'''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      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            _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(lowerCAmelCase__       )['last_hidden_state']
            _A=	tf.TensorShape((1, 10, 768)       )
            self.assertEqual(output.shape				,      lowerCAmelCase__       )
            # 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       )       )
import warnings
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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Tuple		="""segformer"""



     def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=4				,      lowerCAmelCase__=[2, 2, 2, 2]				,      lowerCAmelCase__=[8, 4, 2, 1]				,      lowerCAmelCase__=[32, 64, 160, 256]				,      lowerCAmelCase__=[7, 3, 3, 3]				,      lowerCAmelCase__=[4, 2, 2, 2]				,      lowerCAmelCase__=[1, 2, 5, 8]				,      lowerCAmelCase__=[4, 4, 4, 4]				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=1E-6				,      lowerCAmelCase__=256				,      lowerCAmelCase__=255				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
                   warnings.warn(
                       'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                       ' removed, as the behaviour will default to that of reshape_last_stage = True.'				,      lowerCAmelCase__				,      )

            _A=	num_channels
            _A=	num_encoder_blocks
            _A=	depths
            _A=	sr_ratios
            _A=	hidden_sizes
            _A=	patch_sizes
            _A=	strides
            _A=	mlp_ratios
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	classifier_dropout_prob
            _A=	initializer_range
            _A=	drop_path_rate
            _A=	layer_norm_eps
            _A=	decoder_hidden_size
            _A=	kwargs.get('reshape_last_stage'				,      lowerCAmelCase__       )
            _A=	semantic_loss_ignore_index





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=version.parse("""1.11"""			)



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



     @property
     def 			a__  (					self       ):
            return 1E-4





     @property
     def 			a__  (					self       ):
            return 12