






import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )






class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




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




       """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		  =      DistilBertConfig(
           vocab_size=self.vocab_size ,					dim=self.hidden_size ,					n_layers=self.num_hidden_layers ,					n_heads=self.num_attention_heads ,					hidden_dim=self.intermediate_size ,					hidden_act=self.hidden_act ,					dropout=self.hidden_dropout_prob ,					attention_dropout=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					tie_weights_=__UpperCAmelCase ,					)

       return config, input_ids, attention_mask







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




       """simple docstring"""


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






@require_flax
class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  (
          (
              FlaxDistilBertModel,
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Tuple:




       """simple docstring"""


       a		  =      FlaxDistilBertModelTester(self  )







      @slow
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       for model_class_name in self.all_model_classes:
        a		  =      model_class_name.from_pretrained('''distilbert-base-uncased'''  )
        a		  =      model(np.ones((1, 1)  )  )
        self.assertIsNotNone(__UpperCAmelCase  )






@require_flax
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      @slow
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       a		  =      FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''  )
       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(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase  )[0]
       a		  =      (1, 11, 768)
       self.assertEqual(output.shape ,					__UpperCAmelCase  )
       a		  =      np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]  )

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










def 						_a    (      a :int )							->			list:

 # bit count represents no. of bits in the gray code
 if bit_count < 0:
  raise ValueError('''The given input must be positive''' )

 # get the generated string sequence
 a		  =      gray_code_sequence_string(a )
 #
 # convert them to integers
 for i in range(len(a ) ):
  a		  =      int(sequence[i]	,							2 )

 return sequence
def 						_a    (      a :int )							->			list:

 # The approach is a recursive one
 # Base case achieved when either n = 0 or n=1
 if bit_count == 0:
  return ["0"]

 if bit_count == 1:
  return ["0", "1"]

 a		  =      1 << bit_count  # defines the length of the sequence
 # 1<< n is equivalent to 2^n

 # recursive answer will generate answer for n-1 bits
 a		  =      gray_code_sequence_string(bit_count - 1 )

 a		  =      []

 # append 0 to first half of the smaller sequence generated
 for i in range(seq_len // 2 ):
  a		  =      '''0''' + smaller_sequence[i]
  sequence.append(a )

 # append 1 to second half ... start from the end of the list
 for i in reversed(range(seq_len // 2 ) ):
  a		  =      '''1''' + smaller_sequence[i]
  sequence.append(a )

 return sequence


if __name__ == "__main__":
     import doctest

     doctest.testmod()


