



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 ):





     def __init__(       self :							List[str]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							Optional[Any]=1_3     ,  UpperCamelCase :							int=7     ,  UpperCamelCase :							str=True     ,  UpperCamelCase :							Tuple=True     ,  UpperCamelCase :							int=True     ,  UpperCamelCase :							Tuple=True     ,  UpperCamelCase :							Union[str, Any]=9_9     ,  UpperCamelCase :							Union[str, Any]=3_2     ,  UpperCamelCase :							Dict=5     ,  UpperCamelCase :							List[Any]=4     ,  UpperCamelCase :							List[str]=3_7     ,  UpperCamelCase :							List[str]="gelu"     ,  UpperCamelCase :							Optional[int]=0.1     ,  UpperCamelCase :							Optional[int]=0.1     ,  UpperCamelCase :							Optional[int]=5_1_2     ,  UpperCamelCase :							Union[str, Any]=1_6     ,  UpperCamelCase :							Optional[Any]=2     ,  UpperCamelCase :							Optional[int]=0.0_2     ,  UpperCamelCase :							str=4     ,  )->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	parent
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Any       =	seq_length
      __SCREAMING_SNAKE_CASE       :    str       =	is_training
      __SCREAMING_SNAKE_CASE       :    List[str]       =	use_attention_mask
      __SCREAMING_SNAKE_CASE       :    Dict       =	use_token_type_ids
      __SCREAMING_SNAKE_CASE       :    int       =	use_labels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	vocab_size
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    str       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    Tuple       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    List[str]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Dict       =	max_position_embeddings
      __SCREAMING_SNAKE_CASE       :    int       =	type_vocab_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	type_sequence_label_size
      __SCREAMING_SNAKE_CASE       :    int       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_choices





     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size				)

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

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
      if self.use_token_type_ids:
       __SCREAMING_SNAKE_CASE       :    str       =	ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size				)

      __SCREAMING_SNAKE_CASE       :    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 	__snake_case    (       self :							Dict				)->Any:
      __SCREAMING_SNAKE_CASE       :    str       =	self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE       :    Dict       =	config_and_inputs
      __SCREAMING_SNAKE_CASE       :    str       =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
      return config, inputs_dict






@require_flax
class       _SCREAMING_SNAKE_CASE	(__UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			True

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





     def 	__snake_case    (       self :							List[str]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	FlaxRoFormerModelTester(self				)






     @slow
     def 	__snake_case    (       self :							Optional[Any]				)->int:
      for model_class_name in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	model_class_name.from_pretrained("junnyu/roformer_chinese_small"     ,  from_pt=UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    int       =	model(np.ones((1, 1)				)				)
       self.assertIsNotNone(UpperCamelCase				)






@require_flax
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     @slow
     def 	__snake_case    (       self :							int				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	jnp.array([[0, 1, 2, 3, 4, 5]]				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	model(UpperCamelCase				)[0]

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	5_0_0_0_0

      __SCREAMING_SNAKE_CASE       :    List[str]       =	(1, 6, vocab_size)
      self.assertEqual(output.shape     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	jnp.array(
          [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]				)

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











from collections.abc import Generator
from math import sin






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 if len(__lowerCamelCase     ) != 32:
  raise ValueError("Input must be of length 32"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian += string_aa[8 * i : 8 * i + 8]
 return little_endian






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )

 __SCREAMING_SNAKE_CASE       :    Any       =	format(__lowerCamelCase		,	"08x"     )[-8:]
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"     )
 return little_endian_hex






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	b""
 for char in message:
  bit_string += format(__lowerCamelCase		,	"08b"     ).encode("utf-8"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	format(len(__lowerCamelCase     )		,	"064b"     ).encode("utf-8"     )

 # Pad bit_string to a multiple of 512 chars
 bit_string += b"1"
 while len(__lowerCamelCase     ) % 512 != 448:
  bit_string += b"0"
 bit_string += to_little_endian(start_len[32:]     ) + to_little_endian(start_len[:32]     )

 return bit_string






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 if len(__lowerCamelCase     ) % 512 != 0:
  raise ValueError("Input must have length that's a multiple of 512"     )

 for pos in range(0		,	len(__lowerCamelCase     )		,	512     ):
  __SCREAMING_SNAKE_CASE       :    int       =	bit_string[pos : pos + 512]
  __SCREAMING_SNAKE_CASE       :    Tuple       =	[]
  for i in range(0		,	512		,	32     ):
   block_words.append(int(to_little_endian(block[i : i + 32]     )		,	2     )     )
  yield block_words






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	format(__lowerCamelCase		,	"032b"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	""
 for c in i_str:
  new_str += "1" if c == "0" else "0"
 return int(__lowerCamelCase		,	2     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 return (a + b) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if i < 0:
  raise ValueError("Input must be non-negative"     )
 if shift < 0:
  raise ValueError("Shift must be non-negative"     )
 return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	preprocess(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[int(2**32 * abs(sin(i + 1     )     )     ) for i in range(64     )]

 # Starting states
 __SCREAMING_SNAKE_CASE       :    Tuple       =	0X67452301
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0Xefcdab89
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0X98badcfe
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0X10325476

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     7,
     12,
     17,
     22,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     5,
     9,
     14,
     20,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     4,
     11,
     16,
     23,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
     6,
     10,
     15,
     21,
 ]

 # Process bit string in chunks, each with 16 32-char words
 for block_words in get_block_words(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    Any       =	aa
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ba
  __SCREAMING_SNAKE_CASE       :    str       =	ca
  __SCREAMING_SNAKE_CASE       :    Dict       =	da

  # Hash current chunk
  for i in range(64     ):
   if i <= 15:
    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	d ^ (b & (c ^ d))
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	i
   elif i <= 31:
    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
    __SCREAMING_SNAKE_CASE       :    int       =	c ^ (d & (b ^ c))
    __SCREAMING_SNAKE_CASE       :    int       =	(5 * i + 1) % 16
   elif i <= 47:
    __SCREAMING_SNAKE_CASE       :    List[str]       =	b ^ c ^ d
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(3 * i + 5) % 16
   else:
    __SCREAMING_SNAKE_CASE       :    Any       =	c ^ (b | not_aa(__lowerCamelCase     ))
    __SCREAMING_SNAKE_CASE       :    str       =	(7 * i) % 16
   __SCREAMING_SNAKE_CASE       :    List[str]       =	(f + a + added_consts[i] + block_words[g]) % 2**32
   __SCREAMING_SNAKE_CASE       :    Dict       =	d
   __SCREAMING_SNAKE_CASE       :    str       =	c
   __SCREAMING_SNAKE_CASE       :    Tuple       =	b
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum_aa(__lowerCamelCase		,	left_rotate_aa(__lowerCamelCase		,	shift_amounts[i]     )     )

  # Add hashed chunk to running total
  __SCREAMING_SNAKE_CASE       :    Dict       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    str       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     )
 return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






