






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()
__magic_name__:					Any							=     logging.get_logger(__name__)

__magic_name__:					int							=     "Hello, World!"
__magic_name__:					Tuple							=     "en_XX"

def    UpperCamelCase						(	_A,   _A,   _A			):






						"""simple docstring"""







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

						__magic_name__      :       Optional[Any]       						= xmod.model.encoder.sentence_encoder
						__magic_name__      :       Union[str, Any]       						= 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=514,   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:
												__magic_name__      :       int       						= xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

						__magic_name__      :       List[Any]       						= XmodForSequenceClassification(_A			) if classification_head else XmodForMaskedLM(_A			)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						__magic_name__      :       Dict       						= xmod_sent_encoder.embed_tokens.weight
						__magic_name__      :       List[Any]       						= xmod_sent_encoder.embed_positions.weight
						__magic_name__      :       Union[str, Any]       						= torch.zeros_like(
						    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c xmod doesn't use them.

						__magic_name__      :       Union[str, Any]       						= xmod_sent_encoder.layernorm_embedding.weight
						__magic_name__      :       Any       						= xmod_sent_encoder.layernorm_embedding.bias

						for i in range(config.num_hidden_layers			):
												# Encoder: start of layer
												__magic_name__      :       Any       						= model.roberta.encoder.layer[i]
												__magic_name__      :       Union[str, Any]       						= xmod_sent_encoder.layers[i]

												# self attention
												__magic_name__      :       List[str]       						= 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."""			)

												__magic_name__      :       Optional[Any]       						= xmod_layer.self_attn.q_proj.weight
												__magic_name__      :       Optional[Any]       						= xmod_layer.self_attn.q_proj.bias
												__magic_name__      :       Union[str, Any]       						= xmod_layer.self_attn.k_proj.weight
												__magic_name__      :       Tuple       						= xmod_layer.self_attn.k_proj.bias
												__magic_name__      :       Any       						= xmod_layer.self_attn.v_proj.weight
												__magic_name__      :       List[Any]       						= xmod_layer.self_attn.v_proj.bias

												# self-attention output
												__magic_name__      :       List[str]       						= 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."""			)
												__magic_name__      :       Union[str, Any]       						= xmod_layer.self_attn.out_proj.weight
												__magic_name__      :       List[str]       						= xmod_layer.self_attn.out_proj.bias
												__magic_name__      :       List[Any]       						= xmod_layer.self_attn_layer_norm.weight
												__magic_name__      :       List[str]       						= xmod_layer.self_attn_layer_norm.bias

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

												# output
												__magic_name__      :       List[Any]       						= layer.output
												if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																		raise AssertionError("""Dimensions of feed-forward weights do not match."""			)
												__magic_name__      :       Tuple       						= xmod_layer.fca.weight
												__magic_name__      :       Union[str, Any]       						= xmod_layer.fca.bias
												__magic_name__      :       Optional[Any]       						= xmod_layer.final_layer_norm.weight
												__magic_name__      :       Any       						= xmod_layer.final_layer_norm.bias
												if bert_output.adapter_layer_norm is not None:
																		__magic_name__      :       Optional[int]       						= xmod_layer.adapter_layer_norm.weight
																		__magic_name__      :       List[str]       						= 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():
																		__magic_name__      :       Optional[int]       						= bert_output.adapter_modules[lang_code]
																		__magic_name__      :       Dict       						= xmod_layer.adapter_modules[lang_code]
																		__magic_name__      :       Any       						= from_adapter.fca.weight
																		__magic_name__      :       List[Any]       						= from_adapter.fca.bias
																		__magic_name__      :       List[Any]       						= from_adapter.fca.weight
																		__magic_name__      :       Dict       						= from_adapter.fca.bias

												# end of layer

						if xmod_sent_encoder.layer_norm is not None:
												__magic_name__      :       Tuple       						= xmod_sent_encoder.layer_norm.weight
												__magic_name__      :       Optional[Any]       						= xmod_sent_encoder.layer_norm.bias

						if classification_head:
												__magic_name__      :       str       						= xmod.model.classification_heads["""mnli"""].dense.weight
												__magic_name__      :       str       						= xmod.model.classification_heads["""mnli"""].dense.bias
												__magic_name__      :       List[Any]       						= xmod.model.classification_heads["""mnli"""].out_proj.weight
												__magic_name__      :       Optional[Any]       						= xmod.model.classification_heads["""mnli"""].out_proj.bias
						else:
												# LM Head
												__magic_name__      :       int       						= xmod.model.encoder.lm_head.dense.weight
												__magic_name__      :       int       						= xmod.model.encoder.lm_head.dense.bias
												__magic_name__      :       Optional[int]       						= xmod.model.encoder.lm_head.layer_norm.weight
												__magic_name__      :       Optional[int]       						= xmod.model.encoder.lm_head.layer_norm.bias
												__magic_name__      :       int       						= xmod.model.encoder.lm_head.weight
												__magic_name__      :       str       						= xmod.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
		__magic_name__:					List[str]							=     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."
		)
		__magic_name__:					Optional[Any]							=     parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
