



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							: Optional[Any]						 =	logging.get_logger(__name__)

A_							: int						 =	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """fnet"""







 def __init__(							self  ,a_=32_000  ,a_=768  ,a_=12  ,a_=3_072  ,a_="gelu_new"  ,a_=0.1  ,a_=512  ,a_=4  ,a_=0.02  ,a_=1E-1_2  ,a_=False  ,a_=512  ,a_=3  ,a_=1  ,a_=2  ,**a_  ,)		->   int:
        super().__init__(pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_							)

        _UpperCAmelCase  :						Dict			     = vocab_size
        _UpperCAmelCase  :						Dict			     = max_position_embeddings
        _UpperCAmelCase  :						Union[str, Any]			     = hidden_size
        _UpperCAmelCase  :						Union[str, Any]			     = num_hidden_layers
        _UpperCAmelCase  :						Tuple			     = intermediate_size
        _UpperCAmelCase  :						Optional[Any]			     = hidden_act
        _UpperCAmelCase  :						List[Any]			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[Any]			     = initializer_range
        _UpperCAmelCase  :						int			     = type_vocab_size
        _UpperCAmelCase  :						List[str]			     = layer_norm_eps
        _UpperCAmelCase  :						int			     = use_tpu_fourier_optimizations
        _UpperCAmelCase  :						str			     = tpu_short_seq_length








'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



