




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCamelCase__ :   str						=							False
UpperCamelCase__ :   List[str]						=							True
UpperCamelCase__ :   Optional[Any]						=							False


if __name__ == "__main__":
						UpperCamelCase__ :   Union[str, Any]						=							argparse.ArgumentParser()

						parser.add_argument(
						    '''--repo_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the architecture.''',
						)

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						UpperCamelCase__ :   Optional[Any]						=							parser.parse_args()

						UpperCamelCase__ :   Tuple						=							{
						    '''image_size''': '''sample_size''',
						    '''num_res_blocks''': '''layers_per_block''',
						    '''block_channels''': '''block_out_channels''',
						    '''down_blocks''': '''down_block_types''',
						    '''up_blocks''': '''up_block_types''',
						    '''downscale_freq_shift''': '''freq_shift''',
						    '''resnet_num_groups''': '''norm_num_groups''',
						    '''resnet_act_fn''': '''act_fn''',
						    '''resnet_eps''': '''norm_eps''',
						    '''num_head_channels''': '''attention_head_dim''',
						}

						UpperCamelCase__ :   Optional[int]						=							{
						    '''time_steps''': '''time_proj''',
						    '''mid''': '''mid_block''',
						    '''downsample_blocks''': '''down_blocks''',
						    '''upsample_blocks''': '''up_blocks''',
						}

						UpperCamelCase__ :   Tuple						=							'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

						with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
												UpperCamelCase__ :   Optional[int]						=							reader.read()
												UpperCamelCase__ :   Union[str, Any]						=							json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, '''config.json'''):
												UpperCamelCase__ :   Any						=							UNetaDModel(**config)
						else:
												UpperCamelCase__ :   Tuple						=							UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
												UpperCamelCase__ :   List[str]						=							class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						UpperCamelCase__ :   Union[str, Any]						=							dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								UpperCamelCase__ :   List[str]						=							config[key]
																								del config[key]

												UpperCamelCase__ :   Dict						=							[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
												UpperCamelCase__ :   Dict						=							[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

						if do_only_weights:
												UpperCamelCase__ :   int						=							torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

												UpperCamelCase__ :   List[Any]						=							{}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
																								continue
																		UpperCamelCase__ :   str						=							False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split('''.''')[0] == key:
																														UpperCamelCase__ :   Tuple						=							param_value
																														UpperCamelCase__ :   Dict						=							True
																		if not has_changed:
																								UpperCamelCase__ :   str						=							param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))









'''simple docstring'''





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy

@add_start_docstrings(
    """The RoBERTa Model transformer with early exiting (DeeRoBERTa). """   ,   __lowerCAmelCase   ,   )
class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		RobertaConfig
     SCREAMING_SNAKE_CASE_				:       Dict       							=		"""roberta"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          super().__init__(_SCREAMING_SNAKE_CASE       )

          _snake_case				= RobertaEmbeddings(_SCREAMING_SNAKE_CASE       )
          self.init_weights()







@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """   ,   __lowerCAmelCase   ,   )
class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		RobertaConfig
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		"""roberta"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     str:
          super().__init__(_SCREAMING_SNAKE_CASE       )
          _snake_case				= config.num_labels
          _snake_case				= config.num_hidden_layers

          _snake_case				= DeeRobertaModel(_SCREAMING_SNAKE_CASE       )
          _snake_case				= nn.Dropout(config.hidden_dropout_prob       )
          _snake_case				= nn.Linear(config.hidden_size   ,self.config.num_labels       )






     @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE       )
     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=-1   ,_SCREAMING_SNAKE_CASE=False   ,)				->     List[str]:
          _snake_case				= self.num_layers
          try:
               _snake_case				= self.roberta(
                   _SCREAMING_SNAKE_CASE   ,attention_mask=_SCREAMING_SNAKE_CASE   ,token_type_ids=_SCREAMING_SNAKE_CASE   ,position_ids=_SCREAMING_SNAKE_CASE   ,head_mask=_SCREAMING_SNAKE_CASE   ,inputs_embeds=_SCREAMING_SNAKE_CASE   ,)

               _snake_case				= outputs[1]

               _snake_case				= self.dropout(_SCREAMING_SNAKE_CASE       )
               _snake_case				= self.classifier(_SCREAMING_SNAKE_CASE       )
               _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(_SCREAMING_SNAKE_CASE       )
               _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(_SCREAMING_SNAKE_CASE       )
                         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(_SCREAMING_SNAKE_CASE       )

               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), entropy


