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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}




class 					_UpperCAmelCase			(     __SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE  ):



 '''simple docstring'''




 __A			       =					'''resnet'''
 __A			       =					['''basic''', '''bottleneck''']







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Tuple=3							, lowercase_				:   Optional[int]=64							, lowercase_				:   str=[256, 512, 1024, 2048]							, lowercase_				:   Any=[3, 4, 6, 3]							, lowercase_				:   Tuple="bottleneck"							, lowercase_				:   Dict="relu"							, lowercase_				:   str=False							, lowercase_				:   Tuple=None							, lowercase_				:   int=None							, **lowercase_				:   Union[str, Any]							, )						->	Dict:


   """simple docstring"""







   super().__init__(**UpperCamelCase__)
   if layer_type not in self.layer_types:
     raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types)}')
   _UpperCamelCase           =    num_channels
   _UpperCamelCase           =    embedding_size
   _UpperCamelCase           =    hidden_sizes
   _UpperCamelCase           =    depths
   _UpperCamelCase           =    layer_type
   _UpperCamelCase           =    hidden_act
   _UpperCamelCase           =    downsample_in_first_stage
   _UpperCamelCase           =    ['''stem'''] + [f'stage{idx}' for idx in range(1							, len(UpperCamelCase__) + 1)]
   _UpperCamelCase           =    get_aligned_output_features_output_indices(
       out_features=UpperCamelCase__							, out_indices=UpperCamelCase__							, stage_names=self.stage_names)




class 					_UpperCAmelCase			(     __SCREAMING_SNAKE_CASE  ):



 '''simple docstring'''




 __A			       =					version.parse('''1.11'''  )







 @property
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Mapping[str, Mapping[int, str]]:


   """simple docstring"""







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







 @property
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	float:


   """simple docstring"""







   return 1e-3






'''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 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						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      )      )


