


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       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=2		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=None		,     )     ->   str:
      _lowercase							      =parent
      _lowercase							      =1_3
      _lowercase							      =7
      _lowercase							      =True
      _lowercase							      =True
      _lowercase							      =True
      _lowercase							      =True
      _lowercase							      =9_9
      _lowercase							      =3_2
      _lowercase							      =2
      _lowercase							      =4
      _lowercase							      =3_7
      _lowercase							      ='''gelu'''
      _lowercase							      =0.1
      _lowercase							      =0.1
      _lowercase							      =5_1_2
      _lowercase							      =1_6
      _lowercase							      =2
      _lowercase							      =0.02
      _lowercase							      =3
      _lowercase							      =4
      _lowercase							      =None





     def      __A					(self       )     ->   Any:
      _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size       )

      _lowercase							      =None
      if self.use_input_mask:
       _lowercase							      =random_attention_mask([self.batch_size, self.seq_length]       )

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

      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None
      if self.use_labels:
       _lowercase							      =ids_tensor([self.batch_size]		,     self.type_sequence_label_size       )
       _lowercase							      =ids_tensor([self.batch_size, self.seq_length]		,     self.num_labels       )
       _lowercase							      =ids_tensor([self.batch_size]		,     self.num_choices       )

      _lowercase							      =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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   str:
      _lowercase							      =TFRoFormerModel(config=UpperCAmelCase       )
      _lowercase							      ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

      _lowercase							      =[input_ids, input_mask]
      _lowercase							      =model(UpperCAmelCase       )

      _lowercase							      =model(UpperCAmelCase       )

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Dict:
      _lowercase							      =True
      _lowercase							      =TFRoFormerForCausalLM(config=UpperCAmelCase       )
      _lowercase							      ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      _lowercase							      =model(UpperCAmelCase       )['''logits''']
      self.parent.assertListEqual(
          list(prediction_scores.numpy().shape       )		,     [self.batch_size, self.seq_length, self.vocab_size]       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =TFRoFormerForMaskedLM(config=UpperCAmelCase       )
      _lowercase							      ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      _lowercase							      =self.num_labels
      _lowercase							      =TFRoFormerForSequenceClassification(config=UpperCAmelCase       )
      _lowercase							      ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }

      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =self.num_choices
      _lowercase							      =TFRoFormerForMultipleChoice(config=UpperCAmelCase       )
      _lowercase							      =tf.tile(tf.expand_dims(UpperCAmelCase		,     1       )		,     (1, self.num_choices, 1)       )
      _lowercase							      =tf.tile(tf.expand_dims(UpperCAmelCase		,     1       )		,     (1, self.num_choices, 1)       )
      _lowercase							      =tf.tile(tf.expand_dims(UpperCAmelCase		,     1       )		,     (1, self.num_choices, 1)       )
      _lowercase							      ={
          '''input_ids''': multiple_choice_inputs_ids,
          '''attention_mask''': multiple_choice_input_mask,
          '''token_type_ids''': multiple_choice_token_type_ids,
      }
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   int:
      _lowercase							      =self.num_labels
      _lowercase							      =TFRoFormerForTokenClassification(config=UpperCAmelCase       )
      _lowercase							      ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }
      _lowercase							      =model(UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =TFRoFormerForQuestionAnswering(config=UpperCAmelCase       )
      _lowercase							      ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''token_type_ids''': token_type_ids,
      }

      _lowercase							      =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      __A					(self       )     ->   int:
      _lowercase							      =self.prepare_config_and_inputs()
      (
          (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    (
          _lowercase
      )      ,    
      )							      =config_and_inputs
      _lowercase							      ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_tf
class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(
         (
             TFRoFormerModel,
             TFRoFormerForCausalLM,
             TFRoFormerForMaskedLM,
             TFRoFormerForQuestionAnswering,
             TFRoFormerForSequenceClassification,
             TFRoFormerForTokenClassification,
             TFRoFormerForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''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 {}
     )

