





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        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''				)
         __snake_case								=       AutoTokenizer.from_pretrained('''google/mt5-small'''				)

         __snake_case								=       tokenizer('''Hello there'''    ,      return_tensors='''np'''				).input_ids
         __snake_case								=       tokenizer('''Hi I am'''    ,      return_tensors='''np'''				).input_ids

         __snake_case								=       shift_tokens_right(a__    ,      model.config.pad_token_id    ,      model.config.decoder_start_token_id				)

         __snake_case								=       model(a__    ,      decoder_input_ids=a__				).logits
         __snake_case								=       optax.softmax_cross_entropy(a__    ,      onehot(a__    ,      logits.shape[-1]				)				).mean()

         __snake_case								=       -(labels.shape[-1] * loss.item())

         __snake_case								=       -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_           =      {}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['NllbTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['NllbTokenizerFast']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb import NllbTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

