





"""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       a							:



 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=5_12				,       _snake_case=16				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=3				,       _snake_case=4				,       _snake_case=None				,       ):







  """simple docstring"""






  lowerCAmelCase    				= parent
  lowerCAmelCase    				= 13
  lowerCAmelCase    				= 7
  lowerCAmelCase    				= True
  lowerCAmelCase    				= True
  lowerCAmelCase    				= True
  lowerCAmelCase    				= True
  lowerCAmelCase    				= 99
  lowerCAmelCase    				= 32
  lowerCAmelCase    				= 2
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 37
  lowerCAmelCase    				= 'gelu'
  lowerCAmelCase    				= 0.1
  lowerCAmelCase    				= 0.1
  lowerCAmelCase    				= 5_12
  lowerCAmelCase    				= 16
  lowerCAmelCase    				= 2
  lowerCAmelCase    				= 0.02
  lowerCAmelCase    				= 3
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= None



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

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

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

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

  lowerCAmelCase    				= 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				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFRoFormerModel(config=_snake_case     )
  lowerCAmelCase    				= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

  lowerCAmelCase    				= [input_ids, input_mask]
  lowerCAmelCase    				= model(_snake_case     )

  lowerCAmelCase    				= model(_snake_case     )

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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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

  lowerCAmelCase    				= model(_snake_case     )
  self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= TFRoFormerForQuestionAnswering(config=_snake_case     )
  lowerCAmelCase    				= {
      'input_ids': input_ids,
      'attention_mask': input_mask,
      'token_type_ids': token_type_ids,
  }

  lowerCAmelCase    				= 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     ):







  """simple docstring"""






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







@require_tf
class       a							(   a_   , a_   , unittest.TestCase  ):
 snake_case__             =       (
     (
         TFRoFormerModel,
         TFRoFormerForCausalLM,
         TFRoFormerForMaskedLM,
         TFRoFormerForQuestionAnswering,
         TFRoFormerForSequenceClassification,
         TFRoFormerForTokenClassification,
         TFRoFormerForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 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 {}
 )

 snake_case__             =       False
 snake_case__             =       False



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






  if pipeline_test_casse_name == "TextGenerationPipelineTests":
   return True

  return False



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFRoFormerModelTester(self     )
  lowerCAmelCase    				= ConfigTester(self				,       config_class=_snake_case				,       hidden_size=37     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_lm_head(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_multiple_choice(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case     )



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'     )
  self.assertIsNotNone(_snake_case     )







@require_tf
class       a							(   unittest.TestCase  ):



 @slow
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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

  # TODO Replace vocab size
  lowerCAmelCase    				= 5_00_00

  lowerCAmelCase    				= [1, 6, vocab_size]
  self.assertEqual(output.shape				,       _snake_case     )

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

  # TODO Replace values below with what was printed above.
  lowerCAmelCase    				= 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       a							(   unittest.TestCase  ):
 snake_case__             =       1e-4



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= tf.constant([[4, 10]]     )
  lowerCAmelCase    				= TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				,       embedding_dim=6     )

  lowerCAmelCase    				= emba(input_ids.shape     )
  lowerCAmelCase    				= 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     ):







  """simple docstring"""






  lowerCAmelCase    				= 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],
      ]     )
  lowerCAmelCase    				= TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12				,       embedding_dim=5_12     )
  emba([2, 16, 5_12]     )
  lowerCAmelCase    				= emba.weight[:3, :5]

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







@require_tf
class       a							(   unittest.TestCase  ):
 snake_case__             =       1e-4



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= tf.reshape(tf.range(2 * 12 * 16 * 64				,       dtype=tf.floataa     )				,       shape=(2, 12, 16, 64)     ) / 1_00

  lowerCAmelCase    				= -tf.reshape(tf.range(2 * 12 * 16 * 64				,       dtype=tf.floataa     )				,       shape=(2, 12, 16, 64)     ) / 1_00

  lowerCAmelCase    				= TFRoFormerSinusoidalPositionalEmbedding(num_positions=32				,       embedding_dim=64     )
  lowerCAmelCase    				= embed_positions([2, 16, 7_68]     )[None, None, :, :]

  lowerCAmelCase				,lowerCAmelCase    				= TFRoFormerSelfAttention.apply_rotary_position_embeddings(
      _snake_case				,       _snake_case				,       _snake_case     )

  lowerCAmelCase    				= 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],
      ]     )
  lowerCAmelCase    				= 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     )









import math

import tensorflow as tf
from packaging import version






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0      )  ,							x.dtype      )      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(math.pi  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi      ) * (x + coeff * tf.pow(lowerCamelCase__  ,							3      ))      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )

			return x * tf.tanh(tf.math.softplus(lowerCamelCase__      )      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.7_9_7_8_8_4_5_6_0_8  ,							x.dtype      )

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)      ))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(1.7_0_2  ,							x.dtype      )
			return x * tf.math.sigmoid(coeff * x      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			return tf.clip_by_value(_gelu(lowerCamelCase__      )  ,							-10  ,							10      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Dict=-1      ):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase    		=  tf.split(lowerCamelCase__  ,							2  ,							axis=lowerCamelCase__      )
			return a * tf.math.sigmoid(lowerCamelCase__      )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





					'''simple docstring'''




					return tf.keras.activations.gelu(lowerCamelCase__  ,							approximate=lowerCamelCase__      )

		UpperCAmelCase   :    Union[str, Any]     =		tf.keras.activations.gelu



		UpperCAmelCase   :    int     =		approximate_gelu_wrap
else:
		UpperCAmelCase   :    Dict     =		_gelu
		UpperCAmelCase   :    Dict     =		_gelu_new


UpperCAmelCase   :    List[str]     =		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()      )}'      )

