


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
							raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
							raise Exception('requires fairseq < v2')

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

a					  =     'Hello, World!'
a					  =     'en_XX'


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			Path("""data_bin"""  )
					lowercase_  							=			FairseqXmodModel.from_pretrained(
					    model_name_or_path=str(Path(UpperCAmelCase__  ).parent  )						,							checkpoint_file=Path(UpperCAmelCase__  ).name						,							_name="""xmod_base"""						,							arch="""xmod_base"""						,							task="""multilingual_masked_lm"""						,							data_name_or_path=str(UpperCAmelCase__  )						,							bpe="""sentencepiece"""						,							sentencepiece_model=str(Path(UpperCAmelCase__  ).parent / """sentencepiece.bpe.model"""  )						,							src_dict=str(data_dir / """dict.txt"""  )						,							)
					xmod.eval()  # disable dropout
					print(UpperCAmelCase__  )

					lowercase_  							=			xmod.model.encoder.sentence_encoder
					lowercase_  							=			XmodConfig(
					    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,							hidden_size=xmod.cfg.model.encoder_embed_dim						,							num_hidden_layers=xmod.cfg.model.encoder_layers						,							num_attention_heads=xmod.cfg.model.encoder_attention_heads						,							intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,							max_position_embeddings=5_1_4						,							type_vocab_size=1						,							layer_norm_eps=1e-5						,							pre_norm=xmod.cfg.model.encoder_normalize_before						,							adapter_reduction_factor=getattr(xmod.cfg.model						,							"""bottleneck"""						,							2  )						,							adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,							adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,							ln_before_adapter=xmod.cfg.model.ln_before_adapter						,							languages=xmod.cfg.model.languages						,							)
					if classification_head:
										lowercase_  							=			xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

					print("""Our X-MOD config:"""						,							UpperCAmelCase__  )

					lowercase_  							=			XmodForSequenceClassification(UpperCAmelCase__  ) if classification_head else XmodForMaskedLM(UpperCAmelCase__  )
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowercase_  							=			xmod_sent_encoder.embed_tokens.weight
					lowercase_  							=			xmod_sent_encoder.embed_positions.weight
					lowercase_  							=			torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c xmod doesn't use them.

					lowercase_  							=			xmod_sent_encoder.layernorm_embedding.weight
					lowercase_  							=			xmod_sent_encoder.layernorm_embedding.bias

					for i in range(config.num_hidden_layers  ):
										# Encoder: start of layer
										lowercase_  							=			model.roberta.encoder.layer[i]
										lowercase_  							=			xmod_sent_encoder.layers[i]

										# self attention
										lowercase_  							=			layer.attention.self
										if not (
										    xmod_layer.self_attn.k_proj.weight.data.shape
										    == xmod_layer.self_attn.q_proj.weight.data.shape
										    == xmod_layer.self_attn.v_proj.weight.data.shape
										    == torch.Size((config.hidden_size, config.hidden_size)  )
										):
															raise AssertionError("""Dimensions of self-attention weights do not match."""  )

										lowercase_  							=			xmod_layer.self_attn.q_proj.weight
										lowercase_  							=			xmod_layer.self_attn.q_proj.bias
										lowercase_  							=			xmod_layer.self_attn.k_proj.weight
										lowercase_  							=			xmod_layer.self_attn.k_proj.bias
										lowercase_  							=			xmod_layer.self_attn.v_proj.weight
										lowercase_  							=			xmod_layer.self_attn.v_proj.bias

										# self-attention output
										lowercase_  							=			layer.attention.output
										if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
															raise AssertionError("""Dimensions of self-attention output weights do not match."""  )
										lowercase_  							=			xmod_layer.self_attn.out_proj.weight
										lowercase_  							=			xmod_layer.self_attn.out_proj.bias
										lowercase_  							=			xmod_layer.self_attn_layer_norm.weight
										lowercase_  							=			xmod_layer.self_attn_layer_norm.bias

