'''simple docstring'''
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


a=	logging.get_logger(__name__)

a=	{
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}






class       __lowercase		(    A_      ,				A_    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    "convnext"







     def __init__(		self  ,      _lowerCamelCase=3  ,      _lowerCamelCase=4  ,      _lowerCamelCase=4  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1E-1_2  ,      _lowerCamelCase=1E-6  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=2_2_4  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_A     )

         __UpperCamelCase						:						Union[str, Any]		     =	num_channels
         __UpperCamelCase						:						Optional[Any]		     =	patch_size
         __UpperCamelCase						:						Dict		     =	num_stages
         __UpperCamelCase						:						Optional[Any]		     =	[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
         __UpperCamelCase						:						Tuple		     =	[3, 3, 9, 3] if depths is None else depths
         __UpperCamelCase						:						Dict		     =	hidden_act
         __UpperCamelCase						:						Optional[Any]		     =	initializer_range
         __UpperCamelCase						:						str		     =	layer_norm_eps
         __UpperCamelCase						:						List[str]		     =	layer_scale_init_value
         __UpperCamelCase						:						List[Any]		     =	drop_path_rate
         __UpperCamelCase						:						Union[str, Any]		     =	image_size
         __UpperCamelCase						:						Union[str, Any]		     =	['stem'] + [f"""stage{idx}""" for idx in range(1  ,      len(self.depths     ) + 1     )]
         __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	get_aligned_output_features_output_indices(
             out_features=_A  ,      out_indices=_A  ,      stage_names=self.stage_names     )






class       __lowercase		(    A_    ):







     """simple docstring"""






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







     @property
     def lowerCAmelCase  (		self     ):
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]     )







     @property
     def lowerCAmelCase  (		self     ):
         return 1E-5





from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

