





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_	: Tuple      				=logging.get_logger(__name__)

A_	: Dict      				={
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}







class       __a  (    lowerCAmelCase__						):
     SCREAMING_SNAKE_CASE__					: Tuple									=     "open-llama"



     def __init__(			self   ,	a__=10_00_00   ,	a__=40_96   ,	a__=1_10_08   ,	a__=32   ,	a__=32   ,	a__="silu"   ,	a__=20_48   ,	a__=0.02   ,	a__=1e-6   ,	a__=True   ,	a__=0   ,	a__=1   ,	a__=2   ,	a__=False   ,	a__=True   ,	a__=0.1   ,	a__=0.1   ,	a__=True   ,	a__=True   ,	a__=None   ,	**a__   ,	):
            _lowerCamelCase             =   vocab_size
            _lowerCamelCase             =   max_position_embeddings
            _lowerCamelCase             =   hidden_size
            _lowerCamelCase             =   intermediate_size
            _lowerCamelCase             =   num_hidden_layers
            _lowerCamelCase             =   num_attention_heads
            _lowerCamelCase             =   hidden_act
            _lowerCamelCase             =   initializer_range
            _lowerCamelCase             =   rms_norm_eps
            _lowerCamelCase             =   use_cache
            _lowerCamelCase             =   kwargs.pop(
                'use_memorry_efficient_attention'   ,	snake_case_  )
            _lowerCamelCase             =   hidden_dropout_prob
            _lowerCamelCase             =   attention_dropout_prob
            _lowerCamelCase             =   use_stable_embedding
            _lowerCamelCase             =   shared_input_output_embedding
            _lowerCamelCase             =   rope_scaling
            self._rope_scaling_validation()

            super().__init__(
                pad_token_id=snake_case_   ,	bos_token_id=snake_case_   ,	eos_token_id=snake_case_   ,	tie_word_embeddings=snake_case_   ,	**snake_case_   ,	)



     def 		snake_case_     (			self  ):

            if self.rope_scaling is None:
                   return

            if not isinstance(self.rope_scaling   ,	snake_case_  ) or len(self.rope_scaling  ) != 2:
                   raise ValueError(
                       '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                       F'got {self.rope_scaling}'  )
            _lowerCamelCase             =   self.rope_scaling.get('type'   ,	snake_case_  )
            _lowerCamelCase             =   self.rope_scaling.get('factor'   ,	snake_case_  )
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                   raise ValueError(
                       F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'  )
            if rope_scaling_factor is None or not isinstance(snake_case_   ,	snake_case_  ) or rope_scaling_factor <= 1.0:
                   raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'  )










# 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			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''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			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					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			:			Optional[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=1000,
    tgt_vocab_size=1000,
    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			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					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


