
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowercase_       :						Dict     					=	{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			"""albert"""
     def __init__(     self  ,     snake_case__=30000  ,     snake_case__=128  ,     snake_case__=4096  ,     snake_case__=12  ,     snake_case__=1  ,     snake_case__=64  ,     snake_case__=16384  ,     snake_case__=1  ,     snake_case__="gelu_new"  ,     snake_case__=0  ,     snake_case__=0  ,     snake_case__=512  ,     snake_case__=2  ,     snake_case__=0.02  ,     snake_case__=1E-12  ,     snake_case__=0.1  ,     snake_case__="absolute"  ,     snake_case__=0  ,     snake_case__=2  ,     snake_case__=3  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(pad_token_id=snake_case__  ,     bos_token_id=snake_case__  ,     eos_token_id=snake_case__  ,     **snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = vocab_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = embedding_size
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = hidden_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = num_hidden_layers
       _SCREAMING_SNAKE_CASE   :				Any							    = num_hidden_groups
       _SCREAMING_SNAKE_CASE   :				Tuple							    = num_attention_heads
       _SCREAMING_SNAKE_CASE   :				Dict							    = inner_group_num
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = hidden_act
       _SCREAMING_SNAKE_CASE   :				List[str]							    = intermediate_size
       _SCREAMING_SNAKE_CASE   :				str							    = hidden_dropout_prob
       _SCREAMING_SNAKE_CASE   :				int							    = attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE   :				List[str]							    = max_position_embeddings
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = type_vocab_size
       _SCREAMING_SNAKE_CASE   :				Tuple							    = initializer_range
       _SCREAMING_SNAKE_CASE   :				List[str]							    = layer_norm_eps
       _SCREAMING_SNAKE_CASE   :				Tuple							    = classifier_dropout_prob
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = position_embedding_type
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE   :				Tuple							    = {0: "batch", 1: "choice", 2: "sequence"}
       else:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]				)





"""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 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,     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				)				)



