0].T
                     __UpperCamelCase       =  wi[1].T
              else:
                     __UpperCamelCase       =  wi.T
              __UpperCamelCase       =  wo.T
              if scalable_attention:
                     # convert the rel_embedding of each layer
                     __UpperCamelCase       =  tax_relpos_bias_lookup(
                         __lowercase				,  __lowercase				,  'encoder'     ).T

       __UpperCamelCase       =  old['encoder/encoder_norm/scale']

       if not scalable_attention:
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'encoder'     ).T
              __UpperCamelCase       =  tax_relpos_bias_lookup(
                  __lowercase				,  0				,  'decoder'     ).T

       if not is_encoder_only:
              # Decoder.
              for i in range(__lowercase     ):
                     # Block i, layer 0 (Self Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_self_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'self_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 1 (Cross Attention).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_cross_attention_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  tax_attention_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'encoder_decoder_attention'     )
                     __UpperCamelCase       =  layer_norm
                     __UpperCamelCase       =  k.T
                     __UpperCamelCase       =  o.T
                     __UpperCamelCase       =  q.T
                     __UpperCamelCase       =  v.T

                     # Block i, layer 2 (MLP).
                     __UpperCamelCase       =  tax_layer_norm_lookup(__lowercase				,  __lowercase				,  'decoder'				,  'pre_mlp_layer_norm'     )
                     __UpperCamelCase       ,						__UpperCamelCase       =  tax_mlp_lookup(__lowercase				,  __lowercase				,  'decoder'				,  __lowercase     )
                     __UpperCamelCase       =  layer_norm
                     if split_mlp_wi:
                            __UpperCamelCase       =  wi[0].T
                            __UpperCamelCase       =  wi[1].T
                     else:
                            __UpperCamelCase       =  wi.T
                     __UpperCamelCase       =  wo.T

                     if scalable_attention:
                            # convert the rel_embedding of each layer
                            __UpperCamelCase       =  tax_relpos_bias_lookup(__lowercase				,  __lowercase				,  'decoder'     ).T

              __UpperCamelCase       =  old['decoder/decoder_norm/scale']

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     __UpperCamelCase       =  old['decoder/logits_dense/kernel'].T

       return new
def 				lowercase__   (   __lowercase						:  Optional[Any]				,  __lowercase						:  bool     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              __UpperCamelCase       =  state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     __UpperCamelCase       =  state_dict['shared.weight']

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'     )
                     __UpperCamelCase       =  state_dict['shared.weight']

       return state_dict
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Dict				,  __lowercase						:  str				,  __lowercase						:  int				,  __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  checkpoints.load_tax_checkpoint(__lowercase     )
       __UpperCamelCase       =  convert_tax_to_pytorch(
           __lowercase				,  num_layers=config.num_layers				,  is_encoder_only=__lowercase				,  scalable_attention=__lowercase     )
       __UpperCamelCase       =  make_state_dict(__lowercase				,  __lowercase     )
       model.load_state_dict(__lowercase				,  strict=__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Dict				,  __lowercase						:  List[str]				,  __lowercase						:  bool = False				,  __lowercase						:  bool = False				,  ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  MTaConfig.from_json_file(__lowercase     )
       print(F'''Building PyTorch model from configuration: {config}'''     )
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              __UpperCamelCase       =  UMTaEncoderModel(__lowercase     )
       else:
              __UpperCamelCase       =  UMTaForConditionalGeneration(__lowercase     )

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(__lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase     )

       # Save pytorch-model
       print(F'''Save PyTorch model to {pytorch_dump_path}'''     )
       model.save_pretrained(__lowercase     )

       # Verify that we can load the checkpoint.
       model.from_pretrained(__lowercase     )
       print('Done'     )


if __name__ == "__main__":
    a__							:							List[Any] =argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    parser.add_argument(
        '''--scalable_attention''',
        action='''store_true''',
        help='''Whether the model uses scaled attention (umt5 model)''',
        default=False,
    )
    a__							:							List[str] =parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )
