





import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__:      Dict									=		logging.get_logger('transformers.models.encodec')

a__:      Any									=		{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
a__:      Dict									=		{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
a__:      Tuple									=		{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
a__:      List[str]									=		{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
a__:      List[Any]									=		{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
a__:      Union[str, Any]									=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__:      Union[str, Any]									=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__:      List[Any]									=		[]
a__:      Any									=		[]


def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       Any ,     UpperCamelCase__			:       Optional[int]  )->List[str]:
       for attribute in key.split('''.'''  ):
              A__											=						getattr(UpperCamelCase__ ,     UpperCamelCase__  )

       if weight_type is not None:
              A__											=						getattr(UpperCamelCase__ ,     UpperCamelCase__  ).shape
       else:
              A__											=						hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
                  f" {value.shape} for {full_name}"  )

       if weight_type == "weight":
              A__											=						value
       elif weight_type == "weight_g":
              A__											=						value
       elif weight_type == "weight_v":
              A__											=						value
       elif weight_type == "bias":
              A__											=						value
       elif weight_type == "running_mean":
              A__											=						value
       elif weight_type == "running_var":
              A__											=						value
       elif weight_type == "num_batches_tracked":
              A__											=						value
       elif weight_type == "weight_ih_l0":
              A__											=						value
       elif weight_type == "weight_hh_l0":
              A__											=						value
       elif weight_type == "bias_ih_l0":
              A__											=						value
       elif weight_type == "bias_hh_l0":
              A__											=						value
       elif weight_type == "weight_ih_l1":
              A__											=						value
       elif weight_type == "weight_hh_l1":
              A__											=						value
       elif weight_type == "bias_ih_l1":
              A__											=						value
       elif weight_type == "bias_hh_l1":
              A__											=						value
       else:
              A__											=						value

       logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."  )


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       int  )->List[Any]:
       for key in ignore_keys:
              if key.endswith('''.*'''  ):
                     if name.startswith(key[:-1]  ):
                            return True
              elif ".*." in key:
                     A__					,	A__											=						key.split('''.*.'''  )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False


def 	UpperCamelCase__(       UpperCamelCase__			:       str ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       Dict  )->Optional[int]:
       A__											=						[]

       if model_name == "encodec_24khz" or "encodec_32khz":
              A__											=						MAPPING_24K
       elif model_name == "encodec_48khz":
              A__											=						MAPPING_48K
       else:
              raise ValueError(f"Unsupported model: {model_name}"  )

       for name, value in orig_dict.items():
              if should_ignore(UpperCamelCase__ ,     UpperCamelCase__  ):
                     logger.info(f"{name} was ignored"  )
                     continue

              A__											=						False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            A__					,	A__											=						key.split('''.*.'''  )
                            if prefix in name and suffix in name:
                                   A__											=						suffix

                     if key in name:
                            # HACK otherwise .embed gets initialized with .embed_avg too
                            if key.endswith('''embed'''  ) and name.endswith('''embed_avg'''  ):
                                   continue

                            A__											=						True
                            if "*" in mapped_key:
                                   A__											=						name.split(UpperCamelCase__  )[0].split('''.'''  )[-2]
                                   A__											=						mapped_key.replace('''*''' ,     UpperCamelCase__  )
                            if "weight_g" in name:
                                   A__											=						'''weight_g'''
                            elif "weight_v" in name:
                                   A__											=						'''weight_v'''
                            elif "weight_ih_l0" in name:
                                   A__											=						'''weight_ih_l0'''
                            elif "weight_hh_l0" in name:
                                   A__											=						'''weight_hh_l0'''
                            elif "bias_ih_l0" in name:
                                   A__											=						'''bias_ih_l0'''
                            elif "bias_hh_l0" in name:
                                   A__											=						'''bias_hh_l0'''
                            elif "weight_ih_l1" in name:
                                   A__											=						'''weight_ih_l1'''
                            elif "weight_hh_l1" in name:
                                   A__											=						'''weight_hh_l1'''
                            elif "bias_ih_l1" in name:
                                   A__											=						'''bias_ih_l1'''
                            elif "bias_hh_l1" in name:
                                   A__											=						'''bias_hh_l1'''
                            elif "bias" in name:
                                   A__											=						'''bias'''
                            elif "weight" in name:
                                   A__											=						'''weight'''
                            elif "running_mean" in name:
                                   A__											=						'''running_mean'''
                            elif "running_var" in name:
                                   A__											=						'''running_var'''
                            elif "num_batches_tracked" in name:
                                   A__											=						'''num_batches_tracked'''
                            else:
                                   A__											=						None
                            set_recursively(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     continue
              if not is_used:
                     unused_weights.append(UpperCamelCase__  )

       logger.warning(f"Unused weights: {unused_weights}"  )


@torch.no_grad()
def 	UpperCamelCase__(       UpperCamelCase__			:       Union[str, Any] ,     UpperCamelCase__			:       Optional[int] ,     UpperCamelCase__			:       Any ,     UpperCamelCase__			:       Optional[Any]=None ,     UpperCamelCase__			:       str=None ,     )->List[str]:
       if config_path is not None:
              A__											=						EncodecConfig.from_pretrained(UpperCamelCase__  )
       else:
              A__											=						EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              A__											=						[8, 5, 4, 4]
              A__											=						[2.2]
              A__											=						64
              A__											=						3_20_00
              A__											=						20_48
              A__											=						False
              A__											=						False
              A__											=						False
       elif model_name == "encodec_48khz":
              A__											=						[8, 5, 4, 2]
              A__											=						[3.0, 6.0, 12.0, 24.0]
              A__											=						4_80_00
              A__											=						2
              A__											=						False
              A__											=						'''time_group_norm'''
              A__											=						True
              A__											=						1.0
              A__											=						0.01
       else:
              raise ValueError(f"Unknown model name: {model_name}"  )

       A__											=						EncodecModel(UpperCamelCase__  )

       A__											=						EncodecFeatureExtractor(
           feature_size=config.audio_channels ,     sampling_rate=config.sampling_rate ,     chunk_length_s=config.chunk_length_s ,     overlap=config.overlap ,     )
       feature_extractor.save_pretrained(UpperCamelCase__  )

       A__											=						torch.load(UpperCamelCase__  )
       if "best_state" in original_checkpoint:
              # we might have a training state saved, in which case discard the yaml results and just retain the weights
              A__											=						original_checkpoint['''best_state''']
       recursively_load_weights(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
       model.save_pretrained(UpperCamelCase__  )

       if repo_id:
              print('''Pushing to the hub...'''  )
              feature_extractor.push_to_hub(UpperCamelCase__  )
              model.push_to_hub(UpperCamelCase__  )


if __name__ == "__main__":
       a__:      Tuple									=		argparse.ArgumentParser()
       parser.add_argument(
           '--model',
           default='encodec_24khz',
           type=str,
           help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
       )
       parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
       )

       a__:      int									=		parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )












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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


a__:      int									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None,__lowerCamelCase=None ):
        A__											=						self.layer[current_layer](__lowerCamelCase,__lowerCamelCase,head_mask[current_layer] )

        A__											=						layer_outputs[0]

        return hidden_states




@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )

        A__											=						BertEncoderWithPabee(__lowerCamelCase )

        self.init_weights()
        A__											=						0
        A__											=						0
        A__											=						0

        A__											=						0



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						threshold



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						patience



 def 				UpperCamelCase			(							self ):
        A__											=						0
        A__											=						0



 def 				UpperCamelCase			(							self ):
        A__											=						self.inference_layers_num / self.inference_instances_num
        A__											=						(
            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(__lowerCamelCase )




 @add_start_docstrings_to_model_forward(__lowerCamelCase )
 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=False,):

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

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

        if attention_mask is None:
               A__											=						torch.ones(__lowerCamelCase,device=__lowerCamelCase )
        if token_type_ids is None:
               A__											=						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.
        A__											=						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 self.config.is_decoder and encoder_hidden_states is not None:
               A__					,	A__					,	A__											=						encoder_hidden_states.size()
               A__											=						(encoder_batch_size, encoder_sequence_length)
               if encoder_attention_mask is None:
                      A__											=						torch.ones(__lowerCamelCase,device=__lowerCamelCase )
               A__											=						self.invert_attention_mask(__lowerCamelCase )
        else:
               A__											=						None

        # 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]
        A__											=						self.get_head_mask(__lowerCamelCase,self.config.num_hidden_layers )

        A__											=						self.embeddings(
            input_ids=__lowerCamelCase,position_ids=__lowerCamelCase,token_type_ids=__lowerCamelCase,inputs_embeds=__lowerCamelCase )
        A__											=						embedding_output

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

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

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

                      A__											=						logits
                      if patient_counter == self.patience:
                             break
               A__											=						[patient_result]
               self.inference_layers_num += calculated_layer_num
               self.inference_instances_num += 1

        return res




@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )
        A__											=						config.num_labels

        A__											=						BertModelWithPabee(__lowerCamelCase )
        A__											=						nn.Dropout(config.hidden_dropout_prob )
        A__											=						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(__lowerCamelCase )
 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,):
        A__											=						self.bert(
            input_ids=__lowerCamelCase,attention_mask=__lowerCamelCase,token_type_ids=__lowerCamelCase,position_ids=__lowerCamelCase,head_mask=__lowerCamelCase,inputs_embeds=__lowerCamelCase,output_dropout=self.dropout,output_layers=self.classifiers,regression=self.num_labels == 1,)

        A__											=						(logits[-1],)

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

        return outputs





