

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   UpperCamelCase			:

    def __init__(      self			:   Dict				,     snake_case__			:   Dict				,     snake_case__			:   Any=1_3				,     snake_case__			:   Dict=7				,     snake_case__			:   Dict=True				,     snake_case__			:   Tuple=True				,     snake_case__			:   int=True				,     snake_case__			:   Dict=True				,     snake_case__			:   Optional[int]=9_9				,     snake_case__			:   List[str]=3_2				,     snake_case__			:   Optional[Any]=2				,     snake_case__			:   Optional[Any]=4				,     snake_case__			:   Optional[Any]=3_7				,     snake_case__			:   List[Any]="gelu"				,     snake_case__			:   Optional[int]=0.1				,     snake_case__			:   str=0.1				,     snake_case__			:   List[Any]=5_1_2				,     snake_case__			:   List[str]=1_6				,     snake_case__			:   Optional[Any]=2				,     snake_case__			:   str=0.02				,     snake_case__			:   int=3				,     snake_case__			:   str=4				,     snake_case__			:   Union[str, Any]=None				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      parent
     SCREAMING_SNAKE_CASE		    =      1_3
     SCREAMING_SNAKE_CASE		    =      7
     SCREAMING_SNAKE_CASE		    =      True
     SCREAMING_SNAKE_CASE		    =      True
     SCREAMING_SNAKE_CASE		    =      True
     SCREAMING_SNAKE_CASE		    =      True
     SCREAMING_SNAKE_CASE		    =      9_9
     SCREAMING_SNAKE_CASE		    =      3_2
     SCREAMING_SNAKE_CASE		    =      2
     SCREAMING_SNAKE_CASE		    =      4
     SCREAMING_SNAKE_CASE		    =      3_7
     SCREAMING_SNAKE_CASE		    =      'gelu'
     SCREAMING_SNAKE_CASE		    =      0.1
     SCREAMING_SNAKE_CASE		    =      0.1
     SCREAMING_SNAKE_CASE		    =      5_1_2
     SCREAMING_SNAKE_CASE		    =      1_6
     SCREAMING_SNAKE_CASE		    =      2
     SCREAMING_SNAKE_CASE		    =      0.02
     SCREAMING_SNAKE_CASE		    =      3
     SCREAMING_SNAKE_CASE		    =      4
     SCREAMING_SNAKE_CASE		    =      None

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size     )

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

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

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

     SCREAMING_SNAKE_CASE		    =      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=snake_case__				,     )

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

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   str				,     snake_case__			:   Tuple				,     snake_case__			:   int				,     snake_case__			:   str				,     snake_case__			:   int				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFRoFormerModel(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

     SCREAMING_SNAKE_CASE		    =      [input_ids, input_mask]
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )

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

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   str				,     snake_case__			:   int				,     snake_case__			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   Tuple				,     snake_case__			:   Tuple				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      True
     SCREAMING_SNAKE_CASE		    =      TFRoFormerForCausalLM(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )['logits']
     self.parent.assertListEqual(
         list(prediction_scores.numpy().shape     )				,     [self.batch_size, self.seq_length, self.vocab_size]     )

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   Optional[int]				,     snake_case__			:   Any				,     snake_case__			:   Dict				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   Dict				,     snake_case__			:   Optional[Any]				,     snake_case__			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFRoFormerForMaskedLM(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)     )

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   int				,     snake_case__			:   List[str]				,     snake_case__			:   Dict				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   str				,     snake_case__			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.num_labels
     SCREAMING_SNAKE_CASE		    =      TFRoFormerForSequenceClassification(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels)     )

    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   Any				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.num_choices
     SCREAMING_SNAKE_CASE		    =      TFRoFormerForMultipleChoice(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      tf.tile(tf.expand_dims(snake_case__				,     1     )				,     (1, self.num_choices, 1)     )
     SCREAMING_SNAKE_CASE		    =      tf.tile(tf.expand_dims(snake_case__				,     1     )				,     (1, self.num_choices, 1)     )
     SCREAMING_SNAKE_CASE		    =      tf.tile(tf.expand_dims(snake_case__				,     1     )				,     (1, self.num_choices, 1)     )
     SCREAMING_SNAKE_CASE		    =      {
         'input_ids': multiple_choice_inputs_ids,
         'attention_mask': multiple_choice_input_mask,
         'token_type_ids': multiple_choice_token_type_ids,
     }
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_choices)     )

