

'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "funnel"
							_lowerCamelCase		:      Optional[Any]=   {
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							}


							def __init__(			self    ,			_snake_case=3_0522    ,			_snake_case=[4, 4, 4]    ,			_snake_case=None    ,			_snake_case=2    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=64    ,			_snake_case=3072    ,			_snake_case="gelu_new"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case=None    ,			_snake_case=1e-9    ,			_snake_case="mean"    ,			_snake_case="relative_shift"    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=True    ,			**_snake_case    ,			)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      Optional[Any]       	=					block_sizes
								UpperCAmelCase_		:      Tuple       	=					[1] * len(_snake_case) if block_repeats is None else block_repeats
								assert len(_snake_case) == len(
								    self.block_repeats), "`block_sizes` and `block_repeats` should have the same length."
								UpperCAmelCase_		:      List[str]       	=					num_decoder_layers
								UpperCAmelCase_		:      List[Any]       	=					d_model
								UpperCAmelCase_		:      int       	=					n_head
								UpperCAmelCase_		:      Tuple       	=					d_head
								UpperCAmelCase_		:      str       	=					d_inner
								UpperCAmelCase_		:      Dict       	=					hidden_act
								UpperCAmelCase_		:      Any       	=					hidden_dropout
								UpperCAmelCase_		:      Dict       	=					attention_dropout
								UpperCAmelCase_		:      Dict       	=					activation_dropout
								UpperCAmelCase_		:      Any       	=					initializer_range
								UpperCAmelCase_		:      Any       	=					initializer_std
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								assert pooling_type in [
								    "mean",
								    "max",
								], F"""Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."""
								UpperCAmelCase_		:      Optional[int]       	=					pooling_type
								assert attention_type in [
								    "relative_shift",
								    "factorized",
								], F"""Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."""
								UpperCAmelCase_		:      List[str]       	=					attention_type
								UpperCAmelCase_		:      Union[str, Any]       	=					separate_cls
								UpperCAmelCase_		:      List[str]       	=					truncate_seq
								UpperCAmelCase_		:      List[str]       	=					pool_q_only

								super().__init__(**_snake_case)


							@property
							def 						_snake_case							(			self)							->				Optional[int]:
								return sum(self.block_sizes)


							@num_hidden_layers.setter
							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								raise NotImplementedError(
								    'This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.')


							@property
							def 						_snake_case							(			self)							->				int:
								return len(self.block_sizes)





							@num_blocks.setter
							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								raise NotImplementedError('This model does not support the setting of `num_blocks`. Please set `block_sizes`.')









'''simple docstring'''


import argparse

import torch

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


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

lowerCAmelCase__										= [
    ["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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







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


lowerCAmelCase__										= ["START"]







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

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


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






