


# 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

lowerCAmelCase							=						"""tiny-wmt19-en-ru"""

# Build

# borrowed from a test
lowerCAmelCase							=						[
    """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>""",
]
lowerCAmelCase							=						dict(zip(vocab, range(len(vocab))))
lowerCAmelCase							=						["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
				lowerCAmelCase							=						Path(tmpdirname)
				lowerCAmelCase							=						build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
				lowerCAmelCase							=						build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
				lowerCAmelCase							=						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))

				lowerCAmelCase							=						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,
				)

lowerCAmelCase							=						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,
)

lowerCAmelCase							=						FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
lowerCAmelCase							=						tokenizer(["""Making tiny model"""], return_tensors="""pt""")
lowerCAmelCase							=						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





from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase							=						logging.get_logger(__name__)

lowerCAmelCase							=						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		int        =	'''bert'''


	def __init__(						self							,    lowercase__=3_0_5_2_2							,    lowercase__=7_6_8							,    lowercase__=1_2							,    lowercase__=1_2							,    lowercase__=3_0_7_2							,    lowercase__="gelu"							,    lowercase__=0.1							,    lowercase__=0.1							,    lowercase__=5_1_2							,    lowercase__=2							,    lowercase__=0.0_2							,    lowercase__=1e-12							,    lowercase__=0							,    lowercase__="absolute"							,    lowercase__=True							,    lowercase__=None							,    **lowercase__							,    ):
							super().__init__(pad_token_id=lowercase__							,    **lowercase__)

							__UpperCAmelCase :       Tuple  						=  vocab_size
							__UpperCAmelCase :       Any  						=  hidden_size
							__UpperCAmelCase :       str  						=  num_hidden_layers
							__UpperCAmelCase :       Dict  						=  num_attention_heads
							__UpperCAmelCase :       Tuple  						=  hidden_act
							__UpperCAmelCase :       int  						=  intermediate_size
							__UpperCAmelCase :       List[Any]  						=  hidden_dropout_prob
							__UpperCAmelCase :       Union[str, Any]  						=  attention_probs_dropout_prob
							__UpperCAmelCase :       List[Any]  						=  max_position_embeddings
							__UpperCAmelCase :       Union[str, Any]  						=  type_vocab_size
							__UpperCAmelCase :       List[Any]  						=  initializer_range
							__UpperCAmelCase :       List[Any]  						=  layer_norm_eps
							__UpperCAmelCase :       List[str]  						=  position_embedding_type
							__UpperCAmelCase :       Optional[Any]  						=  use_cache
							__UpperCAmelCase :       List[Any]  						=  classifier_dropout






class        lowerCamelCase      (     _UpperCamelCase   ):


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

