



'''simple docstring'''




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

UpperCamelCase__ :			Optional[int]												=				'''Hello, World!'''
UpperCamelCase__ :			int												=				'''en_XX'''


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

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

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

		__SCREAMING_SNAKE_CASE :	List[str]					 =	XmodForSequenceClassification(_lowerCamelCase      ) if classification_head else XmodForMaskedLM(_lowerCamelCase      )
		model.eval()

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

		__SCREAMING_SNAKE_CASE :	int					 =	xmod_sent_encoder.layernorm_embedding.weight
		__SCREAMING_SNAKE_CASE :	str					 =	xmod_sent_encoder.layernorm_embedding.bias

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

				# self attention
				__SCREAMING_SNAKE_CASE :	Optional[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."""      )

				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	xmod_layer.self_attn.q_proj.weight
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	xmod_layer.self_attn.q_proj.bias
				__SCREAMING_SNAKE_CASE :	Any					 =	xmod_layer.self_attn.k_proj.weight
				__SCREAMING_SNAKE_CASE :	Tuple					 =	xmod_layer.self_attn.k_proj.bias
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	xmod_layer.self_attn.v_proj.weight
				__SCREAMING_SNAKE_CASE :	str					 =	xmod_layer.self_attn.v_proj.bias

				# self-attention output
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	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 :	Optional[int]					 =	xmod_layer.self_attn.out_proj.weight
				__SCREAMING_SNAKE_CASE :	List[str]					 =	xmod_layer.self_attn.out_proj.bias
				__SCREAMING_SNAKE_CASE :	Any					 =	xmod_layer.self_attn_layer_norm.weight
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	xmod_layer.self_attn_layer_norm.bias

				# intermediate
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	layer.intermediate
				if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError("""Dimensions of intermediate weights do not match."""      )
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	xmod_layer.fca.weight
				__SCREAMING_SNAKE_CASE :	int					 =	xmod_layer.fca.bias

				# output
				__SCREAMING_SNAKE_CASE :	int					 =	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 :	Any					 =	xmod_layer.fca.weight
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	xmod_layer.fca.bias
				__SCREAMING_SNAKE_CASE :	str					 =	xmod_layer.final_layer_norm.weight
				__SCREAMING_SNAKE_CASE :	str					 =	xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						__SCREAMING_SNAKE_CASE :	str					 =	xmod_layer.adapter_layer_norm.weight
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	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 :	Union[str, Any]					 =	bert_output.adapter_modules[lang_code]
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	xmod_layer.adapter_modules[lang_code]
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	from_adapter.fca.weight
						__SCREAMING_SNAKE_CASE :	List[str]					 =	from_adapter.fca.bias
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	from_adapter.fca.weight
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	from_adapter.fca.bias

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	xmod_sent_encoder.layer_norm.weight
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	xmod_sent_encoder.layer_norm.bias

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

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

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

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


if __name__ == "__main__":
						UpperCamelCase__ :			Optional[int]												=				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.'''
						)
						UpperCamelCase__ :			int												=				parser.parse_args()
						convert_xmod_checkpoint_to_pytorch(
						    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			List[Any]												=				{
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			List[Any]												=				[
						    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TimeSeriesTransformerForPrediction''',
						    '''TimeSeriesTransformerModel''',
						    '''TimeSeriesTransformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_time_series_transformer import (
						    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TimeSeriesTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_time_series_transformer import (
												    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TimeSeriesTransformerForPrediction,
												    TimeSeriesTransformerModel,
												    TimeSeriesTransformerPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			Dict												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)