										# intermediate
										lowercase_  							=			layer.intermediate
										if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError("""Dimensions of intermediate weights do not match."""  )
										lowercase_  							=			xmod_layer.fca.weight
										lowercase_  							=			xmod_layer.fca.bias

										# output
										lowercase_  							=			layer.output
										if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError("""Dimensions of feed-forward weights do not match."""  )
										lowercase_  							=			xmod_layer.fca.weight
										lowercase_  							=			xmod_layer.fca.bias
										lowercase_  							=			xmod_layer.final_layer_norm.weight
										lowercase_  							=			xmod_layer.final_layer_norm.bias
										if bert_output.adapter_layer_norm is not None:
															lowercase_  							=			xmod_layer.adapter_layer_norm.weight
															lowercase_  							=			xmod_layer.adapter_layer_norm.bias

										if sorted(bert_output.adapter_modules.keys()  ) != sorted(xmod_layer.adapter_modules.keys()  ):
															raise AssertionError("""Lists of language adapters do not match."""  )
										for lang_code, adapter in xmod_layer.adapter_modules.items():
															lowercase_  							=			bert_output.adapter_modules[lang_code]
															lowercase_  							=			xmod_layer.adapter_modules[lang_code]
															lowercase_  							=			from_adapter.fca.weight
															lowercase_  							=			from_adapter.fca.bias
															lowercase_  							=			from_adapter.fca.weight
															lowercase_  							=			from_adapter.fca.bias

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										lowercase_  							=			xmod_sent_encoder.layer_norm.weight
										lowercase_  							=			xmod_sent_encoder.layer_norm.bias

					if classification_head:
										lowercase_  							=			xmod.model.classification_heads["""mnli"""].dense.weight
										lowercase_  							=			xmod.model.classification_heads["""mnli"""].dense.bias
										lowercase_  							=			xmod.model.classification_heads["""mnli"""].out_proj.weight
										lowercase_  							=			xmod.model.classification_heads["""mnli"""].out_proj.bias
					else:
										# LM Head
										lowercase_  							=			xmod.model.encoder.lm_head.dense.weight
										lowercase_  							=			xmod.model.encoder.lm_head.dense.bias
										lowercase_  							=			xmod.model.encoder.lm_head.layer_norm.weight
										lowercase_  							=			xmod.model.encoder.lm_head.layer_norm.bias
										lowercase_  							=			xmod.model.encoder.lm_head.weight
										lowercase_  							=			xmod.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					lowercase_  							=			xmod.encode(UpperCAmelCase__  ).unsqueeze(0  )  # batch of size 1
					model.roberta.set_default_language(UpperCAmelCase__  )

					lowercase_  							=			model(UpperCAmelCase__  )[0]
					if classification_head:
										lowercase_  							=			xmod.model.classification_heads["""mnli"""](xmod.extract_features(UpperCAmelCase__  )  )
					else:
										lowercase_  							=			xmod.model(UpperCAmelCase__						,							lang_id=[SAMPLE_LANGUAGE]  )[0]
					print(our_output.shape						,							their_output.shape  )
					lowercase_  							=			torch.max(torch.abs(our_output - their_output  )  ).item()
					print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
					lowercase_  							=			torch.allclose(UpperCAmelCase__						,							UpperCAmelCase__						,							atol=1e-3  )
					print("""Do both models output the same tensors?"""						,							"""🔥""" if success else """💩"""  )
					if not success:
										raise Exception("""Something went wRoNg"""  )

					Path(UpperCAmelCase__  ).mkdir(parents=UpperCAmelCase__						,							exist_ok=UpperCAmelCase__  )
					print(F'''Saving model to {pytorch_dump_folder_path}'''  )
					model.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
							a					  =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--xmod_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.'
							)
							parser.add_argument(
							    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
							)
							a					  =     parser.parse_args()
							convert_xmod_checkpoint_to_pytorch(
							    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)






from dataclasses import asdict, dataclass
from typing import Optional

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


a					  =     logging.get_logger(__name__)

