




"""simple docstring"""





# 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








"""simple docstring"""





from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class      a__				(	__magic_name__   ):


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    pyspark.sql.DataFrame      ,  UpperCamelCase_      :    Optional[NamedSplit] = None      ,  UpperCamelCase_      :    Optional[Features] = None      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    str = None      ,  UpperCamelCase_      :    bool = False      ,  UpperCamelCase_      :    str = None      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    str = "arrow"      ,  **UpperCamelCase_      :    Tuple      ,  ):


						"""simple docstring"""




						super().__init__(
						    split=UpperCamelCase_      ,  features=UpperCamelCase_      ,  cache_dir=UpperCamelCase_      ,  keep_in_memory=UpperCamelCase_      ,  streaming=UpperCamelCase_      ,  **UpperCamelCase_      ,  )
						__UpperCAmelCase	: List[Any]		=					load_from_cache_file
						__UpperCAmelCase	: List[Any]		=					file_format
						__UpperCAmelCase	: str		=					Spark(
						    df=UpperCamelCase_      ,  features=UpperCamelCase_      ,  cache_dir=UpperCamelCase_      ,  working_dir=UpperCamelCase_      ,  **UpperCamelCase_      ,  )


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						if self.streaming:
									return self.builder.as_streaming_dataset(split=self.split)
						__UpperCAmelCase	: List[Any]		=					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
						self.builder.download_and_prepare(
						    download_mode=UpperCamelCase_      ,  file_format=self._file_format      ,  )
						return self.builder.as_dataset(split=self.split)


