


from typing import List

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


A   :					Dict	=      logging.get_logger(__name__)

A   :					int	=      {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "efficientformer"





       def __init__(     self							:    str      ,   __lowerCamelCase							:    List[int] = [3, 2, 6, 4]      ,   __lowerCamelCase							:    List[int] = [48, 96, 224, 448]      ,   __lowerCamelCase							:    List[bool] = [True, True, True, True]      ,   __lowerCamelCase							:    int = 448      ,   __lowerCamelCase							:    int = 32      ,   __lowerCamelCase							:    int = 4      ,   __lowerCamelCase							:    int = 7      ,   __lowerCamelCase							:    int = 5      ,   __lowerCamelCase							:    int = 8      ,   __lowerCamelCase							:    int = 4      ,   __lowerCamelCase							:    float = 0.0      ,   __lowerCamelCase							:    int = 16      ,   __lowerCamelCase							:    int = 3      ,   __lowerCamelCase							:    int = 3      ,   __lowerCamelCase							:    int = 3      ,   __lowerCamelCase							:    int = 2      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    float = 0.0      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    float = 1E-5      ,   __lowerCamelCase							:    str = "gelu"      ,   __lowerCamelCase							:    float = 0.0_2      ,   __lowerCamelCase							:    float = 1E-1_2      ,   __lowerCamelCase							:    int = 224      ,   __lowerCamelCase							:    float = 1E-0_5      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )

             lowerCamelCase__	:       Union[str, Any]					=						hidden_act
             lowerCamelCase__	:       List[Any]					=						hidden_dropout_prob
             lowerCamelCase__	:       str					=						hidden_sizes
             lowerCamelCase__	:       str					=						num_hidden_layers
             lowerCamelCase__	:       Optional[int]					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						initializer_range
             lowerCamelCase__	:       Optional[int]					=						layer_norm_eps
             lowerCamelCase__	:       Union[str, Any]					=						patch_size
             lowerCamelCase__	:       str					=						num_channels
             lowerCamelCase__	:       str					=						depths
             lowerCamelCase__	:       Tuple					=						mlp_expansion_ratio
             lowerCamelCase__	:       Dict					=						downsamples
             lowerCamelCase__	:       Union[str, Any]					=						dim
             lowerCamelCase__	:       List[Any]					=						key_dim
             lowerCamelCase__	:       Optional[Any]					=						attention_ratio
             lowerCamelCase__	:       int					=						resolution
             lowerCamelCase__	:       int					=						pool_size
             lowerCamelCase__	:       Optional[int]					=						downsample_patch_size
             lowerCamelCase__	:       List[Any]					=						downsample_stride
             lowerCamelCase__	:       Union[str, Any]					=						downsample_pad
             lowerCamelCase__	:       List[str]					=						drop_path_rate
             lowerCamelCase__	:       str					=						num_metaad_blocks
             lowerCamelCase__	:       str					=						distillation
             lowerCamelCase__	:       Dict					=						use_layer_scale
             lowerCamelCase__	:       Optional[Any]					=						layer_scale_init_value
             lowerCamelCase__	:       Optional[Any]					=						image_size
             lowerCamelCase__	:       int					=						batch_norm_eps






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 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

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

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]      ,   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 ) )


