





'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerModel,
  )
  from transformers.models.roformer.modeling_tf_roformer import (
      TFRoFormerSelfAttention,
      TFRoFormerSinusoidalPositionalEmbedding,
  )


class  SCREAMING_SNAKE_CASE    :


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase=13						,	_UpperCAmelCase=7						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=99						,	_UpperCAmelCase=32						,	_UpperCAmelCase=2						,	_UpperCAmelCase=4						,	_UpperCAmelCase=37						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=16						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=3						,	_UpperCAmelCase=4						,	_UpperCAmelCase=None						,	):

          '''simple docstring'''






          __A						: List[str]      =       parent
          __A						: int      =       13
          __A						: Optional[int]      =       7
          __A						: int      =       True
          __A						: List[str]      =       True
          __A						: str      =       True
          __A						: int      =       True
          __A						: int      =       99
          __A						: str      =       32
          __A						: Tuple      =       2
          __A						: int      =       4
          __A						: str      =       37
          __A						: List[str]      =       """gelu"""
          __A						: Tuple      =       0.1
          __A						: Tuple      =       0.1
          __A						: Optional[Any]      =       512
          __A						: Union[str, Any]      =       16
          __A						: Optional[Any]      =       2
          __A						: Union[str, Any]      =       0.02
          __A						: Union[str, Any]      =       3
          __A						: Tuple      =       4
          __A						: List[Any]      =       None


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: List[Any]      =       None
          if self.use_input_mask:
               __A						: int      =       random_attention_mask([self.batch_size, self.seq_length])

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

          __A						: Dict      =       None
          __A						: List[Any]      =       None
          __A						: Dict      =       None
          if self.use_labels:
               __A						: Optional[Any]      =       ids_tensor([self.batch_size]						,	self.type_sequence_label_size)
               __A						: Optional[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels)
               __A						: int      =       ids_tensor([self.batch_size]						,	self.num_choices)

          __A						: Optional[Any]      =       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						,	initializer_range=self.initializer_range						,	return_dict=_UpperCAmelCase						,	)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       TFRoFormerModel(config=_UpperCAmelCase)
          __A						: int      =       {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

          __A						: int      =       [input_ids, input_mask]
          __A						: Tuple      =       model(_UpperCAmelCase)

          __A						: Union[str, Any]      =       model(_UpperCAmelCase)

          self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Optional[Any]      =       True
          __A						: Any      =       TFRoFormerForCausalLM(config=_UpperCAmelCase)
          __A						: Union[str, Any]      =       {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          __A						: Dict      =       model(_UpperCAmelCase)["""logits"""]
          self.parent.assertListEqual(
              list(prediction_scores.numpy().shape)						,	[self.batch_size, self.seq_length, self.vocab_size])


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Union[str, Any]      =       TFRoFormerForMaskedLM(config=_UpperCAmelCase)
          __A						: Optional[int]      =       {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          __A						: Optional[int]      =       model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[str]      =       self.num_labels
          __A						: Optional[int]      =       TFRoFormerForSequenceClassification(config=_UpperCAmelCase)
          __A						: Optional[int]      =       {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }

          __A						: Tuple      =       model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[Any]      =       self.num_choices
          __A						: List[Any]      =       TFRoFormerForMultipleChoice(config=_UpperCAmelCase)
          __A						: Optional[int]      =       tf.tile(tf.expand_dims(_UpperCAmelCase						,	1)						,	(1, self.num_choices, 1))
          __A						: Any      =       tf.tile(tf.expand_dims(_UpperCAmelCase						,	1)						,	(1, self.num_choices, 1))
          __A						: int      =       tf.tile(tf.expand_dims(_UpperCAmelCase						,	1)						,	(1, self.num_choices, 1))
          __A						: List[str]      =       {
              """input_ids""": multiple_choice_inputs_ids,
              """attention_mask""": multiple_choice_input_mask,
              """token_type_ids""": multiple_choice_token_type_ids,
          }
          __A						: List[str]      =       model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Any      =       self.num_labels
          __A						: Tuple      =       TFRoFormerForTokenClassification(config=_UpperCAmelCase)
          __A						: Tuple      =       {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }
          __A						: Union[str, Any]      =       model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       TFRoFormerForQuestionAnswering(config=_UpperCAmelCase)
          __A						: Dict      =       {
              """input_ids""": input_ids,
              """attention_mask""": input_mask,
              """token_type_ids""": token_type_ids,
          }

          __A						: Optional[int]      =       model(_UpperCAmelCase)
          self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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




@require_tf
class  SCREAMING_SNAKE_CASE    (UpperCAmelCase__					,		UpperCAmelCase__					,		unittest.TestCase							):
     lowerCAmelCase					=       (
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowerCAmelCase					=       (
         {
             "feature-extraction": TFRoFormerModel,
             "fill-mask": TFRoFormerForMaskedLM,
             "question-answering": TFRoFormerForQuestionAnswering,
             "text-classification": TFRoFormerForSequenceClassification,
             "text-generation": TFRoFormerForCausalLM,
             "token-classification": TFRoFormerForTokenClassification,
             "zero-shot": TFRoFormerForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )

     lowerCAmelCase					=       False
     lowerCAmelCase					=       False


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          if pipeline_test_casse_name == "TextGenerationPipelineTests":
               return True

          return False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       TFRoFormerModelTester(self)
          __A						: Any      =       ConfigTester(self						,	config_class=_UpperCAmelCase						,	hidden_size=37)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base')
          self.assertIsNotNone(_UpperCAmelCase)




