





from dataclasses import asdict, dataclass
from typing import Optional

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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

# TODO Update this
_SCREAMING_SNAKE_CASE      	=		{
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):

						'''simple docstring'''





						__magic_name__:			Tuple       =       "esm"






						def __init__(      self      :      Optional[Any]		,   _A      :      Optional[int]=None		,   _A      :      Optional[Any]=None		,   _A      :      Dict=None		,   _A      :      Any=768		,   _A      :      List[str]=12		,   _A      :      Dict=12		,   _A      :      List[Any]=3072		,   _A      :      Any=0.1		,   _A      :      Tuple=0.1		,   _A      :      Any=1026		,   _A      :      Optional[Any]=0.0_2		,   _A      :      Optional[int]=1E-12		,   _A      :      List[str]="absolute"		,   _A      :      int=True		,   _A      :      Optional[int]=None		,   _A      :      List[Any]=False		,   _A      :      Optional[int]=False		,   _A      :      Optional[Any]=None		,   _A      :      Dict=None		,   **_A      :      Optional[int]		,   )							->    Optional[int]:







							"""simple docstring"""


							super().__init__(pad_token_id=_A		,   mask_token_id=_A		,   **_A	)

							snake_case_		:			Dict											=	vocab_size
							snake_case_		:			Optional[Any]											=	hidden_size
							snake_case_		:			List[str]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			str											=	intermediate_size
							snake_case_		:			Optional[int]											=	hidden_dropout_prob
							snake_case_		:			Optional[Any]											=	attention_probs_dropout_prob
							snake_case_		:			str											=	max_position_embeddings
							snake_case_		:			List[Any]											=	initializer_range
							snake_case_		:			Optional[int]											=	layer_norm_eps
							snake_case_		:			Any											=	position_embedding_type
							snake_case_		:			Dict											=	use_cache
							snake_case_		:			Union[str, Any]											=	emb_layer_norm_before
							snake_case_		:			Optional[Any]											=	token_dropout
							snake_case_		:			int											=	is_folding_model
							if is_folding_model:
								if esmfold_config is None:
									logger.info('No esmfold_config supplied for folding model, using default values.'	)
									snake_case_		:			str											=	EsmFoldConfig()
								elif isinstance(_A		,   _A	):
									snake_case_		:			Union[str, Any]											=	EsmFoldConfig(**_A	)
								snake_case_		:			Union[str, Any]											=	esmfold_config
								if vocab_list is None:
									logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'	)
									snake_case_		:			Union[str, Any]											=	get_default_vocab_list()
								else:
									snake_case_		:			List[str]											=	vocab_list
							else:
								snake_case_		:			Optional[Any]											=	None
								snake_case_		:			str											=	None
							if self.esmfold_config is not None and getattr(self.esmfold_config		,   'use_esm_attn_map'		,   _A	):
								raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'	)






						def UpperCAmelCase_				(      self      :      Any	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	super().to_dict()
							if isinstance(self.esmfold_config		,   _A	):
								snake_case_		:			Tuple											=	self.esmfold_config.to_dict()
							return output
@dataclass
class SCREAMING_SNAKE_CASE_						:

						'''simple docstring'''





						__magic_name__:			str       =       None
						__magic_name__:			bool       =       True
						__magic_name__:			bool       =       False
						__magic_name__:			bool       =       False
						__magic_name__:			bool       =       False
						__magic_name__:			float       =       0

						__magic_name__:			bool       =       True
						__magic_name__:			bool       =       False

						__magic_name__:			int       =       128
						__magic_name__:			"TrunkConfig"       =       None






						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[int]:







							"""simple docstring"""


							if self.trunk is None:
								snake_case_		:			Any											=	TrunkConfig()
							elif isinstance(self.trunk		,   _A	):
								snake_case_		:			Union[str, Any]											=	TrunkConfig(**self.trunk	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			int											=	asdict(self	)
							snake_case_		:			List[Any]											=	self.trunk.to_dict()
							return output
@dataclass
class SCREAMING_SNAKE_CASE_						:

						'''simple docstring'''





						__magic_name__:			int       =       48
						__magic_name__:			int       =       1024
						__magic_name__:			int       =       128
						__magic_name__:			int       =       32
						__magic_name__:			int       =       32
						__magic_name__:			int       =       32
						__magic_name__:			float       =       0
						__magic_name__:			float       =       0
						__magic_name__:			bool       =       False
						__magic_name__:			int       =       4
						__magic_name__:			Optional[int]       =       128
						__magic_name__:			"StructureModuleConfig"       =       None






