'''simple docstring'''




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
  )




class     a__      (   unittest.TestCase ):






     def __init__( self		:		Union[str, Any]  ,				a		:		Optional[int]  ,				a		:		str=13  ,				a		:		str=7  ,				a		:		Optional[Any]=True  ,				a		:		List[Any]=True  ,				a		:		str=True  ,				a		:		str=True  ,				a		:		Dict=99  ,				a		:		Dict=32  ,				a		:		Union[str, Any]=5  ,				a		:		Tuple=4  ,				a		:		List[str]=37  ,				a		:		Optional[int]="gelu"  ,				a		:		Dict=0.1  ,				a		:		List[str]=0.1  ,				a		:		Any=5_12  ,				a		:		int=16  ,				a		:		List[Any]=2  ,				a		:		List[str]=0.02  ,				a		:		Any=4  ,				):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



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

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

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

         __lowerCamelCase		=  RobertaPreLayerNormConfig(
             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    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  self.prepare_config_and_inputs()
         __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  config_and_inputs

         __lowerCamelCase		=  True
         __lowerCamelCase		=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
         __lowerCamelCase		=  ids_tensor([self.batch_size, self.seq_length]  ,				vocab_size=2    )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     a__      (   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     Optional[Any]							=True

     lowerCamelCase  :     Dict							=(
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  FlaxRobertaPreLayerNormModelTester(self    )






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         for model_class_name in self.all_model_classes:
             __lowerCamelCase		=  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,				from_pt=a    )
             __lowerCamelCase		=  model(np.ones((1, 1)    )    )
             self.assertIsNotNone(a    )




@require_flax
class     a__      (   unittest.TestCase ):






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,				from_pt=a    )

         __lowerCamelCase		=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  ,				dtype=jnp.intaa    )
         __lowerCamelCase		=  model(a    )[0]
         __lowerCamelCase		=  [1, 11, 5_02_65]
         self.assertEqual(list(output.shape    )  ,				a    )
         # compare the actual values for a slice.
         __lowerCamelCase		=  np.array(
             [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]  ,				dtype=np.floataa    )
         self.assertTrue(np.allclose(output[:, :3, :3]  ,				a  ,				atol=1e-4    )    )






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,				from_pt=a    )

         __lowerCamelCase		=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  ,				dtype=jnp.intaa    )
         __lowerCamelCase		=  model(a    )[0]
         # compare the actual values for a slice.
         __lowerCamelCase		=  np.array(
             [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]  ,				dtype=np.floataa    )
         self.assertTrue(np.allclose(output[:, :3, :3]  ,				a  ,				atol=1e-4    )    )




"""simple docstring"""





import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__						:   Optional[Any]			 = '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
snake_case__						:   Dict			 = direct_transformers_import(PATH_TO_TRANSFORMERS)

snake_case__						:   Optional[int]			 = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
snake_case__						:   Optional[int]			 = re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


snake_case__						:   int			 = {
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}


def    _snake_case  (  _snake_case :	List[str]	):
      lowerCAmelCase    :		Dict				=      None

      # source code of `config_class`
      lowerCAmelCase    :		Union[str, Any]				=      inspect.getsource(_snake_case	)
      lowerCAmelCase    :		List[Any]				=      _re_checkpoint.findall(_snake_case	)

      # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
      # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
      for ckpt_name, ckpt_link in checkpoints:
            # allow the link to end with `/`
            if ckpt_link.endswith('''/'''	):
                  lowerCAmelCase    :		List[str]				=      ckpt_link[:-1]

            # verify the checkpoint name corresponds to the checkpoint link
            lowerCAmelCase    :		Optional[int]				=      f'''https://huggingface.co/{ckpt_name}'''
            if ckpt_link == ckpt_link_from_name:
                  lowerCAmelCase    :		List[str]				=      ckpt_name
                  break

      return checkpoint


def    _snake_case  (  ):
      lowerCAmelCase    :		List[Any]				=      []

      for config_class in list(CONFIG_MAPPING.values()	):
            # Skip deprecated models
            if "models.deprecated" in config_class.__module__:
                  continue
            lowerCAmelCase    :		int				=      get_checkpoint_from_config_class(_snake_case	)

            lowerCAmelCase    :		int				=      config_class.__name__
            if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
                  configs_without_checkpoint.append(_snake_case	)

      if len(_snake_case	) > 0:
            lowerCAmelCase    :		Dict				=      '''\n'''.join(sorted(_snake_case	)	)
            raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}'''	)


if __name__ == "__main__":
 check_config_docstrings_have_checkpoints()