@require_tf
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
          __A						: Union[str, Any]      =       tf.constant([[0, 1, 2, 3, 4, 5]])
          __A						: Optional[Any]      =       model(_UpperCAmelCase)[0]

          # TODO Replace vocab size
          __A						: Tuple      =       5_0000

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

          print(output[:, :3, :3])

          # TODO Replace values below with what was printed above.
          __A						: Dict      =       tf.constant(
              [
                  [
                      [-0.12053341, -1.0264901, 0.29221946],
                      [-1.5133783, 0.197433, 0.15190607],
                      [-5.0135403, -3.900256, -0.84038764],
                  ]
              ])
          tf.debugging.assert_near(output[:, :3, :3]						,	_UpperCAmelCase						,	atol=1e-4)




@require_tf
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       1E-4


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       tf.constant([[4, 10]])
          __A						: int      =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,	embedding_dim=6)

          __A						: Optional[Any]      =       emba(input_ids.shape)
          __A						: Union[str, Any]      =       tf.constant(
              [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]])

          tf.debugging.assert_near(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=self.tolerance)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       tf.constant(
              [
                  [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                  [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                  [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
              ])
          __A						: Tuple      =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,	embedding_dim=512)
          emba([2, 16, 512])
          __A						: Union[str, Any]      =       emba.weight[:3, :5]

          tf.debugging.assert_near(_UpperCAmelCase						,	_UpperCAmelCase						,	atol=self.tolerance)




@require_tf
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       1E-4


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       tf.reshape(tf.range(2 * 12 * 16 * 64						,	dtype=tf.floataa)						,	shape=(2, 12, 16, 64)) / 100

          __A						: int      =       -tf.reshape(tf.range(2 * 12 * 16 * 64						,	dtype=tf.floataa)						,	shape=(2, 12, 16, 64)) / 100

          __A						: List[str]      =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=32						,	embedding_dim=64)
          __A						: List[Any]      =       embed_positions([2, 16, 768])[None, None, :, :]

          __A						: Dict      =       TFRoFormerSelfAttention.apply_rotary_position_embeddings(
              _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase)

          __A						: Any      =       tf.constant(
              [
                  [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                  [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                  [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                  [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                  [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                  [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
              ])
          __A						: Dict      =       tf.constant(
              [
                  [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                  [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                  [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                  [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                  [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                  [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
              ])

          tf.debugging.assert_near(query_layer[0, 0, :6, :8]						,	_UpperCAmelCase						,	atol=self.tolerance)
          tf.debugging.assert_near(key_layer[0, 0, :6, :8]						,	_UpperCAmelCase						,	atol=self.tolerance)



'''simple docstring'''







from __future__ import annotations

from typing import TypedDict







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str
      __UpperCamelCase       :  int


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			list[str]:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )

       return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase  )  )]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			BWTTransformDict:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )
       if not s:
              raise ValueError("""The parameter s must not be empty."""  )

       _a    : List[Any]     					=all_rotations(_UpperCAmelCase  )
       rotations.sort()  # sort the list of rotations in alphabetically order
       # make a string composed of the last char of each rotation
       _a    : BWTTransformDict     					={
           "bwt_string": "".join([word[-1] for word in rotations]  ),
           "idx_original_string": rotations.index(_UpperCAmelCase  ),
       }
       return response


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	int  )  ->			str:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter bwt_string type must be str."""  )
       if not bwt_string:
              raise ValueError("""The parameter bwt_string must not be empty."""  )
       try:
              _a    : List[str]     					=int(_UpperCAmelCase  )
       except ValueError:
              raise TypeError(
                  """The parameter idx_original_string type must be int or passive"""
                  """ of cast to int."""  )
       if idx_original_string < 0:
              raise ValueError("""The parameter idx_original_string must not be lower than 0."""  )
       if idx_original_string >= len(_UpperCAmelCase  ):
              raise ValueError(
                  """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""  )

       _a    : Optional[int]     					=[""""""] * len(_UpperCAmelCase  )
       for _ in range(len(_UpperCAmelCase  )  ):
              for i in range(len(_UpperCAmelCase  )  ):
                     _a    : int     					=bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   A__:    Any					    =       '''Provide a string that I will generate its BWT transform: '''
   A__:    Union[str, Any]					    =       input(entry_msg).strip()
   A__:    Optional[int]					    =       bwt_transform(s)
   print(
       F"Burrows Wheeler transform for string '{s}' results "
       F"in '{result['bwt_string']}'"
   )
   A__:    Union[str, Any]					    =       reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
   print(
       F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
       F"we get original string '{original_string}'"
   )


