
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()
lowercase__							:							List[Any]									=			logging.get_logger(__name__)

lowercase__							:							Optional[int]									=			"Hello, World!"
lowercase__							:							List[str]									=			"en_XX"




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      snake_case							:   bool )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			Path('''data_bin''' )
						__UpperCamelCase       =			FairseqXmodModel.from_pretrained(
						    model_name_or_path=str(Path(snake_case ).parent ) ,      checkpoint_file=Path(snake_case ).name ,      _name='''xmod_base''' ,      arch='''xmod_base''' ,      task='''multilingual_masked_lm''' ,      data_name_or_path=str(snake_case ) ,      bpe='''sentencepiece''' ,      sentencepiece_model=str(Path(snake_case ).parent / '''sentencepiece.bpe.model''' ) ,      src_dict=str(data_dir / '''dict.txt''' ) ,      )
						xmod.eval()  # disable dropout
						print(snake_case )

						__UpperCamelCase       =			xmod.model.encoder.sentence_encoder
						__UpperCamelCase       =			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:
												__UpperCamelCase       =			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

						print('''Our X-MOD config:''' ,      snake_case )

						__UpperCamelCase       =			XmodForSequenceClassification(snake_case ) if classification_head else XmodForMaskedLM(snake_case )
						model.eval()

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

						__UpperCamelCase       =			xmod_sent_encoder.layernorm_embedding.weight
						__UpperCamelCase       =			xmod_sent_encoder.layernorm_embedding.bias

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

												# self attention
												__UpperCamelCase       =			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.''' )

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

												# self-attention output
												__UpperCamelCase       =			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.''' )
												__UpperCamelCase       =			xmod_layer.self_attn.out_proj.weight
												__UpperCamelCase       =			xmod_layer.self_attn.out_proj.bias
												__UpperCamelCase       =			xmod_layer.self_attn_layer_norm.weight
												__UpperCamelCase       =			xmod_layer.self_attn_layer_norm.bias

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

												# output
												__UpperCamelCase       =			layer.output
												if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																		raise AssertionError('''Dimensions of feed-forward weights do not match.''' )
												__UpperCamelCase       =			xmod_layer.fca.weight
												__UpperCamelCase       =			xmod_layer.fca.bias
												__UpperCamelCase       =			xmod_layer.final_layer_norm.weight
												__UpperCamelCase       =			xmod_layer.final_layer_norm.bias
												if bert_output.adapter_layer_norm is not None:
																		__UpperCamelCase       =			xmod_layer.adapter_layer_norm.weight
																		__UpperCamelCase       =			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():
																		__UpperCamelCase       =			bert_output.adapter_modules[lang_code]
																		__UpperCamelCase       =			xmod_layer.adapter_modules[lang_code]
																		__UpperCamelCase       =			from_adapter.fca.weight
																		__UpperCamelCase       =			from_adapter.fca.bias
																		__UpperCamelCase       =			from_adapter.fca.weight
																		__UpperCamelCase       =			from_adapter.fca.bias

												# end of layer

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

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

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

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

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


if __name__ == "__main__":
				lowercase__							:							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."
				)
				lowercase__							:							str									=			parser.parse_args()
				convert_xmod_checkpoint_to_pytorch(
				    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  1



							@register_to_config
							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = 1000     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple:





													'''simple docstring'''






													self.set_timesteps(SCREAMING_SNAKE_CASE_			)

													# standard deviation of the initial noise distribution
													__UpperCamelCase       =			1.0

													# For now we only support F-PNDM, i.e. the runge-kutta method
													# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
													# mainly at formula (9), (12), (13) and the Algorithm 2.
													__UpperCamelCase       =			4

													# running values
													__UpperCamelCase       =			[]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			num_inference_steps
													__UpperCamelCase       =			torch.linspace(1     ,	0     ,	num_inference_steps + 1			)[:-1]
													__UpperCamelCase       =			torch.cat([steps, torch.tensor([0.0]			)]			)

													if self.config.trained_betas is not None:
																			__UpperCamelCase       =			torch.tensor(self.config.trained_betas     ,	dtype=torch.floataa			)
													else:
																			__UpperCamelCase       =			torch.sin(steps * math.pi / 2			) ** 2

													__UpperCamelCase       =			(1.0 - self.betas**2) ** 0.5

													__UpperCamelCase       =			(torch.atana(self.betas     ,	self.alphas			) / math.pi * 2)[:-1]
													__UpperCamelCase       =			timesteps.to(SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			[]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[SchedulerOutput, Tuple]:





													'''simple docstring'''






													if self.num_inference_steps is None:
																			raise ValueError(
																			    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''			)

													__UpperCamelCase       =			(self.timesteps == timestep).nonzero().item()
													__UpperCamelCase       =			timestep_index + 1

													__UpperCamelCase       =			sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
													self.ets.append(SCREAMING_SNAKE_CASE_			)

													if len(self.ets			) == 1:
																			__UpperCamelCase       =			self.ets[-1]
													elif len(self.ets			) == 2:
																			__UpperCamelCase       =			(3 * self.ets[-1] - self.ets[-2]) / 2
													elif len(self.ets			) == 3:
																			__UpperCamelCase       =			(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
													else:
																			__UpperCamelCase       =			(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

													__UpperCamelCase       =			self._get_prev_sample(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   torch.FloatTensor:





													'''simple docstring'''






													return sample



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.alphas[timestep_index]
													__UpperCamelCase       =			self.betas[timestep_index]

													__UpperCamelCase       =			self.alphas[prev_timestep_index]
													__UpperCamelCase       =			self.betas[prev_timestep_index]

													__UpperCamelCase       =			(sample - sigma * ets) / max(SCREAMING_SNAKE_CASE_     ,	1E-8			)
													__UpperCamelCase       =			next_alpha * pred + ets * next_sigma

													return prev_sample



							def __len__(  self			)->   Optional[int]:





													'''simple docstring'''






													return self.config.num_train_timesteps




