




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Dict    =    [
				    'decoder.version',
				    'decoder.output_projection.weight',
				    '_float_tensor',
				    'decoder.embed_positions._float_tensor',
				]
				for k in ignore_keys:
								state_dict.pop(lowercase__							,    lowercase__		)







def      _snake_case			(				lowercase__		):
				_lowerCamelCase, _lowerCamelCase     :			List[str]    =    emb.weight.shape
				_lowerCamelCase     :			Union[str, Any]    =    nn.Linear(lowercase__							,    lowercase__							,    bias=lowercase__		)
				_lowerCamelCase     :			List[str]    =    emb.weight.data
				return lin_layer







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			List[str]    =    torch.load(lowercase__							,    map_location='cpu'		)
				_lowerCamelCase     :			Tuple    =    Namespace(**checkpoint['cfg']['model']		)
				_lowerCamelCase     :			Optional[int]    =    checkpoint['model']
				remove_ignore_keys_(lowercase__		)
				_lowerCamelCase     :			int    =    state_dict['decoder.embed_tokens.weight'].shape[0]

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

				_lowerCamelCase     :			Tuple    =    XGLMConfig(
				    vocab_size=lowercase__							,    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							,    )

				_lowerCamelCase     :			Union[str, Any]    =    XGLMForCausalLM(lowercase__		)
				_lowerCamelCase     :			Optional[Any]    =    model.load_state_dict(lowercase__							,    strict=lowercase__		)
				print(lowercase__		)
				_lowerCamelCase     :			Union[str, Any]    =    make_linear_from_emb(model.model.embed_tokens		)

				return model


if __name__ == "__main__":
			lowercase__     						=						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__     						=						parser.parse_args()
			lowercase__     						=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
			model.save_pretrained(args.pytorch_dump_folder_path)







'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

