



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      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

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



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=parent
   lowerCamelCase__:   Dict    						=batch_size
   lowerCamelCase__:   int    						=seq_length
   lowerCamelCase__:   Optional[int]    						=is_training
   lowerCamelCase__:   Union[str, Any]    						=use_attention_mask
   lowerCamelCase__:   List[str]    						=use_token_type_ids
   lowerCamelCase__:   Union[str, Any]    						=use_labels
   lowerCamelCase__:   Optional[int]    						=vocab_size
   lowerCamelCase__:   str    						=hidden_size
   lowerCamelCase__:   Optional[int]    						=num_hidden_layers
   lowerCamelCase__:   Tuple    						=num_attention_heads
   lowerCamelCase__:   Tuple    						=intermediate_size
   lowerCamelCase__:   Tuple    						=hidden_act
   lowerCamelCase__:   Dict    						=hidden_dropout_prob
   lowerCamelCase__:   List[str]    						=attention_probs_dropout_prob
   lowerCamelCase__:   str    						=max_position_embeddings
   lowerCamelCase__:   Any    						=type_vocab_size
   lowerCamelCase__:   Optional[Any]    						=type_sequence_label_size
   lowerCamelCase__:   List[str]    						=initializer_range
   lowerCamelCase__:   List[str]    						=num_choices

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->int:



   '''simple docstring'''



   lowerCamelCase__:   str    						=ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size)

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

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

   lowerCamelCase__:   List[str]    						=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=UpperCAmelCase_ ,				initializer_range=self.initializer_range ,				)

   return config, input_ids, token_type_ids, attention_mask







 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:   str    						=self.prepare_config_and_inputs()
   lowerCamelCase__:   List[Any]    						=config_and_inputs
   lowerCamelCase__:   int    						={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
   return config, inputs_dict



@require_flax
class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    True

 lowercase_	=    (
     (
         FlaxRoFormerModel,
         FlaxRoFormerForMaskedLM,
         FlaxRoFormerForSequenceClassification,
         FlaxRoFormerForTokenClassification,
         FlaxRoFormerForMultipleChoice,
         FlaxRoFormerForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						=FlaxRoFormerModelTester(self)







 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Union[str, Any]:



   '''simple docstring'''



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



@require_flax
class      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

 @slow
 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   str    						=FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
   lowerCamelCase__:   str    						=jnp.array([[0, 1, 2, 3, 4, 5]])
   lowerCamelCase__:   int    						=model(UpperCAmelCase_)[0]

   lowerCamelCase__:   Optional[int]    						=50_000

   lowerCamelCase__:   str    						=(1, 6, vocab_size)
   self.assertEqual(output.shape ,				UpperCAmelCase_)

   lowerCamelCase__:   Any    						=jnp.array(
       [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])

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








from __future__ import annotations





def   lowerCAmelCase_				( __a			,					__a			,					__a			,					)  ->	tuple:







  """simple docstring"""





  if (electron_conc, hole_conc, intrinsic_conc).count(0	) != 1:
    raise ValueError("You cannot supply more or less than 2 values"	)
  elif electron_conc < 0:
    raise ValueError("Electron concentration cannot be negative in a semiconductor"	)
  elif hole_conc < 0:
    raise ValueError("Hole concentration cannot be negative in a semiconductor"	)
  elif intrinsic_conc < 0:
    raise ValueError(
        "Intrinsic concentration cannot be negative in a semiconductor"	)
  elif electron_conc == 0:
    return (
        "electron_conc",
        intrinsic_conc**2 / hole_conc,
    )
  elif hole_conc == 0:
    return (
        "hole_conc",
        intrinsic_conc**2 / electron_conc,
    )
  elif intrinsic_conc == 0:
    return (
        "intrinsic_conc",
        (electron_conc * hole_conc) ** 0.5,
    )
  else:
    return (-1, -1)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

