





# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase 					=      'facebook/wmt19-en-de'
_lowerCamelCase 					=      FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase 					=      FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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
_lowerCamelCase 					=      'tiny-wmt19-en-de'
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-de






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__     )
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
							lowerCamelCase_							     =       field(default="text-classification"					,	metadata={"include_in_asdict_even_if_is_default": True}     )
							lowerCamelCase_							     =       Features({"text": Value("string"     )}     )
							lowerCamelCase_							     =       Features({"labels": ClassLabel}     )
							lowerCamelCase_							     =       "text"
							lowerCamelCase_							     =       "labels"


							def   _snake_case	(				self     :Any				,					__A     :Dict		) -> Optional[Any]:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]				,					__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__								=   self.label_schema.copy()
									SCREAMING_SNAKE_CASE__								=   features[self.label_column]
									SCREAMING_SNAKE_CASE__								=   label_schema
									return task_template


							@property
							def   _snake_case	(				self     :str		) -> Dict[str, str]:



									"""simple docstring"""



									return {
									    self.text_column: "text",
									    self.label_column: "labels",
									}