






"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase    :   Dict   			=						logging.get_logger(__name__)

_UpperCamelCase    :   str   			=						{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''glpn'''





    def __init__(							self     : List[str]    ,		A     : Dict=3    ,		A     : str=4    ,		A     : Any=[2, 2, 2, 2]    ,		A     : int=[8, 4, 2, 1]    ,		A     : Dict=[3_2, 6_4, 1_6_0, 2_5_6]    ,		A     : Tuple=[7, 3, 3, 3]    ,		A     : Dict=[4, 2, 2, 2]    ,		A     : Union[str, Any]=[1, 2, 5, 8]    ,		A     : List[str]=[4, 4, 4, 4]    ,		A     : Tuple="gelu"    ,		A     : List[str]=0.0    ,		A     : Tuple=0.0    ,		A     : str=0.02    ,		A     : Dict=0.1    ,		A     : Tuple=1E-6    ,		A     : List[str]=6_4    ,		A     : Tuple=1_0    ,		A     : List[str]=-1    ,		**A     : Dict    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Union[str, Any]    =       num_channels
      a					:      Optional[Any]    =       num_encoder_blocks
      a					:      int    =       depths
      a					:      Any    =       sr_ratios
      a					:      Tuple    =       hidden_sizes
      a					:      int    =       patch_sizes
      a					:      Optional[Any]    =       strides
      a					:      Any    =       mlp_ratios
      a					:      List[str]    =       num_attention_heads
      a					:      Any    =       hidden_act
      a					:      int    =       hidden_dropout_prob
      a					:      str    =       attention_probs_dropout_prob
      a					:      int    =       initializer_range
      a					:      List[Any]    =       drop_path_rate
      a					:      Optional[Any]    =       layer_norm_eps
      a					:      Union[str, Any]    =       decoder_hidden_size
      a					:      Union[str, Any]    =       max_depth
      a					:      List[str]    =       head_in_index












"""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      )      )




