





'''simple docstring'''





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def       A__(		__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   [
				    'decoder.version',
				    'decoder.output_projection.weight',
				    '_float_tensor',
				    'decoder.embed_positions._float_tensor',
				]
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase__	,			lowerCAmelCase__ )

def       A__(		__lowerCAmelCase ):
				_snake_case						,			_snake_case :			List[Any]  						=   emb.weight.shape
				_snake_case :			Dict  						=   nn.Linear(lowerCAmelCase__	,			lowerCAmelCase__	,			bias=lowerCAmelCase__ )
				_snake_case :			str  						=   emb.weight.data
				return lin_layer

def       A__(		__lowerCAmelCase ):
				_snake_case :			str  						=   torch.load(lowerCAmelCase__	,			map_location='cpu' )
				_snake_case :			Optional[Any]  						=   Namespace(**checkpoint['cfg']['model'] )
				_snake_case :			Optional[Any]  						=   checkpoint['model']
				remove_ignore_keys_(lowerCAmelCase__ )
				_snake_case :			Optional[int]  						=   state_dict['decoder.embed_tokens.weight'].shape[0]

				_snake_case :			Optional[Any]  						=   {key.replace('decoder'	,			'model' ): val for key, val in state_dict.items()}

				_snake_case :			Optional[Any]  						=   XGLMConfig(
				    vocab_size=lowerCAmelCase__	,			max_position_embeddings=args.max_target_positions	,			num_layers=args.decoder_layers	,			attention_heads=args.decoder_attention_heads	,			ffn_dim=args.decoder_ffn_embed_dim	,			d_model=args.decoder_embed_dim	,			layerdrop=args.decoder_layerdrop	,			dropout=args.dropout	,			attention_dropout=args.attention_dropout	,			activation_dropout=args.activation_dropout	,			activation_function='gelu'	,			scale_embedding=not args.no_scale_embedding	,			tie_word_embeddings=args.share_decoder_input_output_embed	,			)

				_snake_case :			Tuple  						=   XGLMForCausalLM(lowerCAmelCase__ )
				_snake_case :			str  						=   model.load_state_dict(lowerCAmelCase__	,			strict=lowerCAmelCase__ )
				print(lowerCAmelCase__ )
				_snake_case :			int  						=   make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		lowercase_		:				int				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		lowercase_		:				Optional[Any]				=						parser.parse_args()
		lowercase_		:				Optional[int]				=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Optional[Any]				=						{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Tuple				=						[
		    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoCBertForCausalLM''',
		    '''RoCBertForMaskedLM''',
		    '''RoCBertForMultipleChoice''',
		    '''RoCBertForPreTraining''',
		    '''RoCBertForQuestionAnswering''',
		    '''RoCBertForSequenceClassification''',
		    '''RoCBertForTokenClassification''',
		    '''RoCBertLayer''',
		    '''RoCBertModel''',
		    '''RoCBertPreTrainedModel''',
		    '''load_tf_weights_in_roc_bert''',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		lowercase_		:				Tuple				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






