



import argparse

import torch

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


logging.set_verbosity_info()
lowercase_													=      logging.get_logger(__name__)

lowercase_													=      [
    ['''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		(	__lowerCamelCase						:     str						)	->    str:
					if k == "embeddings.weight":
										return "shared.weight"

					for parlai_name, hf_name in PATTERNS:
										__lowerCAmelCase						 =k.replace(__lowerCamelCase					, __lowerCamelCase						)

					if k.startswith("""encoder"""						):
										__lowerCAmelCase						 =k.replace(""".attn"""					, """.self_attn"""						)
										__lowerCAmelCase						 =k.replace("""norm1"""					, """self_attn_layer_norm"""						)
										__lowerCAmelCase						 =k.replace("""norm2"""					, """final_layer_norm"""						)
					elif k.startswith("""decoder"""						):
										__lowerCAmelCase						 =k.replace("""norm1"""					, """self_attn_layer_norm"""						)
										__lowerCAmelCase						 =k.replace("""norm2"""					, """encoder_attn_layer_norm"""						)
										__lowerCAmelCase						 =k.replace("""norm3"""					, """final_layer_norm"""						)
					return k






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]						)	->    Optional[int]:
					__lowerCAmelCase						 =[
					    """model.encoder.layernorm_embedding.weight""",
					    """model.encoder.layernorm_embedding.bias""",
					    """model.decoder.layernorm_embedding.weight""",
					    """model.decoder.layernorm_embedding.bias""",
					]
					for k in keys:
										__lowerCAmelCase						 =sd.pop(__lowerCamelCase						)
										__lowerCAmelCase						 =k.replace("""layernorm_embedding"""					, """layer_norm"""						)
										assert new_k not in sd
										__lowerCAmelCase						 =v


lowercase_													=      ['''START''']






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

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


if __name__ == "__main__":
				lowercase_													=      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_													=      parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"distilbert"
	SCREAMING_SNAKE_CASE  	=							{
	    "hidden_size": "dim",
	    "num_attention_heads": "n_heads",
	    "num_hidden_layers": "n_layers",
	}



	def __init__(    self				:     Optional[Any]					,						snake_case_				:     Tuple=3_05_22					,						snake_case_				:     Any=5_12					,						snake_case_				:     Dict=False					,						snake_case_				:     Tuple=6					,						snake_case_				:     Union[str, Any]=12					,						snake_case_				:     List[str]=7_68					,						snake_case_				:     Optional[Any]=4 * 7_68					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     Dict=0.1					,						snake_case_				:     Union[str, Any]="gelu"					,						snake_case_				:     Any=0.0_2					,						snake_case_				:     Optional[Any]=0.1					,						snake_case_				:     List[Any]=0.2					,						snake_case_				:     Optional[Any]=0					,						**snake_case_				:     List[Any]					,						)->				Tuple:
						__lowerCAmelCase						 =vocab_size
						__lowerCAmelCase						 =max_position_embeddings
						__lowerCAmelCase						 =sinusoidal_pos_embds
						__lowerCAmelCase						 =n_layers
						__lowerCAmelCase						 =n_heads
						__lowerCAmelCase						 =dim
						__lowerCAmelCase						 =hidden_dim
						__lowerCAmelCase						 =dropout
						__lowerCAmelCase						 =attention_dropout
						__lowerCAmelCase						 =activation
						__lowerCAmelCase						 =initializer_range
						__lowerCAmelCase						 =qa_dropout
						__lowerCAmelCase						 =seq_classif_dropout
						super().__init__(**snake_case_					,						pad_token_id=snake_case_)






class     __a			(  SCREAMING_SNAKE_CASE					):



	@property
	def 		UpperCamelCase				(    self				:     Union[str, Any])->				Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__lowerCAmelCase						 ={0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
											__lowerCAmelCase						 ={0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ])