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							if self.structure_module is None:
								snake_case_		:			Union[str, Any]											=	StructureModuleConfig()
							elif isinstance(self.structure_module		,   _A	):
								snake_case_		:			Optional[Any]											=	StructureModuleConfig(**self.structure_module	)

							if self.max_recycles <= 0:
								raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}."""	)
							if self.sequence_state_dim % self.sequence_state_dim != 0:
								raise ValueError(
								    '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
								    F""" {self.sequence_state_dim} and {self.sequence_state_dim}."""	)
							if self.pairwise_state_dim % self.pairwise_state_dim != 0:
								raise ValueError(
								    '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
								    F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}."""	)

							snake_case_		:			List[str]											=	self.sequence_state_dim // self.sequence_head_width
							snake_case_		:			Optional[int]											=	self.pairwise_state_dim // self.pairwise_head_width

							if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
								raise ValueError(
								    '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
								    F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."""	)
							if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
								raise ValueError(
								    '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
								    F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."""	)
							if self.pairwise_state_dim % 2 != 0:
								raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."""	)

							if self.dropout >= 0.4:
								raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}."""	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			int											=	asdict(self	)
							snake_case_		:			Optional[int]											=	self.structure_module.to_dict()
							return output
@dataclass
class SCREAMING_SNAKE_CASE_						:

						'''simple docstring'''





						__magic_name__:			int       =       384
						__magic_name__:			int       =       128
						__magic_name__:			int       =       16
						__magic_name__:			int       =       128
						__magic_name__:			int       =       12
						__magic_name__:			int       =       4
						__magic_name__:			int       =       8
						__magic_name__:			float       =       0.1
						__magic_name__:			int       =       8
						__magic_name__:			int       =       1
						__magic_name__:			int       =       2
						__magic_name__:			int       =       7
						__magic_name__:			int       =       10
						__magic_name__:			float       =       1E-8
						__magic_name__:			float       =       1E5






						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return asdict(self	)


def 							SCREAMING_SNAKE_CASE__						(    ):
	return (
	    "<cls>",
	    "<pad>",
	    "<eos>",
	    "<unk>",
	    "L",
	    "A",
	    "G",
	    "V",
	    "S",
	    "E",
	    "R",
	    "T",
	    "I",
	    "D",
	    "P",
	    "K",
	    "Q",
	    "N",
	    "F",
	    "Y",
	    "M",
	    "H",
	    "W",
	    "C",
	    "X",
	    "B",
	    "U",
	    "Z",
	    "O",
	    ".",
	    "-",
	    "<null_1>",
	    "<mask>",
	)








import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

	snake_case_		:			List[str]											=	{
	    'self_attn': 'ngram_self_attn',
	    'cross_attn': 'encoder_attn',
	    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
	    'feed_forward_layer_norm': 'final_layer_norm',
	    'feed_forward': '',
	    'intermediate': 'fc1',
	    'output': 'fc2',
	    'key_proj': 'k_proj',
	    'query_proj': 'q_proj',
	    'value_proj': 'v_proj',
	    'word_embeddings': 'embed_tokens',
	    'embeddings_layer_norm': 'emb_layer_norm',
	    'relative_pos_embeddings': 'relative_linear',
	    'ngram_embeddings': 'ngram_input_embed',
	    'position_embeddings': 'embed_positions',
	}

	for key in loading_info["missing_keys"]:
		snake_case_		:			Union[str, Any]											=	key.split('.'   )

		if attributes[0] == "lm_head":
			snake_case_		:			Optional[Any]											=	prophet
			snake_case_		:			Any											=	prophet_old
		else:
			snake_case_		:			Optional[int]											=	prophet.prophetnet
			snake_case_		:			str											=	prophet_old.model

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				snake_case_		:			int											=	old_model.weight
				logger.info(f"""{attribute} is initialized."""   )
				snake_case_		:			Optional[int]											=	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				snake_case_		:			List[str]											=	old_model.bias
				logger.info(f"""{attribute} is initialized"""   )
				snake_case_		:			int											=	True
				break
			elif attribute in special_keys and hasattr(__a   , 'in_proj_weight'   ):
				snake_case_		:			Optional[Any]											=	old_model.in_proj_weight.shape[0] // 3
				snake_case_		:			List[Any]											=	getattr(__a   , __a   )
				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
				if attribute == "query_proj":
					snake_case_		:			Tuple											=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
					snake_case_		:			Optional[int]											=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

				elif attribute == "key_proj":
					snake_case_		:			int											=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
					snake_case_		:			Any											=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
				elif attribute == "value_proj":
					snake_case_		:			List[Any]											=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
					snake_case_		:			str											=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
				snake_case_		:			Any											=	True
				break
			elif attribute == "position_embeddings":
				assert (
				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
				), "Hidden size has to match"
				assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
				snake_case_		:			Any											=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
				snake_case_		:			List[Any]											=	True
				break

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	getattr(__a   , __a   )

		if not is_key_init:
			raise ValueError(f"""{key} was not correctly initialized!"""   )

	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	prophet.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

