





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    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =TFCamembertModel.from_pretrained("jplu/tf-camembert-base")

   lowerCamelCase__:      int			  =tf.convert_to_tensor(
       [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       , dtype=tf.intaa       , )  # J'aime le camembert !"

   lowerCamelCase__:      Tuple			  =model(UpperCAmelCase_)["last_hidden_state"]
   lowerCamelCase__:      Union[str, Any]			  =tf.TensorShape((1, 10, 768))
   self.assertEqual(output.shape       , UpperCAmelCase_)
   # compare the actual values for a slice.
   lowerCamelCase__:      List[str]			  =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


a		:       List[Any]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "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     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	'segformer'
      def __init__(      self					:			Dict		,      __lowercase					:			str=3		,      __lowercase					:			Dict=4		,      __lowercase					:			Any=[2, 2, 2, 2]		,      __lowercase					:			Optional[int]=[8, 4, 2, 1]		,      __lowercase					:			List[str]=[32, 64, 160, 256]		,      __lowercase					:			Union[str, Any]=[7, 3, 3, 3]		,      __lowercase					:			Optional[int]=[4, 2, 2, 2]		,      __lowercase					:			Any=[1, 2, 5, 8]		,      __lowercase					:			List[str]=[4, 4, 4, 4]		,      __lowercase					:			Any="gelu"		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.0		,      __lowercase					:			Optional[Any]=0.1		,      __lowercase					:			int=0.02		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			Optional[Any]=1e-6		,      __lowercase					:			Tuple=256		,      __lowercase					:			List[Any]=255		,      **__lowercase					:			Union[str, Any]		,      )							->      List[Any]:
        super().__init__(**__lowercase					)

        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."""		,      __lowercase		,      )

        __UpperCAmelCase							:	Tuple           =  num_channels
        __UpperCAmelCase							:	Any           =  num_encoder_blocks
        __UpperCAmelCase							:	List[Any]           =  depths
        __UpperCAmelCase							:	Dict           =  sr_ratios
        __UpperCAmelCase							:	int           =  hidden_sizes
        __UpperCAmelCase							:	Optional[Any]           =  patch_sizes
        __UpperCAmelCase							:	List[Any]           =  strides
        __UpperCAmelCase							:	List[str]           =  mlp_ratios
        __UpperCAmelCase							:	Tuple           =  num_attention_heads
        __UpperCAmelCase							:	List[str]           =  hidden_act
        __UpperCAmelCase							:	Tuple           =  hidden_dropout_prob
        __UpperCAmelCase							:	Dict           =  attention_probs_dropout_prob
        __UpperCAmelCase							:	Any           =  classifier_dropout_prob
        __UpperCAmelCase							:	Optional[int]           =  initializer_range
        __UpperCAmelCase							:	Optional[Any]           =  drop_path_rate
        __UpperCAmelCase							:	int           =  layer_norm_eps
        __UpperCAmelCase							:	Dict           =  decoder_hidden_size
        __UpperCAmelCase							:	Tuple           =  kwargs.get("""reshape_last_stage"""		,      __lowercase					)
        __UpperCAmelCase							:	int           =  semantic_loss_ignore_index


class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Union[str, Any]							       =	version.parse('1.11'   )
      @property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]					)
      @property
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      float:
        return 1e-4




      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      int:
        return 12


