






"""simple docstring"""


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

_UpperCamelCase    :   str   			= {
    '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 						snake_case  (  __lowerCamelCase	):
    __magic_name__   					=					'segformer'





    def __init__(							self     : Union[str, Any]    ,		A     : Optional[int]=3    ,		A     : Any=4    ,		A     : int=[2, 2, 2, 2]    ,		A     : Optional[Any]=[8, 4, 2, 1]    ,		A     : Union[str, Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,		A     : int=[7, 3, 3, 3]    ,		A     : Dict=[4, 2, 2, 2]    ,		A     : str=[1, 2, 5, 8]    ,		A     : List[str]=[4, 4, 4, 4]    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : List[Any]=0.0    ,		A     : Tuple=0.1    ,		A     : Optional[int]=0.02    ,		A     : List[Any]=0.1    ,		A     : Optional[int]=1E-6    ,		A     : Optional[int]=2_5_6    ,		A     : Optional[Any]=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**UpperCamelCase_      )

      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.'    ,		UpperCamelCase_    ,		)

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






class 						snake_case  (  __lowerCamelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




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





    @property
    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      return 1E-4





    @property
    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      return 1_2












"""simple docstring"""


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




