code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import inspect
  
  from diffusers.utils import is_flax_available
  from diffusers.utils.testing_utils import require_flax
  
  
  if is_flax_available():
  						import jax
  
  
  
  
  
  @require_flax
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case ,					_snake_case			 = self.prepare_init_args_and_inputs_for_common()
  
  							_snake_case			 = self.model_class(**_lowerCAmelCase				)
  							_snake_case			 = model.init(inputs_dict["prng_key"],						inputs_dict["sample"]				)
  							jax.lax.stop_gradient(_lowerCAmelCase				)
  
  							_snake_case			 = model.apply(_lowerCAmelCase,						inputs_dict["sample"]				)
  
  							if isinstance(_lowerCAmelCase,						_lowerCAmelCase				):
  									_snake_case			 = output.sample
  
  							self.assertIsNotNone(_lowerCAmelCase				)
  							_snake_case			 = inputs_dict["sample"].shape
  							self.assertEqual(output.shape,						_lowerCAmelCase,						"Input and output shapes do not match"				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case ,					_snake_case			 = self.prepare_init_args_and_inputs_for_common()
  
  							_snake_case			 = 16
  							_snake_case			 = (16, 32)
  
  							_snake_case			 = self.model_class(**_lowerCAmelCase				)
  							_snake_case			 = model.init(inputs_dict["prng_key"],						inputs_dict["sample"]				)
  							jax.lax.stop_gradient(_lowerCAmelCase				)
  
  							_snake_case			 = model.apply(_lowerCAmelCase,						inputs_dict["sample"]				)
  
  							if isinstance(_lowerCAmelCase,						_lowerCAmelCase				):
  									_snake_case			 = output.sample
  
  							self.assertIsNotNone(_lowerCAmelCase				)
  							_snake_case			 = inputs_dict["sample"].shape
  							self.assertEqual(output.shape,						_lowerCAmelCase,						"Input and output shapes do not match"				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = "kwargs" in inspect.signature(self.model_class.__init__				).parameters
  							_snake_case			 = len(self.model_class._deprecated_kwargs				) > 0
  
  							if has_kwarg_in_model_class and not has_deprecated_kwarg:
  									raise ValueError(
  									    f'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
  									    " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
  									    " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
  									    " [<deprecated_argument>]`"				)
  
  							if not has_kwarg_in_model_class and has_deprecated_kwarg:
  									raise ValueError(
  									    f'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
  									    " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
  									    f''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
  									    " from `_deprecated_kwargs = [<deprecated_argument>]`"				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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'''
  
  
  
  
  
  
  					lowercase__					=				42
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						0				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = 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				),						)
  
  
  
  
  
  