


'''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 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=13			,     lowerCamelCase=7			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=99			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=512			,     lowerCamelCase=16			,     lowerCamelCase=2			,     lowerCamelCase=0.02			,     lowerCamelCase=4			,     ):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size		)

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

          __A	:       Union[str, Any]      =							None
          if self.use_token_type_ids:
               __A	:       Tuple      =							ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size		)

          __A	:       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=lowerCamelCase			,     initializer_range=self.initializer_range			,     )

          return config, input_ids, token_type_ids, attention_mask


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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







@require_flax
class 							__magic_name__	(       lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				True

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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       int      =							FlaxRoFormerModelTester(self		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





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







@require_flax
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
          __A	:       Union[str, Any]      =							jnp.array([[0, 1, 2, 3, 4, 5]]		)
          __A	:       Any      =							model(lowerCamelCase		)[0]

          __A	:       Optional[int]      =							5_0000

          __A	:       Optional[int]      =							(1, 6, vocab_size)
          self.assertEqual(output.shape			,     lowerCamelCase		)

          __A	:       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]			,     lowerCamelCase			,     atol=1E-4		)		)





'''simple docstring'''


# Function to print upper half of diamond (pyramid)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(0       ,					SCREAMING_SNAKE_CASE							):
          for _ in range(0       ,					n - i - 1							):  # printing spaces
               print(" "       ,					end=""							)
          for _ in range(0       ,					i + 1							):  # printing stars
               print("* "       ,					end=""							)
          print()


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):
          for _ in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):  # printing stars
               print("* "       ,					end=""							)
          print()
          for _ in range(n - i + 1       ,					0       ,					-1							):  # printing spaces
               print(" "       ,					end=""							)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if n <= 0:
          print("       ...       ....        nothing printing :("							)
          return
     floyd(SCREAMING_SNAKE_CASE							)  # upper half
     reverse_floyd(SCREAMING_SNAKE_CASE							)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      _UpperCamelCase 							=  1
      while K:
            _UpperCamelCase 							=  int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            _UpperCamelCase 							=  int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")