     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Union[str, Any]:
      if pipeline_test_casse_name == "TextGenerationPipelineTests":
       return True

      return False





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =TFRoFormerModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   str:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase       )





     def      __A					(self       )     ->   Any:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase       )





     def      __A					(self       )     ->   Any:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''       )
      self.assertIsNotNone(UpperCAmelCase       )







@require_tf
class       lowerCamelCase__   (       unittest.TestCase):





     @slow
     def      __A					(self       )     ->   Dict:
      _lowercase							      =TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''       )
      _lowercase							      =tf.constant([[0, 1, 2, 3, 4, 5]]       )
      _lowercase							      =model(UpperCAmelCase       )[0]

      # TODO Replace vocab size
      _lowercase							      =5_0_0_0_0

      _lowercase							      =[1, 6, vocab_size]
      self.assertEqual(output.shape		,     UpperCAmelCase       )

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

      # TODO Replace values below with what was printed above.
      _lowercase							      =tf.constant(
          [
              [
                  [-0.1205_3341, -1.026_4901, 0.2922_1946],
                  [-1.513_3783, 0.19_7433, 0.1519_0607],
                  [-5.013_5403, -3.90_0256, -0.8403_8764],
              ]
          ]       )
      tf.debugging.assert_near(output[:, :3, :3]		,     UpperCAmelCase		,     atol=1e-4       )







@require_tf
class       lowerCamelCase__   (       unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			1E-4





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =tf.constant([[4, 1_0]]       )
      _lowercase							      =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,     embedding_dim=6       )

      _lowercase							      =emba(input_ids.shape       )
      _lowercase							      =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      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =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],
          ]       )
      _lowercase							      =TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2		,     embedding_dim=5_1_2       )
      emba([2, 1_6, 5_1_2]       )
      _lowercase							      =emba.weight[:3, :5]

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







@require_tf
class       lowerCamelCase__   (       unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			1E-4





     def      __A					(self       )     ->   List[Any]:
      # 2,12,16,64
      _lowercase							      =tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4		,     dtype=tf.floataa       )		,     shape=(2, 1_2, 1_6, 6_4)       ) / 1_0_0

      _lowercase							      =-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4		,     dtype=tf.floataa       )		,     shape=(2, 1_2, 1_6, 6_4)       ) / 1_0_0

      _lowercase							      =TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2		,     embedding_dim=6_4       )
      _lowercase							      =embed_positions([2, 1_6, 7_6_8]       )[None, None, :, :]

      _lowercase      ,    _lowercase							      =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )

      _lowercase							      =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],
          ]       )
      _lowercase							      =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       )





def 				UpperCAmelCase_					(							__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      =0

 # if input_string is "aba" than new_input_string become "a|b|a"
 _lowercase							      =''''''
 _lowercase							      =''''''

 # append each character + "|" in new_string for range(0, length-1)
 for i in input_string[: len(__snake_case						) - 1]:
  new_input_string += i + "|"
 # append last character
 new_input_string += input_string[-1]

 # we will store the starting and ending of previous furthest ending palindromic
 # substring
 _lowercase      ,    _lowercase							      =0, 0

 # length[i] shows the length of palindromic substring with center i
 _lowercase							      =[1 for i in range(len(__snake_case						)						)]

 # for each character in new_string find corresponding palindromic string
 _lowercase							      =0
 for j in range(len(__snake_case						)						):
  _lowercase							      =1 if j > r else min(length[l + r - j] // 2     ,						r - j + 1						)
  while (
      j - k >= 0
      and j + k < len(__snake_case						)
      and new_input_string[k + j] == new_input_string[j - k]
  ):
   k += 1

  _lowercase							      =2 * k - 1

  # does this string is ending after the previously explored end (that is r) ?
  # if yes the update the new r to the last index of this
  if j + k - 1 > r:
   _lowercase							      =j - k + 1  # noqa: E741
   _lowercase							      =j + k - 1

  # update max_length and start position
  if max_length < length[j]:
   _lowercase							      =length[j]
   _lowercase							      =j

    # create that string
 _lowercase							      =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
 for i in s:
  if i != "|":
   output_string += i

 return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()

