






'''simple docstring'''







import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	Any									=   FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''       )
							a_		:	Union[str, Any]									=   AutoTokenizer.from_pretrained('''google/mt5-small'''       )

							a_		:	Optional[int]									=   tokenizer('''Hello there'''	,    return_tensors='''np'''       ).input_ids
							a_		:	List[str]									=   tokenizer('''Hi I am'''	,    return_tensors='''np'''       ).input_ids

							a_		:	Dict									=   shift_tokens_right(__SCREAMING_SNAKE_CASE	,    model.config.pad_token_id	,    model.config.decoder_start_token_id       )

							a_		:	Optional[Any]									=   model(__SCREAMING_SNAKE_CASE	,    decoder_input_ids=__SCREAMING_SNAKE_CASE       ).logits
							a_		:	Tuple									=   optax.softmax_cross_entropy(__SCREAMING_SNAKE_CASE	,    onehot(__SCREAMING_SNAKE_CASE	,    logits.shape[-1]       )       ).mean()

							a_		:	Tuple									=   -(labels.shape[-1] * loss.item())

							a_		:	Union[str, Any]									=   -84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1e-4       )












'''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

__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




