

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_						:     Optional[int]			  =				logging.get_logger(__name__)

A_						:     Union[str, Any]			  =				{
    '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    A_				( _a	,       _a  ):


    '''simple docstring'''






    a__       							=    "convnext"


    def __init__(self       , lowercase__=3       , lowercase__=4       , lowercase__=4       , lowercase__=None       , lowercase__=None       , lowercase__="gelu"       , lowercase__=0.02       , lowercase__=1E-12       , lowercase__=1E-6       , lowercase__=0.0       , lowercase__=224       , lowercase__=None       , lowercase__=None       , **lowercase__       , )       -> Optional[int]:
        super().__init__(**lowercase__					)

        __UpperCAmelCase			   =     num_channels
        __UpperCAmelCase			   =     patch_size
        __UpperCAmelCase			   =     num_stages
        __UpperCAmelCase			   =     [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
        __UpperCAmelCase			   =     [3, 3, 9, 3] if depths is None else depths
        __UpperCAmelCase			   =     hidden_act
        __UpperCAmelCase			   =     initializer_range
        __UpperCAmelCase			   =     layer_norm_eps
        __UpperCAmelCase			   =     layer_scale_init_value
        __UpperCAmelCase			   =     drop_path_rate
        __UpperCAmelCase			   =     image_size
        __UpperCAmelCase			   =     ['''stem'''] + [F'''stage{idx}''' for idx in range(1       , len(self.depths					) + 1					)]
        __UpperCAmelCase   ,       __UpperCAmelCase			   =     get_aligned_output_features_output_indices(
            out_features=lowercase__       , out_indices=lowercase__       , stage_names=self.stage_names					)






class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    version.parse("1.11"  )


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






    @property
    def        lowerCAmelCase_  (self					)       -> float:
        return 1E-5







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/




