


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_			       =      {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_			       =      [
           'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MvpForCausalLM',
           'MvpForConditionalGeneration',
           'MvpForQuestionAnswering',
           'MvpForSequenceClassification',
           'MvpModel',
           'MvpPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

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








'''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 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        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(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

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

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

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

