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



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      parent
           lowercase							:     List[Any]       	=      13
           lowercase							:     Any       	=      7
           lowercase							:     Union[str, Any]       	=      True
           lowercase							:     Dict       	=      True
           lowercase							:     List[Any]       	=      True
           lowercase							:     List[str]       	=      True
           lowercase							:     Any       	=      99
           lowercase							:     Tuple       	=      32
           lowercase							:     Optional[Any]       	=      2
           lowercase							:     Tuple       	=      4
           lowercase							:     Tuple       	=      37
           lowercase							:     Optional[int]       	=      """gelu"""
           lowercase							:     List[str]       	=      0.1
           lowercase							:     Tuple       	=      0.1
           lowercase							:     Union[str, Any]       	=      512
           lowercase							:     List[str]       	=      16
           lowercase							:     Optional[int]       	=      2
           lowercase							:     str       	=      0.02
           lowercase							:     int       	=      3
           lowercase							:     Optional[Any]       	=      4
           lowercase							:     str       	=      None
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size      )

           lowercase							:     Tuple       	=      None
           if self.use_input_mask:
                  lowercase							:     Dict       	=      random_attention_mask([self.batch_size, self.seq_length]      )

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

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

           lowercase							:     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=snake_case		,)

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      TFRoFormerModel(config=snake_case      )
           lowercase							:     List[str]       	=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

           lowercase							:     Tuple       	=      [input_ids, input_mask]
           lowercase							:     Any       	=      model(snake_case      )

           lowercase							:     Any       	=      model(snake_case      )

           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, self.seq_length, self.hidden_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      True
           lowercase							:     Dict       	=      TFRoFormerForCausalLM(config=snake_case      )
           lowercase							:     Dict       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           lowercase							:     Optional[int]       	=      model(snake_case      )["""logits"""]
           self.parent.assertListEqual(
               list(prediction_scores.numpy().shape      )		,[self.batch_size, self.seq_length, self.vocab_size]      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      TFRoFormerForMaskedLM(config=snake_case      )
           lowercase							:     List[Any]       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           lowercase							:     Dict       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.num_labels
           lowercase							:     Any       	=      TFRoFormerForSequenceClassification(config=snake_case      )
           lowercase							:     int       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           lowercase							:     Tuple       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.num_choices
           lowercase							:     Tuple       	=      TFRoFormerForMultipleChoice(config=snake_case      )
           lowercase							:     Any       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Union[str, Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      tf.tile(tf.expand_dims(snake_case		,1      )		,(1, self.num_choices, 1)      )
           lowercase							:     Optional[Any]       	=      {
               """input_ids""": multiple_choice_inputs_ids,
               """attention_mask""": multiple_choice_input_mask,
               """token_type_ids""": multiple_choice_token_type_ids,
           }
           lowercase							:     Dict       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_choices)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      self.num_labels
           lowercase							:     List[str]       	=      TFRoFormerForTokenClassification(config=snake_case      )
           lowercase							:     int       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }
           lowercase							:     List[str]       	=      model(snake_case      )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFRoFormerForQuestionAnswering(config=snake_case      )
           lowercase							:     Optional[Any]       	=      {
               """input_ids""": input_ids,
               """attention_mask""": input_mask,
               """token_type_ids""": token_type_ids,
           }

           lowercase							:     Dict       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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






@require_tf
class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase					,	unittest.TestCase     ):
    _a		:						Tuple=				(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _a		:						Dict=				(
        {
            "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 {}
    )

    _a		:						List[Any]=				False
    _a		:						Optional[Any]=				False
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           if pipeline_test_casse_name == "TextGenerationPipelineTests":
                  return True

           return False
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      TFRoFormerModelTester(self      )
           lowercase							:     Optional[Any]       	=      ConfigTester(self		,config_class=snake_case		,hidden_size=37      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           self.config_tester.run_common_tests()
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*snake_case      )
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""      )
           self.assertIsNotNone(snake_case      )






@require_tf
class __snake_case		(      unittest.TestCase     ):
    @slow
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Any       	=      TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""      )
           lowercase							:     Optional[int]       	=      tf.constant([[0, 1, 2, 3, 4, 5]]      )
           lowercase							:     Tuple       	=      model(snake_case      )[0]

           # TODO Replace vocab size
           lowercase							:     int       	=      50000

           lowercase							:     Optional[Any]       	=      [1, 6, vocab_size]
           self.assertEqual(output.shape		,snake_case      )

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

           # TODO Replace values below with what was printed above.
           lowercase							:     Any       	=      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 __snake_case		(      unittest.TestCase     ):
    _a		:						Dict=				1E-4
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

           lowercase							:     Optional[Any]       	=      emba(input_ids.shape      )
           lowercase							:     Optional[int]       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      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],
               ]      )
           lowercase							:     str       	=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=512		,embedding_dim=512      )
           emba([2, 16, 512]      )
           lowercase							:     Union[str, Any]       	=      emba.weight[:3, :5]

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






@require_tf
class __snake_case		(      unittest.TestCase     ):
    _a		:						int=				1E-4
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

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

           lowercase							:     Tuple       	=      TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,embedding_dim=64      )
           lowercase							:     Union[str, Any]       	=      embed_positions([2, 16, 768]      )[None, None, :, :]

           lowercase	,			lowercase							:     Union[str, Any]       	=      TFRoFormerSelfAttention.apply_rotary_position_embeddings(
               snake_case		,snake_case		,snake_case      )

           lowercase							:     Optional[Any]       	=      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],
               ]      )
           lowercase							:     Optional[Any]       	=      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      )





def        a						(      _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	0
		__UpperCAmelCase    :     str													=	len(_UpperCAmelCase						)

		for i in range(n - 1						):
				for j in range(i + 1 ,   _UpperCAmelCase						):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions

def        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		if len(_UpperCAmelCase						) <= 1:
				return arr, 0
		__UpperCAmelCase    :     Dict													=	len(_UpperCAmelCase						) // 2
		__UpperCAmelCase    :     Union[str, Any]													=	arr[0:mid]
		__UpperCAmelCase    :     Optional[Any]													=	arr[mid:]

		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	count_inversions_recursive(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	_count_cross_inversions(_UpperCAmelCase ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     List[str]													=	inversion_p + inversions_q + cross_inversions
		return c, num_inversions

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	[]
		__UpperCAmelCase    :     Any													=	0
		while i < len(_UpperCAmelCase						) and j < len(_UpperCAmelCase						):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(_UpperCAmelCase						) - i
						r.append(q[j]						)
						j += 1
				else:
						r.append(p[i]						)
						i += 1

		if i < len(_UpperCAmelCase						):
				r.extend(p[i:]						)
		else:
				r.extend(q[j:]						)

		return r, num_inversion

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[10, 2, 1, 5, 5, 2, 11]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		__UpperCAmelCase    :     List[str]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		__UpperCAmelCase    :     Any													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)

		# an empty list should also have zero inversions
		__UpperCAmelCase    :     Tuple													=	[]
		__UpperCAmelCase    :     Union[str, Any]													=	count_inversions_bf(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	count_inversions_recursive(_UpperCAmelCase						)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = ''' ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





