



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()
lowerCAmelCase__		:					List[str]							=logging.get_logger(__name__)

lowerCAmelCase__		:					Any							='''Hello, World!'''
lowerCAmelCase__		:					Tuple							='''en_XX'''






def      __lowercase       (			a__							,       a__							,       a__       )    ->			List[Any]:
							__SCREAMING_SNAKE_CASE									= Path('data_bin'       )
							__SCREAMING_SNAKE_CASE									= 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__       )

							__SCREAMING_SNAKE_CASE									= xmod.model.encoder.sentence_encoder
							__SCREAMING_SNAKE_CASE									= 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_14							,       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:
														__SCREAMING_SNAKE_CASE									= xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

							__SCREAMING_SNAKE_CASE									= XmodForSequenceClassification(a__       ) if classification_head else XmodForMaskedLM(a__       )
							model.eval()

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

							__SCREAMING_SNAKE_CASE									= xmod_sent_encoder.layernorm_embedding.weight
							__SCREAMING_SNAKE_CASE									= xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														__SCREAMING_SNAKE_CASE									= 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.'       )

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

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

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

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

														# end of layer

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

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

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

							__SCREAMING_SNAKE_CASE									= model(a__       )[0]
							if classification_head:
														__SCREAMING_SNAKE_CASE									= xmod.model.classification_heads['mnli'](xmod.extract_features(a__       )       )
							else:
														__SCREAMING_SNAKE_CASE									= xmod.model(a__							,       lang_id=[SAMPLE_LANGUAGE]       )[0]
							print(our_output.shape							,       their_output.shape       )
							__SCREAMING_SNAKE_CASE									= torch.max(torch.abs(our_output - their_output       )       ).item()
							print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
							__SCREAMING_SNAKE_CASE									= 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__":
			lowerCAmelCase__		:					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.'''
			)
			lowerCAmelCase__		:					Dict							=parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)











from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''The output directory where the model will be written.'''}   ,   )
						UpperCamelCase__      :			str      =  field(
						    metadata={
						        '''help''': (
						            '''The encoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train an encoder model from scratch.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			str      =  field(
						    metadata={
						        '''help''': (
						            '''The decoder model checkpoint for weights initialization.'''
						            '''Don\'t set if you want to train a decoder model from scratch.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''}	)







def      __lowercase       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments,)       )
							((__SCREAMING_SNAKE_CASE)							,	)									= parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

							# Use explicit specified encoder config
							if model_args.encoder_config_name:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.encoder_config_name       )
							# Use pretrained encoder model's config
							else:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path       )

							# Use explicit specified decoder config
							if model_args.decoder_config_name:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.decoder_config_name       )
							# Use pretrained decoder model's config
							else:
														__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path       )

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							__SCREAMING_SNAKE_CASE									= True
							__SCREAMING_SNAKE_CASE									= True

							__SCREAMING_SNAKE_CASE									= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
							    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path							,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path							,       encoder_config=a__							,       decoder_config=a__							,       )

							# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
							__SCREAMING_SNAKE_CASE									= decoder_config.decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= decoder_config.pad_token_id
							if decoder_start_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.bos_token_id
							if pad_token_id is None:
														__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id

							# This is necessary to make Flax's generate() work
							__SCREAMING_SNAKE_CASE									= decoder_config.eos_token_id
							__SCREAMING_SNAKE_CASE									= decoder_start_token_id
							__SCREAMING_SNAKE_CASE									= pad_token_id

							__SCREAMING_SNAKE_CASE									= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path       )

							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path       )
							__SCREAMING_SNAKE_CASE									= tokenizer.convert_ids_to_tokens(model.config.pad_token_id       )

							model.save_pretrained(model_args.output_dir       )
							image_processor.save_pretrained(model_args.output_dir       )
							tokenizer.save_pretrained(model_args.output_dir       )


if __name__ == "__main__":
			main()






