'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	Optional[Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [
							    'encoder.version',
							    'decoder.version',
							    'model.encoder.version',
							    'model.decoder.version',
							    'decoder.output_projection.weight',
							    '_float_tensor',
							    'encoder.embed_positions._float_tensor',
							    'decoder.embed_positions._float_tensor',
							]
							for k in ignore_keys:
														state_dict.pop(UpperCAmelCase__				,						UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  Any		)			->	Dict:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = emb.weight.shape
							SCREAMING_SNAKE_CASE__     :List[str]						    = nn.Linear(UpperCAmelCase__				,						UpperCAmelCase__				,						bias=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Any						    = emb.weight.data
							return lin_layer


def    lowerCamelCase       (			UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.load(UpperCAmelCase__				,						map_location='cpu'		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = mam_aaa['args'] or mam_aaa['cfg']['model']
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = mam_aaa['model']
							remove_ignore_keys_(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = state_dict['encoder.embed_tokens.weight'].shape[0]

							SCREAMING_SNAKE_CASE__     :Tuple						    = MaMaaaConfig(
							    vocab_size=UpperCAmelCase__				,						max_position_embeddings=1_0_2_4				,						encoder_layers=args.encoder_layers				,						decoder_layers=args.decoder_layers				,						encoder_attention_heads=args.encoder_attention_heads				,						decoder_attention_heads=args.decoder_attention_heads				,						encoder_ffn_dim=args.encoder_ffn_embed_dim				,						decoder_ffn_dim=args.decoder_ffn_embed_dim				,						d_model=args.encoder_embed_dim				,						encoder_layerdrop=args.encoder_layerdrop				,						decoder_layerdrop=args.decoder_layerdrop				,						dropout=args.dropout				,						attention_dropout=args.attention_dropout				,						activation_dropout=args.activation_dropout				,						activation_function='relu'				,						)

							SCREAMING_SNAKE_CASE__     :int						    = state_dict['decoder.embed_tokens.weight']
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = MaMaaaForConditionalGeneration(UpperCAmelCase__		)
							model.model.load_state_dict(UpperCAmelCase__				,						strict=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = make_linear_from_emb(model.model.shared		)

							return model


if __name__ == "__main__":
		UpperCamelCase_      	=	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.''')
		UpperCamelCase_      	=	parser.parse_args()
		UpperCamelCase_      	=	convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)







'''simple docstring'''







from typing import TYPE_CHECKING

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


UpperCamelCase_      	=	{
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	['''BloomTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BloomForCausalLM''',
		    '''BloomModel''',
		    '''BloomPreTrainedModel''',
		    '''BloomForSequenceClassification''',
		    '''BloomForTokenClassification''',
		    '''BloomForQuestionAnswering''',
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






