

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__	   = logging.get_logger(__name__)

lowerCAmelCase__	   = {
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class 					a__						(					UpperCamelCase__			):
			"""simple docstring"""







			__lowerCamelCase        =       'deit'


			def __init__(    self      ,      lowercase=768      ,      lowercase=12      ,      lowercase=12      ,      lowercase=3072      ,      lowercase="gelu"      ,      lowercase=0.0      ,      lowercase=0.0      ,      lowercase=0.02      ,      lowercase=1e-12      ,      lowercase=224      ,      lowercase=16      ,      lowercase=3      ,      lowercase=True      ,      lowercase=16      ,      **lowercase      ,      )  ->						List[str]:
							'''simple docstring'''






							super().__init__(**__A       )

							A__        =			hidden_size
							A__        =			num_hidden_layers
							A__        =			num_attention_heads
							A__        =			intermediate_size
							A__        =			hidden_act
							A__        =			hidden_dropout_prob
							A__        =			attention_probs_dropout_prob
							A__        =			initializer_range
							A__        =			layer_norm_eps
							A__        =			image_size
							A__        =			patch_size
							A__        =			num_channels
							A__        =			qkv_bias
							A__        =			encoder_stride

class 					a__						(					UpperCamelCase__			):
			"""simple docstring"""







			__lowerCamelCase        =       version.parse('1.11'			)


			@property
			def      UpperCamelCase		(    self       )  ->						Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]       )


			@property
			def      UpperCamelCase		(    self       )  ->						float:
							'''simple docstring'''






							return 1e-4




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 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

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

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			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       )       )

