from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[
    """python""",
    """tqdm""",
    """regex""",
    """requests""",
    """packaging""",
    """filelock""",
    """numpy""",
    """tokenizers""",
    """huggingface-hub""",
    """safetensors""",
    """accelerate""",
    """pyyaml""",
]

for pkg in pkgs_to_check_at_runtime:
  if pkg in deps:
    if pkg == "tokenizers":
      # must be loaded here, or else tqdm check may fail
      from .utils import is_tokenizers_available

      if not is_tokenizers_available():
        continue  # not required, check version only if installed
    elif pkg == "accelerate":
      # must be loaded here, or else tqdm check may fail
      from .utils import is_accelerate_available

      # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
      # Transformers with PyTorch
      if not is_accelerate_available():
        continue  # not required, check version only if installed

    require_version_core(deps[pkg])
  else:
    raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')



def   __A		(			_A							,				_A=None					):



 """simple docstring"""


 require_version(deps[pkg]							,				_A					)



import unittest

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


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


