"""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

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

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

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

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

# Save
SCREAMING_SNAKE_CASE__      						=  "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 abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

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






										"""simple docstring"""

										snake_case								=			path_or_paths
										snake_case								=			split if split or isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else 'train'
										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

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






										"""simple docstring"""

										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass


