



import copy
import re






class 						lowercase		:






	"""simple docstring"""


	a__ :   List[Any]						  =				'hp'
	a__ :   str						  =				{}
	a__ :   Tuple						  =				None




	@classmethod
	def     _SCREAMING_SNAKE_CASE						(      cls      :		Any		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		Optional[int]	)     ->					int:
							UpperCAmelCase_=  prefix
							UpperCAmelCase_=  defaults
							cls.build_naming_info()




	@staticmethod
	def     _SCREAMING_SNAKE_CASE						(      __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		List[str]	)     ->					Union[str, Any]:



							if len(__UpperCAmelCase	) == 0:
													return ""
							UpperCAmelCase_=  None
							if any(char.isdigit() for char in word	):
													raise Exception(F"""Parameters should not contain numbers: \'{word}\' contains a number"""	)
							if word in info["short_word"]:
													return info["short_word"][word]
							for prefix_len in range(1		,    len(__UpperCAmelCase	) + 1	):
													UpperCAmelCase_=  word[:prefix_len]
													if prefix in info["reverse_short_word"]:
																			continue
													else:
																			UpperCAmelCase_=  prefix
																			break

							if short_word is None:
													# Paranoid fallback
													def int_to_alphabetic(__UpperCAmelCase      :		str	):
																			UpperCAmelCase_=  """"""
																			while integer != 0:
																									UpperCAmelCase_=  chr(ord("""A"""	) + integer % 10	) + s
																									integer //= 10
																			return s

													UpperCAmelCase_=  0
													while True:
																			UpperCAmelCase_=  word + """#""" + int_to_alphabetic(__UpperCAmelCase	)
																			if sword in info["reverse_short_word"]:
																									continue
																			else:
																									UpperCAmelCase_=  sword
																									break

							UpperCAmelCase_=  short_word
							UpperCAmelCase_=  word
							return short_word




	@staticmethod
	def     _SCREAMING_SNAKE_CASE						(      __UpperCAmelCase      :		Optional[Any]		,    __UpperCAmelCase      :		Tuple	)     ->					Optional[int]:
							UpperCAmelCase_=  param_name.split("""_"""	)

							UpperCAmelCase_=  [TrialShortNamer.shortname_for_word(__UpperCAmelCase		,    __UpperCAmelCase	) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							UpperCAmelCase_=  ["""""", """_"""]

							for separator in separators:
													UpperCAmelCase_=  separator.join(__UpperCAmelCase	)
													if shortname not in info["reverse_short_param"]:
																			UpperCAmelCase_=  shortname
																			UpperCAmelCase_=  param_name
																			return shortname

							return param_name




	@staticmethod
	def     _SCREAMING_SNAKE_CASE						(      __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		Optional[Any]	)     ->					Optional[Any]:
							UpperCAmelCase_=  TrialShortNamer.shortname_for_key(__UpperCAmelCase		,    __UpperCAmelCase	)
							UpperCAmelCase_=  short_name
							UpperCAmelCase_=  param_name




	@classmethod
	def     _SCREAMING_SNAKE_CASE						(      cls      :		int	)     ->					Dict:



							if cls.NAMING_INFO is not None:
													return

							UpperCAmelCase_=  {
							    """short_word""": {},
							    """reverse_short_word""": {},
							    """short_param""": {},
							    """reverse_short_param""": {},
							}

							UpperCAmelCase_=  list(cls.DEFAULTS.keys()	)

							for k in field_keys:
													cls.add_new_param_name(__UpperCAmelCase		,    __UpperCAmelCase	)

							UpperCAmelCase_=  info




	@classmethod
	def     _SCREAMING_SNAKE_CASE						(      cls      :		List[str]		,    __UpperCAmelCase      :		Any	)     ->					Tuple:
							cls.build_naming_info()
							assert cls.PREFIX is not None
							UpperCAmelCase_=  [copy.copy(cls.PREFIX	)]

							for k, v in params.items():
													if k not in cls.DEFAULTS:
																			raise Exception(F"""You should provide a default value for the param name {k} with value {v}"""	)
													if v == cls.DEFAULTS[k]:
																			# The default value is not added to the name
																			continue

