





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  A__    (							unittest.TestCase	):




   """simple docstring"""



   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Tuple =				FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small')
          a__						:	str =				AutoTokenizer.from_pretrained('google/mt5-small')

          a__						:	Union[str, Any] =				tokenizer('Hello there'					,  return_tensors='np').input_ids
          a__						:	Tuple =				tokenizer('Hi I am'					,  return_tensors='np').input_ids

          a__						:	Union[str, Any] =				shift_tokens_right(lowercase					,  model.config.pad_token_id					,  model.config.decoder_start_token_id)

          a__						:	str =				model(lowercase					,  decoder_input_ids=lowercase).logits
          a__						:	Any =				optax.softmax_cross_entropy(lowercase					,  onehot(lowercase					,  logits.shape[-1])).mean()

          a__						:	Optional[int] =				-(labels.shape[-1] * loss.item())

          a__						:	Optional[int] =				-84.91_27
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL


