






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


a_		:    Optional[Any]   							=     logging.get_logger(__name__)

a_		:    Tuple   							=     {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class 		_snake_case	(	A__       ):
			_lowercase		:     Optional[int]        =				'''dpt'''






			def __init__(							self		,       a=768		,       a=12		,       a=12		,       a=3072		,       a="gelu"		,       a=0.0		,       a=0.0		,       a=0.02		,       a=1E-12		,       a=384		,       a=16		,       a=3		,       a=False		,       a=True		,       a=[2, 5, 8, 11]		,       a="project"		,       a=[4, 2, 1, 0.5]		,       a=[96, 192, 384, 768]		,       a=256		,       a=-1		,       a=False		,       a=True		,       a=0.4		,       a=255		,       a=0.1		,       a=[1, 1024, 24, 24]		,       a=[0, 1]		,       a=None		,       **a		,       )   ->							List[str]:
						super().__init__(**a)

						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			is_hybrid

						if self.is_hybrid:
									if backbone_config is None:
												logger.info('Initializing the config with a `BiT` backbone.')
												SCREAMING_SNAKE_CASE							=			{
												    'global_padding': 'same',
												    'layer_type': 'bottleneck',
												    'depths': [3, 4, 9],
												    'out_features': ['stage1', 'stage2', 'stage3'],
												    'embedding_dynamic_padding': True,
												}
												SCREAMING_SNAKE_CASE							=			BitConfig(**a)
									elif isinstance(a		,       a):
												logger.info('Initializing the config with a `BiT` backbone.')
												SCREAMING_SNAKE_CASE							=			BitConfig(**a)
									elif isinstance(a		,       a):
												SCREAMING_SNAKE_CASE							=			backbone_config
									else:
												raise ValueError(
												    f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''')

									SCREAMING_SNAKE_CASE							=			backbone_featmap_shape
									SCREAMING_SNAKE_CASE							=			neck_ignore_stages

									if readout_type != "project":
												raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.')
						else:
									SCREAMING_SNAKE_CASE							=			None
									SCREAMING_SNAKE_CASE							=			None
									SCREAMING_SNAKE_CASE							=			[]

						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			intermediate_size
						SCREAMING_SNAKE_CASE							=			hidden_act
						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			layer_norm_eps
						SCREAMING_SNAKE_CASE							=			image_size
						SCREAMING_SNAKE_CASE							=			patch_size
						SCREAMING_SNAKE_CASE							=			num_channels
						SCREAMING_SNAKE_CASE							=			qkv_bias
						SCREAMING_SNAKE_CASE							=			backbone_out_indices
						if readout_type not in ["ignore", "add", "project"]:
									raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']')
						SCREAMING_SNAKE_CASE							=			readout_type
						SCREAMING_SNAKE_CASE							=			reassemble_factors
						SCREAMING_SNAKE_CASE							=			neck_hidden_sizes
						SCREAMING_SNAKE_CASE							=			fusion_hidden_size
						SCREAMING_SNAKE_CASE							=			head_in_index
						SCREAMING_SNAKE_CASE							=			use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						SCREAMING_SNAKE_CASE							=			use_auxiliary_head
						SCREAMING_SNAKE_CASE							=			auxiliary_loss_weight
						SCREAMING_SNAKE_CASE							=			semantic_loss_ignore_index
						SCREAMING_SNAKE_CASE							=			semantic_classifier_dropout







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			copy.deepcopy(self.__dict__)

						if output["backbone_config"] is not None:
									SCREAMING_SNAKE_CASE							=			self.backbone_config.to_dict()

						SCREAMING_SNAKE_CASE							=			self.__class__.model_type
						return output











'''simple docstring'''




import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : str         =       [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

     if k.startswith('encoder'     ):
          __A		:					Any						=			k.replace('.attn'					,				'.self_attn'     )
          __A		:					Any						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          __A		:					Tuple						=			k.replace('norm1'					,				'self_attn_layer_norm'     )
          __A		:					str						=			k.replace('norm2'					,				'encoder_attn_layer_norm'     )
          __A		:					int						=			k.replace('norm3'					,				'final_layer_norm'     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					Optional[int]						=			[
         'model.encoder.layernorm_embedding.weight',
         'model.encoder.layernorm_embedding.bias',
         'model.decoder.layernorm_embedding.weight',
         'model.decoder.layernorm_embedding.bias',
     ]
     for k in keys:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					List[Any]     )	->	int:
     __A		:					List[str]						=			torch.load(__snake_case					,				map_location='cpu'     )
     __A		:					Tuple						=			model['model']
     __A		:					str						=			BlenderbotConfig.from_json_file(__snake_case     )
     __A		:					int						=			BlenderbotForConditionalGeneration(__snake_case     )
     __A		:					List[Any]						=			m.model.state_dict().keys()
     __A		:					Optional[int]						=			[]
     __A		:					Optional[int]						=			{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __A		:					Union[str, Any]						=			rename_state_dict_key(__snake_case     )
          if new_k not in valid_keys:
               failures.append([k, new_k]     )
          else:
               __A		:					Optional[Any]						=			v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__snake_case     )
     m.model.load_state_dict(__snake_case					,				strict=__snake_case     )
     m.half()
     m.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
  parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
  parser.add_argument(
      '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)