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   Dict				,     snake_case__			:   List[str]				,     snake_case__			:   Optional[int]				,     snake_case__			:   Tuple				,     snake_case__			:   Dict				,     snake_case__			:   int				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.num_labels
     SCREAMING_SNAKE_CASE		    =      TFRoFormerForTokenClassification(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)     )

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   Tuple				,     snake_case__			:   List[str]				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   int				,     snake_case__			:   int				,     snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFRoFormerForQuestionAnswering(config=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )
     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 UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






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




@require_tf
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __UpperCamelCase    =(
        {
            "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 {}
    )

    __UpperCamelCase    =False
    __UpperCamelCase    =False

    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   List[Any]				,     snake_case__			:   Union[str, Any]				,     snake_case__			:   List[Any]				,     snake_case__			:   List[Any]				,     snake_case__			:   Any     ):

     """simple docstring"""






     if pipeline_test_casse_name == "TextGenerationPipelineTests":
      return True

     return False

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFRoFormerModelTester(self     )
     SCREAMING_SNAKE_CASE		    =      ConfigTester(self				,     config_class=snake_case__				,     hidden_size=3_7     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__     )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head(*snake_case__     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case__     )

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__     )

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__     )




    @slow
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'     )
     self.assertIsNotNone(snake_case__     )




@require_tf
class   UpperCamelCase			(  unittest.TestCase ):

    @slow
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'     )
     SCREAMING_SNAKE_CASE		    =      tf.constant([[0, 1, 2, 3, 4, 5]]     )
     SCREAMING_SNAKE_CASE		    =      model(snake_case__     )[0]

     # TODO Replace vocab size
     SCREAMING_SNAKE_CASE		    =      5_0_0_0_0

     SCREAMING_SNAKE_CASE		    =      [1, 6, vocab_size]
     self.assertEqual(output.shape				,     snake_case__     )

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

     # TODO Replace values below with what was printed above.
     SCREAMING_SNAKE_CASE		    =      tf.constant(
         [
             [
                 [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                 [-1.5_133_783, 0.197_433, 0.15_190_607],
                 [-5.0_135_403, -3.900_256, -0.84_038_764],
             ]
         ]     )
     tf.debugging.assert_near(output[:, :3, :3]				,     snake_case__				,     atol=1E-4     )




@require_tf
class   UpperCamelCase			(  unittest.TestCase ):
    __UpperCamelCase    =1e-4

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      tf.constant([[4, 1_0]]     )
     SCREAMING_SNAKE_CASE		    =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				,     embedding_dim=6     )

     SCREAMING_SNAKE_CASE		    =      emba(input_ids.shape     )
     SCREAMING_SNAKE_CASE		    =      tf.constant(
         [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]     )

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




    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      tf.constant(
         [
             [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
             [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
             [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
         ]     )
     SCREAMING_SNAKE_CASE		    =      TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2				,     embedding_dim=5_1_2     )
     emba([2, 1_6, 5_1_2]     )
     SCREAMING_SNAKE_CASE		    =      emba.weight[:3, :5]

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




@require_tf
class   UpperCamelCase			(  unittest.TestCase ):
    __UpperCamelCase    =1e-4

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      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

     SCREAMING_SNAKE_CASE		    =      -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

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

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
         snake_case__				,     snake_case__				,     snake_case__     )

     SCREAMING_SNAKE_CASE		    =      tf.constant(
         [
             [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
             [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
             [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
             [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
             [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
             [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
         ]     )
     SCREAMING_SNAKE_CASE		    =      tf.constant(
         [
             [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
             [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
             [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
             [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
             [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
             [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
         ]     )

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




from collections.abc import Sequence


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Sequence[int] | None = None						)		->				int:
 '''simple docstring'''


 if nums is None or not nums:
  raise ValueError('Input sequence should not be empty'						)

 SCREAMING_SNAKE_CASE		    =      nums[0]
 for i in range(1		,				len(_UpperCamelCase						)						):
  SCREAMING_SNAKE_CASE		    =      nums[i]
  SCREAMING_SNAKE_CASE		    =      max(_UpperCamelCase		,				ans + num		,				_UpperCamelCase						)

 return ans


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # Try on a sample input from the user
      a_     :    str     		=   int(input("Enter number of elements : ").strip())
      a_     :    Any     		=   list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
      print(max_subsequence_sum(array))

