
'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_							: str =							logging.get_logger(__name__)

UpperCAmelCase_							: Optional[int] =							{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		'xlm-roberta-xl'



    def __init__(						self   :							str,__A   :							Optional[Any]=2_5_0_8_8_0,__A   :							str=2_5_6_0,__A   :							Dict=3_6,__A   :							int=3_2,__A   :							int=1_0_2_4_0,__A   :							Union[str, Any]="gelu",__A   :							Optional[Any]=0.1,__A   :							Tuple=0.1,__A   :							Any=5_1_4,__A   :							int=1,__A   :							Dict=0.02,__A   :							Any=1e-05,__A   :							str=1,__A   :							Optional[int]=0,__A   :							Tuple=2,__A   :							Dict="absolute",__A   :							Dict=True,__A   :							str=None,**__A   :							Any,):
          super().__init__(pad_token_id=__A,bos_token_id=__A,eos_token_id=__A,**__A  )
          _lowerCamelCase					: Tuple		    = vocab_size
          _lowerCamelCase					: List[Any]		    = hidden_size
          _lowerCamelCase					: Union[str, Any]		    = num_hidden_layers
          _lowerCamelCase					: Optional[int]		    = num_attention_heads
          _lowerCamelCase					: Dict		    = hidden_act
          _lowerCamelCase					: Tuple		    = intermediate_size
          _lowerCamelCase					: int		    = hidden_dropout_prob
          _lowerCamelCase					: List[Any]		    = attention_probs_dropout_prob
          _lowerCamelCase					: List[Any]		    = max_position_embeddings
          _lowerCamelCase					: List[Any]		    = type_vocab_size
          _lowerCamelCase					: Optional[Any]		    = initializer_range
          _lowerCamelCase					: str		    = layer_norm_eps
          _lowerCamelCase					: Optional[int]		    = position_embedding_type
          _lowerCamelCase					: int		    = use_cache
          _lowerCamelCase					: str		    = classifier_dropout


class 			UpperCAmelCase__    (							A			):



    @property
    def 	lowerCamelCase_	(						self   :							int  ):
          if self.task == "multiple-choice":
                _lowerCamelCase					: Optional[int]		    = {0: "batch", 1: "choice", 2: "sequence"}
          else:
                _lowerCamelCase					: Optional[int]		    = {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]  )



# 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