													UpperCAmelCase_=  cls.NAMING_INFO["""short_param"""][k]

													if isinstance(__UpperCAmelCase		,    __UpperCAmelCase	):
																			UpperCAmelCase_=  1 if v else 0

													UpperCAmelCase_=  """""" if isinstance(__UpperCAmelCase		,    (int, float)	) else """-"""
													UpperCAmelCase_=  F"""{key}{sep}{v}"""
													name.append(__UpperCAmelCase	)

							return "_".join(__UpperCAmelCase	)




	@classmethod
	def     _SCREAMING_SNAKE_CASE						(      cls      :		Optional[int]		,    __UpperCAmelCase      :		Tuple	)     ->					List[Any]:
							UpperCAmelCase_=  repr[len(cls.PREFIX	) + 1 :]
							if repr == "":
													UpperCAmelCase_=  []
							else:
													UpperCAmelCase_=  repr.split("""_"""	)

							UpperCAmelCase_=  {}

							for value in values:
													if "-" in value:
																			UpperCAmelCase_,  UpperCAmelCase_=  value.split("""-"""	)
													else:
																			UpperCAmelCase_=  re.sub("""[0-9.]"""		,    """"""		,    __UpperCAmelCase	)
																			UpperCAmelCase_=  float(re.sub("""[^0-9.]"""		,    """"""		,    __UpperCAmelCase	)	)

													UpperCAmelCase_=  cls.NAMING_INFO["""reverse_short_param"""][p_k]

													UpperCAmelCase_=  p_v

							for k in cls.DEFAULTS:
													if k not in parameters:
																			UpperCAmelCase_=  cls.DEFAULTS[k]

							return parameters









'''simple docstring'''





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=True					,  A=True					,  A=True					,  A=True					,  A=99					,  A=32					,  A=5					,  A=4					,  A=37					,  A="gelu"					,  A=0.1					,  A=0.1					,  A=512					,  A=16					,  A=2					,  A=0.02					,  A=4					,  )       ->	List[str]:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    is_training
      _a           =    use_attention_mask
      _a           =    use_token_type_ids
      _a           =    use_labels
      _a           =    vocab_size
      _a           =    hidden_size
      _a           =    num_hidden_layers
      _a           =    num_attention_heads
      _a           =    intermediate_size
      _a           =    hidden_act
      _a           =    hidden_dropout_prob
      _a           =    attention_probs_dropout_prob
      _a           =    max_position_embeddings
      _a           =    type_vocab_size
      _a           =    type_sequence_label_size
      _a           =    initializer_range
      _a           =    num_choices



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

      _a           =    None
      if self.use_attention_mask:
           _a           =    random_attention_mask([self.batch_size, self.seq_length]			)

      _a           =    None
      if self.use_token_type_ids:
           _a           =    ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size			)

      _a           =    AlbertConfig(
          vocab_size=self.vocab_size					,  hidden_size=self.hidden_size					,  num_hidden_layers=self.num_hidden_layers					,  num_attention_heads=self.num_attention_heads					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  max_position_embeddings=self.max_position_embeddings					,  type_vocab_size=self.type_vocab_size					,  is_decoder=A					,  initializer_range=self.initializer_range					,  )

      return config, input_ids, token_type_ids, attention_mask



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      _a    ,       _a    ,       _a    ,       _a           =    config_and_inputs
      _a           =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict






@require_flax
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[int]		=			(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    FlaxAlbertModelTester(self			)



 @slow
 def    a__    (self			)       ->	str:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a           =    model_class_name.from_pretrained('''albert-base-v2'''			)
           _a           =    model(np.ones((1, 1)			)			)
           self.assertIsNotNone(A			)






@require_flax
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    FlaxAlbertModel.from_pretrained('''albert-base-v2'''			)
      _a           =    np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]			)
      _a           =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
      _a           =    model(A					,  attention_mask=A			)[0]
      _a           =    (1, 11, 768)
      self.assertEqual(output.shape					,  A			)
      _a           =    np.array(
          [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]			)

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]					,  A					,  atol=1E-4			)			)



