





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




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Union[str, Any]   =						ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size		)

         __snake_case:						List[str]   =						None
         if self.use_input_mask:
           __snake_case:						int   =						random_attention_mask([self.batch_size, self.seq_length]		)

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

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

         __snake_case:						Optional[int]   =						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=A							,				)

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




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Dict							,				A :    int							,				A :    Any							,				A :    Optional[Any]							,				A :    Dict							,				A :    List[str]							,				A :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						TFRoFormerModel(config=A		)
         __snake_case:						Dict   =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

         __snake_case:						int   =						[input_ids, input_mask]
         __snake_case:						str   =						model(A		)

         __snake_case:						List[str]   =						model(A		)

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




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Tuple							,				A :    Optional[Any]							,				A :    Optional[Any]							,				A :    List[Any]							,				A :    List[Any]							,				A :    List[str]							,				A :    Optional[int]		):
         __snake_case:						List[Any]   =						True
         __snake_case:						Optional[int]   =						TFRoFormerForCausalLM(config=A		)
         __snake_case:						Optional[Any]   =						{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         __snake_case:						List[str]   =						model(A		)["""logits"""]
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape		)							,				[self.batch_size, self.seq_length, self.vocab_size]		)




       def 							UpperCAmelCase__ (  self :    Any							,				A :    Optional[Any]							,				A :    Dict							,				A :    str							,				A :    Optional[Any]							,				A :    Union[str, Any]							,				A :    List[str]							,				A :    Tuple		):
         __snake_case:						Optional[Any]   =						TFRoFormerForMaskedLM(config=A		)
         __snake_case:						List[Any]   =						{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         __snake_case:						str   =						model(A		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.vocab_size)		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Optional[Any]							,				A :    List[Any]							,				A :    List[Any]							,				A :    int							,				A :    List[str]							,				A :    str							,				A :    Union[str, Any]		):
         __snake_case:						str   =						self.num_labels
         __snake_case:						Union[str, Any]   =						TFRoFormerForSequenceClassification(config=A		)
         __snake_case:						int   =						{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         __snake_case:						Dict   =						model(A		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)		)




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




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    str							,				A :    List[Any]							,				A :    Dict							,				A :    Union[str, Any]							,				A :    Tuple							,				A :    Dict							,				A :    Optional[int]		):
         __snake_case:						Dict   =						self.num_labels
         __snake_case:						Union[str, Any]   =						TFRoFormerForTokenClassification(config=A		)
         __snake_case:						Optional[int]   =						{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         __snake_case:						Dict   =						model(A		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.num_labels)		)




       def 							UpperCAmelCase__ (  self :    int							,				A :    List[Any]							,				A :    List[str]							,				A :    str							,				A :    List[Any]							,				A :    Optional[int]							,				A :    str							,				A :    Optional[Any]		):
         __snake_case:						Dict   =						TFRoFormerForQuestionAnswering(config=A		)
         __snake_case:						List[str]   =						{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         __snake_case:						List[str]   =						model(A		)
         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 :    Union[str, Any]		):
         __snake_case:						int   =						self.prepare_config_and_inputs()
         (
             (
             __snake_case    
         )  ,   (
             __snake_case    
         )  ,   (
             __snake_case    
         )  ,   (
             __snake_case    
         )  ,   (
             __snake_case    
         )  ,   (
             __snake_case    
         )  ,   (
             __snake_case    
         )  ,       
         ):						Tuple   =						config_and_inputs
         __snake_case:						Dict   =						{"""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			):





       '''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 :    Any							,				A :    Optional[int]							,				A :    str							,				A :    Union[str, Any]							,				A :    Union[str, Any]							,				A :    int		):
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
           return True

         return False




       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						Tuple   =						TFRoFormerModelTester(self		)
         __snake_case:						Tuple   =						ConfigTester(self							,				config_class=A							,				hidden_size=37		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         self.config_tester.run_common_tests()




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A		)




       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						Dict   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						int   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*A		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						str   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						str   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						List[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						List[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A		)






       @slow
       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[Any]   =						TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""		)
         self.assertIsNotNone(A		)







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @slow
       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						List[str]   =						TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""		)
         __snake_case:						Dict   =						tf.constant([[0, 1, 2, 3, 4, 5]]		)
         __snake_case:						Optional[int]   =						model(A		)[0]

         # TODO Replace vocab size
         __snake_case:						Optional[Any]   =						50_000

         __snake_case:						Tuple   =						[1, 6, vocab_size]
         self.assertEqual(output.shape							,				A		)

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

         # TODO Replace values below with what was printed above.
         __snake_case:						Any   =						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]							,				A							,				atol=1E-4		)







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     1E-4




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						Tuple   =						tf.constant([[4, 10]]		)
         __snake_case:						str   =						TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,				embedding_dim=6		)

         __snake_case:						Dict   =						emba(input_ids.shape		)
         __snake_case:						Optional[int]   =						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(A							,				A							,				atol=self.tolerance		)






       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Union[str, Any]   =						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],
             ]		)
         __snake_case:						Dict   =						TFRoFormerSinusoidalPositionalEmbedding(num_positions=512							,				embedding_dim=512		)
         emba([2, 16, 512]		)
         __snake_case:						Tuple   =						emba.weight[:3, :5]

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







@require_tf
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     1E-4




       def 							UpperCAmelCase__ (  self :    int		):
         # 2,12,16,64
         __snake_case:						List[str]   =						tf.reshape(tf.range(2 * 12 * 16 * 64							,				dtype=tf.floataa		)							,				shape=(2, 12, 16, 64)		) / 100

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

         __snake_case:						int   =						TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							,				embedding_dim=64		)
         __snake_case:						str   =						embed_positions([2, 16, 768]		)[None, None, :, :]

         __snake_case  ,   __snake_case:						int   =						TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             A							,				A							,				A		)

         __snake_case:						List[Any]   =						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],
             ]		)
         __snake_case:						List[Any]   =						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]							,				A							,				atol=self.tolerance		)
         tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,				A							,				atol=self.tolerance		)










UpperCAmelCase__   	=							8.31_44_62  # Unit - J mol-1 K-1
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if moles < 0 or kelvin < 0 or volume < 0:
  raise ValueError('''Invalid inputs. Enter positive value.'''						)
 return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			float:







 """simple docstring"""


 if moles < 0 or kelvin < 0 or pressure < 0:
  raise ValueError('''Invalid inputs. Enter positive value.'''						)
 return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


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

  testmod()

