





"""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 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__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=512 ,						__lowerCamelCase=16 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=3 ,						__lowerCamelCase=4 ,						__lowerCamelCase=None ,						):



     '''simple docstring'''




     __A   :						Dict									=					parent
     __A   :						List[Any]									=					13
     __A   :						Optional[Any]									=					7
     __A   :						Optional[int]									=					True
     __A   :						Optional[Any]									=					True
     __A   :						Dict									=					True
     __A   :						Dict									=					True
     __A   :						Any									=					99
     __A   :						List[Any]									=					32
     __A   :						str									=					2
     __A   :						Any									=					4
     __A   :						Optional[Any]									=					37
     __A   :						List[Any]									=					'''gelu'''
     __A   :						Optional[Any]									=					0.1
     __A   :						Tuple									=					0.1
     __A   :						List[str]									=					512
     __A   :						Any									=					16
     __A   :						Union[str, Any]									=					2
     __A   :						Union[str, Any]									=					0.0_2
     __A   :						str									=					3
     __A   :						Tuple									=					4
     __A   :						Tuple									=					None

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )

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

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

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

     __A   :						Union[str, 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=__lowerCamelCase ,						)

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					TFRoFormerModel(config=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

     __A   :						Any									=					[input_ids, input_mask]
     __A   :						Optional[int]									=					model(__lowerCamelCase  )

     __A   :						Any									=					model(__lowerCamelCase  )

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Tuple									=					TFRoFormerForMaskedLM(config=__lowerCamelCase  )
     __A   :						List[Any]									=					{
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }
     __A   :						Optional[int]									=					model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.num_labels
     __A   :						str									=					TFRoFormerForSequenceClassification(config=__lowerCamelCase  )
     __A   :						Any									=					{
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }

     __A   :						Optional[int]									=					model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						int									=					TFRoFormerForQuestionAnswering(config=__lowerCamelCase  )
     __A   :						int									=					{
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }

     __A   :						List[str]									=					model(__lowerCamelCase  )
     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'''




     __A   :						List[str]									=					self.prepare_config_and_inputs()
     (
         (
         __A
     )			,	(
         __A
     )			,	(
         __A
     )			,	(
         __A
     )			,	(
         __A
     )			,	(
         __A
     )			,	(
         __A
     )			,	
     )   :						Tuple									=					config_and_inputs
     __A   :						int									=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
     return config, inputs_dict






@require_tf
class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     if pipeline_test_casse_name == "TextGenerationPipelineTests":
         return True

     return False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					TFRoFormerModelTester(self  )
     __A   :						Optional[int]									=					ConfigTester(self ,						config_class=__lowerCamelCase ,						hidden_size=37  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''  )
     self.assertIsNotNone(__lowerCamelCase  )






@require_tf
class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

     # TODO Replace vocab size
     __A   :						List[Any]									=					5_0000

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

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

     # TODO Replace values below with what was printed above.
     __A   :						List[str]									=					tf.constant(
         [
             [
                 [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
                 [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
                 [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
             ]
         ]  )
     tf.debugging.assert_near(output[:, :3, :3] ,						__lowerCamelCase ,						atol=1e-4  )






@require_tf
class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					1e-4

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

     __A   :						Optional[Any]									=					emba(input_ids.shape  )
     __A   :						List[Any]									=					tf.constant(
         [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]  )

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







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					tf.constant(
         [
             [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
             [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
             [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
         ]  )
     __A   :						Optional[int]									=					TFRoFormerSinusoidalPositionalEmbedding(num_positions=512 ,						embedding_dim=512  )
     emba([2, 16, 512]  )
     __A   :						Optional[int]									=					emba.weight[:3, :5]

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






@require_tf
class 		__snake_case (		unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					1e-4

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




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

     __A   :						Union[str, Any]									=					-tf.reshape(tf.range(2 * 12 * 16 * 64 ,						dtype=tf.floataa  ) ,						shape=(2, 12, 16, 64)  ) / 100

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

     __A			,	__A   :						int									=					TFRoFormerSelfAttention.apply_rotary_position_embeddings(
         __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						Tuple									=					tf.constant(
         [
             [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
             [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
             [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
             [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
             [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
             [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
         ]  )
     __A   :						int									=					tf.constant(
         [
             [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
             [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
             [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
             [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
             [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
             [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
         ]  )

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











"""simple docstring"""



a_			=      {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
a_			=      {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}







def 		__lowercase					(       snake_case_  :  float    ,snake_case_  :  str    ,snake_case_  :  str						)       ->float:



    '''simple docstring'''







    __A   :						Tuple									=					from_type.lower().strip('''s'''						)
    __A   :						Optional[int]									=					to_type.lower().strip('''s'''						)

    __A   :						List[str]									=					UNIT_SYMBOL.get(snake_case_    ,snake_case_						)
    __A   :						Any									=					UNIT_SYMBOL.get(snake_case_    ,snake_case_						)

    if from_sanitized not in METRIC_CONVERSION:
        __A   :						int									=					(
            F"""Invalid 'from_type' value: {from_type!r}.\n"""
            F"""Conversion abbreviations are: {', '.join(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)
    if to_sanitized not in METRIC_CONVERSION:
        __A   :						str									=					(
            F"""Invalid 'to_type' value: {to_type!r}.\n"""
            F"""Conversion abbreviations are: {', '.join(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)
    __A   :						Optional[Any]									=					METRIC_CONVERSION[from_sanitized]
    __A   :						Optional[int]									=					METRIC_CONVERSION[to_sanitized]
    __A   :						Union[str, Any]									=					1

    if from_exponent > to_exponent:
        __A   :						Dict									=					from_exponent - to_exponent
    else:
        __A   :						Union[str, Any]									=					-(to_exponent - from_exponent)

    return value * pow(10    ,snake_case_						)


if __name__ == "__main__":
       from doctest import testmod

       testmod()




