






import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir





@dataclass
class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				42





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece.model'   )
							_UpperCAmelCase     =      get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   0   )

							_UpperCAmelCase     =      FakeOriginalTokenizer(vocab_file=UpperCAmelCase   )

							with warnings.catch_warnings(record=UpperCAmelCase   ) as w:
													_UpperCAmelCase     =      SpmConverter(UpperCAmelCase   )
							self.assertEqual(len(UpperCAmelCase   )	,   1   )

							self.assertIn(
							    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
							    ' which is not implemented in the fast tokenizers.'	,   str(w[0].message   )	,   )








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


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"regnet"
	UpperCamelCase__								=				["x", "y"]

	def __init__(							self	,   UpperCAmelCase=3	,   UpperCAmelCase=32	,   UpperCAmelCase=[128, 192, 512, 1088]	,   UpperCAmelCase=[2, 6, 12, 2]	,   UpperCAmelCase=64	,   UpperCAmelCase="y"	,   UpperCAmelCase="relu"	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase   )
							if layer_type not in self.layer_types:
													raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types   )}"""   )
							_UpperCAmelCase     =      num_channels
							_UpperCAmelCase     =      embedding_size
							_UpperCAmelCase     =      hidden_sizes
							_UpperCAmelCase     =      depths
							_UpperCAmelCase     =      groups_width
							_UpperCAmelCase     =      layer_type
							_UpperCAmelCase     =      hidden_act
							# always downsample in the first stage
							_UpperCAmelCase     =      True
