



'''simple docstring'''
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	@cached_property
	def      a__ (		self			) ->							List[Any]:
				return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""			)


	@cached_property
	def      a__ (		self			) ->							str:
				return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""			)


	def      a__ (		self			) ->							Tuple:
				A:      List[Any]														=   self.tokenizer_ab
				A:      Any														=   """ I am a small frog."""
				A:      Tuple														=   tok([src_text]					,   padding=A					,   truncation=A			)["""input_ids"""]
				A:      Tuple														=   tok.batch_decode(A					,   skip_special_tokens=A					,   clean_up_tokenization_spaces=A			)[0]
				assert src_text == decoded


	def      a__ (		self			) ->							Union[str, Any]:
				A:      List[Any]														=   self.rust_tokenizer_ab
				A:      Optional[int]														=   """ I am a small frog."""
				A:      Optional[int]														=   tok([src_text]					,   padding=A					,   truncation=A			)["""input_ids"""]
				A:      Tuple														=   tok.batch_decode(A					,   skip_special_tokens=A					,   clean_up_tokenization_spaces=A			)[0]
				assert src_text == decoded


	def      a__ (		self			) ->							Tuple:
				assert self.tokenizer_ab.add_prefix_space
				assert self.tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]





	def      a__ (		self			) ->							Optional[int]:
				assert self.rust_tokenizer_ab.add_prefix_space
				assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]			).input_ids == [[55_02, 2], [55_02, 2]]










'''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


__SCREAMING_SNAKE_CASE :      int             =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE :      Optional[int]             ={
    '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   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_					):
	"""simple docstring"""


	A__    :    List[str]						=				'''convnext'''


	def __init__(		self					,   A=3					,   A=4					,   A=4					,   A=None					,   A=None					,   A="gelu"					,   A=0.02					,   A=1e-12					,   A=1e-6					,   A=0.0					,   A=2_24					,   A=None					,   A=None					,   **A					,   ) ->							Dict:
				super().__init__(**A			)

				A:      List[str]														=   num_channels
				A:      Optional[Any]														=   patch_size
				A:      List[str]														=   num_stages
				A:      Tuple														=   [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
				A:      List[Any]														=   [3, 3, 9, 3] if depths is None else depths
				A:      int														=   hidden_act
				A:      int														=   initializer_range
				A:      Optional[int]														=   layer_norm_eps
				A:      Tuple														=   layer_scale_init_value
				A:      int														=   drop_path_rate
				A:      Tuple														=   image_size
				A:      Union[str, Any]														=   ["""stem"""] + [f'stage{idx}' for idx in range(1					,   len(self.depths			) + 1			)]
				A							,		A:      List[str]														=   get_aligned_output_features_output_indices(
				    out_features=A					,   out_indices=A					,   stage_names=self.stage_names			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	A__    :    Any						=				version.parse('''1.11'''					)


	@property
	def      a__ (		self			) ->							Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
				    ]			)





	@property
	def      a__ (		self			) ->							float:
				return 1e-5