# TODO Update this
a					  =     {
    '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        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Tuple						=   'esm'

					def __init__(     self       :   Optional[Any]							,					UpperCamelCase__       :   Dict=None							,					UpperCamelCase__       :   Tuple=None							,					UpperCamelCase__       :   List[Any]=None							,					UpperCamelCase__       :   List[Any]=768							,					UpperCamelCase__       :   Tuple=12							,					UpperCamelCase__       :   Optional[int]=12							,					UpperCamelCase__       :   Dict=3_072							,					UpperCamelCase__       :   Union[str, Any]=0.1							,					UpperCamelCase__       :   Dict=0.1							,					UpperCamelCase__       :   Optional[int]=1_026							,					UpperCamelCase__       :   Any=0.02							,					UpperCamelCase__       :   Dict=1e-12							,					UpperCamelCase__       :   List[str]="absolute"							,					UpperCamelCase__       :   Union[str, Any]=True							,					UpperCamelCase__       :   Tuple=None							,					UpperCamelCase__       :   Union[str, Any]=False							,					UpperCamelCase__       :   Optional[int]=False							,					UpperCamelCase__       :   Dict=None							,					UpperCamelCase__       :   str=None							,					**UpperCamelCase__       :   str							,					):






										'''simple docstring'''






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

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







					def   UpperCAmelCase__       (     self       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			super().to_dict()
										if isinstance(self.esmfold_config							,					UpperCamelCase__       ):
															lowercase_  							=			self.esmfold_config.to_dict()
										return output







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   None
					__SCREAMING_SNAKE_CASE       :  bool						=   True
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  float						=   0

					__SCREAMING_SNAKE_CASE       :  bool						=   True
					__SCREAMING_SNAKE_CASE       :  bool						=   False

					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  "TrunkConfig"						=   None

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										if self.trunk is None:
															lowercase_  							=			TrunkConfig()
										elif isinstance(self.trunk							,					UpperCamelCase__       ):
															lowercase_  							=			TrunkConfig(**self.trunk       )







					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			asdict(self       )
										lowercase_  							=			self.trunk.to_dict()
										return output







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  int						=   48
					__SCREAMING_SNAKE_CASE       :  int						=   1024
					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  int						=   32
					__SCREAMING_SNAKE_CASE       :  int						=   32
					__SCREAMING_SNAKE_CASE       :  int						=   32
					__SCREAMING_SNAKE_CASE       :  float						=   0
					__SCREAMING_SNAKE_CASE       :  float						=   0
					__SCREAMING_SNAKE_CASE       :  bool						=   False
					__SCREAMING_SNAKE_CASE       :  int						=   4
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   128
					__SCREAMING_SNAKE_CASE       :  "StructureModuleConfig"						=   None

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										if self.structure_module is None:
															lowercase_  							=			StructureModuleConfig()
										elif isinstance(self.structure_module							,					UpperCamelCase__       ):
															lowercase_  							=			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}.'''       )

										lowercase_  							=			self.sequence_state_dim // self.sequence_head_width
										lowercase_  							=			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       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			asdict(self       )
										lowercase_  							=			self.structure_module.to_dict()
										return output







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  int						=   384
					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  int						=   16
					__SCREAMING_SNAKE_CASE       :  int						=   128
					__SCREAMING_SNAKE_CASE       :  int						=   12
					__SCREAMING_SNAKE_CASE       :  int						=   4
					__SCREAMING_SNAKE_CASE       :  int						=   8
					__SCREAMING_SNAKE_CASE       :  float						=   0.1
					__SCREAMING_SNAKE_CASE       :  int						=   8
					__SCREAMING_SNAKE_CASE       :  int						=   1
					__SCREAMING_SNAKE_CASE       :  int						=   2
					__SCREAMING_SNAKE_CASE       :  int						=   7
					__SCREAMING_SNAKE_CASE       :  int						=   10
					__SCREAMING_SNAKE_CASE       :  float						=   1e-8
					__SCREAMING_SNAKE_CASE       :  float						=   1e5

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										return asdict(self       )



def 			UpperCAmelCase_				(      ):
					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>",
					)


