






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_       :str       =						logging.get_logger(__name__)

A_       :Dict       =						"""Hello, World!"""
A_       :Any       =						"""en_XX"""






def 							A     (       a_							,a_							,a_					)     ->						Any:
					__UpperCamelCase      :					Optional[Any]       		=Path('data_bin'					)
					__UpperCamelCase      :					Optional[Any]       		=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					)

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

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

					__UpperCamelCase      :					Union[str, Any]       		=XmodForSequenceClassification(_UpperCAmelCase					) if classification_head else XmodForMaskedLM(_UpperCAmelCase					)
					model.eval()

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

					__UpperCamelCase      :					Any       		=xmod_sent_encoder.layernorm_embedding.weight
					__UpperCamelCase      :					str       		=xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										__UpperCamelCase      :					Union[str, Any]       		=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      :					Optional[int]       		=xmod_layer.self_attn.q_proj.weight
										__UpperCamelCase      :					Any       		=xmod_layer.self_attn.q_proj.bias
										__UpperCamelCase      :					Tuple       		=xmod_layer.self_attn.k_proj.weight
										__UpperCamelCase      :					Tuple       		=xmod_layer.self_attn.k_proj.bias
										__UpperCamelCase      :					int       		=xmod_layer.self_attn.v_proj.weight
										__UpperCamelCase      :					Any       		=xmod_layer.self_attn.v_proj.bias

										# self-attention output
										__UpperCamelCase      :					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.'					)
										__UpperCamelCase      :					Optional[Any]       		=xmod_layer.self_attn.out_proj.weight
										__UpperCamelCase      :					Tuple       		=xmod_layer.self_attn.out_proj.bias
										__UpperCamelCase      :					Dict       		=xmod_layer.self_attn_layer_norm.weight
										__UpperCamelCase      :					Tuple       		=xmod_layer.self_attn_layer_norm.bias

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

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

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										__UpperCamelCase      :					List[Any]       		=xmod_sent_encoder.layer_norm.weight
										__UpperCamelCase      :					List[str]       		=xmod_sent_encoder.layer_norm.bias

					if classification_head:
										__UpperCamelCase      :					Union[str, Any]       		=xmod.model.classification_heads['mnli'].dense.weight
										__UpperCamelCase      :					Any       		=xmod.model.classification_heads['mnli'].dense.bias
										__UpperCamelCase      :					int       		=xmod.model.classification_heads['mnli'].out_proj.weight
										__UpperCamelCase      :					Union[str, Any]       		=xmod.model.classification_heads['mnli'].out_proj.bias
					else:
										# LM Head
										__UpperCamelCase      :					Optional[int]       		=xmod.model.encoder.lm_head.dense.weight
										__UpperCamelCase      :					Any       		=xmod.model.encoder.lm_head.dense.bias
										__UpperCamelCase      :					Dict       		=xmod.model.encoder.lm_head.layer_norm.weight
										__UpperCamelCase      :					str       		=xmod.model.encoder.lm_head.layer_norm.bias
										__UpperCamelCase      :					str       		=xmod.model.encoder.lm_head.weight
										__UpperCamelCase      :					Optional[Any]       		=xmod.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					__UpperCamelCase      :					Optional[Any]       		=xmod.encode(_UpperCAmelCase					).unsqueeze(0					)  # batch of size 1
					model.roberta.set_default_language(_UpperCAmelCase					)

					__UpperCamelCase      :					Union[str, Any]       		=model(_UpperCAmelCase					)[0]
					if classification_head:
										__UpperCamelCase      :					int       		=xmod.model.classification_heads['mnli'](xmod.extract_features(_UpperCAmelCase					)					)
					else:
										__UpperCamelCase      :					Any       		=xmod.model(_UpperCAmelCase							,lang_id=[SAMPLE_LANGUAGE]					)[0]
					print(our_output.shape							,their_output.shape					)
					__UpperCamelCase      :					Any       		=torch.max(torch.abs(our_output - their_output					)					).item()
					print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
					__UpperCamelCase      :					Any       		=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_       :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.'''
							)
							A_       :Tuple       =						parser.parse_args()
							convert_xmod_checkpoint_to_pytorch(
							    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)








import flax.linen as nn
import jax
import jax.numpy as jnp

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





