



# 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

a_								=       """facebook/wmt19-en-de"""
a_								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
a_								=       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,
    )
)

a_								=       FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
a_								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
a_								=       tiny_model(**batch)

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

# Save
a_								=       """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











from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(
        features=__UpperCAmelCase						,					cache_dir=__UpperCAmelCase						,					keep_in_memory=__UpperCAmelCase						,					streaming=__UpperCAmelCase						,					num_proc=__UpperCAmelCase						,					**__UpperCAmelCase						,					)
    __lowerCamelCase    				=     Generator(
        cache_dir=__UpperCAmelCase						,					features=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					gen_kwargs=__UpperCAmelCase						,					**__UpperCAmelCase						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Build iterable dataset
    if self.streaming:
       __lowerCamelCase    				=     self.builder.as_streaming_dataset(split='''train'''					)
    # Build regular (map-style) dataset
    else:
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase						,					download_mode=__UpperCAmelCase						,					verification_mode=__UpperCAmelCase						,					base_path=__UpperCAmelCase						,					num_proc=self.num_proc						,					)
       __lowerCamelCase    				=     self.builder.as_dataset(
           split='''train'''						,					verification_mode=__UpperCAmelCase						,					in_memory=self.keep_in_memory					)
    return dataset






