



"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
     FlaxRoFormerForMaskedLM,
     FlaxRoFormerForMultipleChoice,
     FlaxRoFormerForQuestionAnswering,
     FlaxRoFormerForSequenceClassification,
     FlaxRoFormerForTokenClassification,
     FlaxRoFormerModel,
 )






class      UpperCAmelCase_  (    unittest.TestCase						):



      def __init__(					self					:     str					,     A					:     Optional[Any]					,     A					:     Optional[Any]=1_3					,     A					:     List[Any]=7					,     A					:     Dict=True					,     A					:     Tuple=True					,     A					:     Union[str, Any]=True					,     A					:     str=True					,     A					:     Dict=9_9					,     A					:     List[str]=3_2					,     A					:     Optional[int]=5					,     A					:     Tuple=4					,     A					:     Dict=3_7					,     A					:     Union[str, Any]="gelu"					,     A					:     Optional[Any]=0.1					,     A					:     List[str]=0.1					,     A					:     Union[str, Any]=5_1_2					,     A					:     List[str]=1_6					,     A					:     Tuple=2					,     A					:     List[str]=0.02					,     A					:     List[Any]=4					,     ):
           _UpperCAmelCase      :     Dict        =   parent
           _UpperCAmelCase      :     Union[str, Any]        =   batch_size
           _UpperCAmelCase      :     Union[str, Any]        =   seq_length
           _UpperCAmelCase      :     int        =   is_training
           _UpperCAmelCase      :     List[str]        =   use_attention_mask
           _UpperCAmelCase      :     int        =   use_token_type_ids
           _UpperCAmelCase      :     str        =   use_labels
           _UpperCAmelCase      :     int        =   vocab_size
           _UpperCAmelCase      :     Optional[Any]        =   hidden_size
           _UpperCAmelCase      :     Optional[int]        =   num_hidden_layers
           _UpperCAmelCase      :     List[str]        =   num_attention_heads
           _UpperCAmelCase      :     Optional[int]        =   intermediate_size
           _UpperCAmelCase      :     Any        =   hidden_act
           _UpperCAmelCase      :     List[str]        =   hidden_dropout_prob
           _UpperCAmelCase      :     Tuple        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     List[Any]        =   max_position_embeddings
           _UpperCAmelCase      :     Optional[int]        =   type_vocab_size
           _UpperCAmelCase      :     Dict        =   type_sequence_label_size
           _UpperCAmelCase      :     Optional[Any]        =   initializer_range
           _UpperCAmelCase      :     Optional[int]        =   num_choices



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   ids_tensor([self.batch_size, self.seq_length]					,     self.vocab_size						)

           _UpperCAmelCase      :     Optional[Any]        =   None
           if self.use_attention_mask:
                _UpperCAmelCase      :     Optional[Any]        =   random_attention_mask([self.batch_size, self.seq_length]						)

           _UpperCAmelCase      :     int        =   None
           if self.use_token_type_ids:
                _UpperCAmelCase      :     List[str]        =   ids_tensor([self.batch_size, self.seq_length]					,     self.type_vocab_size						)

           _UpperCAmelCase      :     Optional[int]        =   RoFormerConfig(
               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 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[Any]        =   self.prepare_config_and_inputs()
           _UpperCAmelCase      :     Optional[Any]        =   config_and_inputs
           _UpperCAmelCase      :     Optional[int]        =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict







@require_flax
class      UpperCAmelCase_  (    __lowercase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			True

      __SCREAMING_SNAKE_CASE						:       int								=			(
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     int        =   FlaxRoFormerModelTester(self						)



      @slow
      def 						snake_case_ (					self					:     Union[str, Any]						):

           for model_class_name in self.all_model_classes:
                _UpperCAmelCase      :     List[str]        =   model_class_name.from_pretrained("junnyu/roformer_chinese_small"					,     from_pt=A						)
                _UpperCAmelCase      :     List[Any]        =   model(np.ones((1, 1)						)						)
                self.assertIsNotNone(A						)







@require_flax
class      UpperCAmelCase_  (    unittest.TestCase						):



      @slow
      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     List[str]        =   FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"						)
           _UpperCAmelCase      :     List[Any]        =   jnp.array([[0, 1, 2, 3, 4, 5]]						)
           _UpperCAmelCase      :     Tuple        =   model(A						)[0]

           _UpperCAmelCase      :     Any        =   5_0_0_0_0

           _UpperCAmelCase      :     Tuple        =   (1, 6, vocab_size)
           self.assertEqual(output.shape					,     A						)

           _UpperCAmelCase      :     Tuple        =   jnp.array(
               [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]						)

           self.assertTrue(jnp.allclose(output[:, :3, :3]					,     A					,     atol=1e-4						)						)










"""simple docstring"""




import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def 						__snake_case						(				*SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Union[Dict, Any]] = None						,  SCREAMING_SNAKE_CASE__     :						int=True						,  SCREAMING_SNAKE_CASE__     :						Tuple=2    )  ->       Optional[Any]:





     '''simple docstring'''

     from .. import __version__

     _UpperCAmelCase      :     Tuple        =   take_from
     _UpperCAmelCase      :     Optional[int]        =   ()
     if not isinstance(args[0]						,  SCREAMING_SNAKE_CASE__    ):
          _UpperCAmelCase      :     int        =   (args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(SCREAMING_SNAKE_CASE__    ).base_version    ) >= version.parse(SCREAMING_SNAKE_CASE__    ):
               raise ValueError(
                   f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                   f' version {__version__} is >= {version_name}'    )

          _UpperCAmelCase      :     Any        =   None
          if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE__    ),)
               _UpperCAmelCase      :     Tuple        =   f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
          elif hasattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
               values += (getattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ),)
               _UpperCAmelCase      :     str        =   f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
          elif deprecated_kwargs is None:
               _UpperCAmelCase      :     Tuple        =   f'`{attribute}` is deprecated and will be removed in version {version_name}.'

          if warning is not None:
               _UpperCAmelCase      :     Optional[int]        =   warning + " " if standard_warn else ""
               warnings.warn(warning + message						,  SCREAMING_SNAKE_CASE__						,  stacklevel=SCREAMING_SNAKE_CASE__    )

     if isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ) and len(SCREAMING_SNAKE_CASE__    ) > 0:
          _UpperCAmelCase      :     List[Any]        =   inspect.getouterframes(inspect.currentframe()    )[1]
          _UpperCAmelCase      :     Optional[int]        =   call_frame.filename
          _UpperCAmelCase      :     Dict        =   call_frame.lineno
          _UpperCAmelCase      :     List[Any]        =   call_frame.function
          _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   next(iter(deprecated_kwargs.items()    )    )
          raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'    )

     if len(SCREAMING_SNAKE_CASE__    ) == 0:
          return
     elif len(SCREAMING_SNAKE_CASE__    ) == 1:
          return values[0]
     return values
