
'''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 			UpperCAmelCase__    :



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



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: List[Any]		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

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

          _lowerCamelCase					: Dict		    = None
          if self.use_token_type_ids:
                _lowerCamelCase					: List[str]		    = ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size  )

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

          _lowerCamelCase					: 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=__A,)

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



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

          _lowerCamelCase					: Dict		    = [input_ids, input_mask]
          _lowerCamelCase					: Optional[int]		    = model(__A  )

          _lowerCamelCase					: List[Any]		    = model(__A  )

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



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



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



    def 	lowerCamelCase_	(						self   :							Any,__A   :							int,__A   :							Dict,__A   :							List[str],__A   :							Tuple,__A   :							List[str],__A   :							int,__A   :							Union[str, Any]  ):
          _lowerCamelCase					: int		    = self.num_labels
          _lowerCamelCase					: int		    = TFRoFormerForSequenceClassification(config=__A  )
          _lowerCamelCase					: Optional[int]		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          _lowerCamelCase					: Union[str, Any]		    = model(__A  )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)  )



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



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



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							Union[str, Any],__A   :							Dict,__A   :							Optional[Any],__A   :							List[Any],__A   :							int,__A   :							Tuple,__A   :							Optional[int]  ):
          _lowerCamelCase					: Any		    = TFRoFormerForQuestionAnswering(config=__A  )
          _lowerCamelCase					: int		    = {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

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


@require_tf
class 			UpperCAmelCase__    (							A    ,						A    ,						unittest.TestCase			):
    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 	lowerCamelCase_	(						self   :							List[str],__A   :							List[str],__A   :							List[Any],__A   :							int,__A   :							str,__A   :							Optional[int]  ):
          if pipeline_test_casse_name == "TextGenerationPipelineTests":
                return True

          return False



    def 	lowerCamelCase_	(						self   :							Optional[int]  ):
          _lowerCamelCase					: Dict		    = TFRoFormerModelTester(self  )
          _lowerCamelCase					: List[Any]		    = ConfigTester(self,config_class=__A,hidden_size=3_7  )



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



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Optional[Any]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_lm_head(*__A  )



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: int		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__A  )



    def 	lowerCamelCase_	(						self   :							int  ):
          _lowerCamelCase					: Optional[int]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__A  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Dict		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*__A  )



    @slow
    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: List[Any]		    = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"  )
          self.assertIsNotNone(__A  )


@require_tf
class 			UpperCAmelCase__    (							unittest.TestCase			):



    @slow
    def 	lowerCamelCase_	(						self   :							Any  ):
          _lowerCamelCase					: Optional[Any]		    = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"  )
          _lowerCamelCase					: str		    = tf.constant([[0, 1, 2, 3, 4, 5]]  )
          _lowerCamelCase					: List[str]		    = model(__A  )[0]

          # TODO Replace vocab size
          _lowerCamelCase					: List[Any]		    = 5_0_0_0_0

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

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

          # TODO Replace values below with what was printed above.
          _lowerCamelCase					: Any		    = tf.constant(
              [
                  [
                      [-0.12053341, -1.0264901, 0.29221946],
                      [-1.5133783, 0.197433, 0.15190607],
                      [-5.0135403, -3.900256, -0.84038764],
                  ]
              ]  )
          tf.debugging.assert_near(output[:, :3, :3],__A,atol=1e-4  )


@require_tf
class 			UpperCAmelCase__    (							unittest.TestCase			):
    lowerCAmelCase_					     =		1E-4



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Tuple		    = tf.constant([[4, 1_0]]  )
          _lowerCamelCase					: Any		    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6,embedding_dim=6  )

          _lowerCamelCase					: int		    = emba(input_ids.shape  )
          _lowerCamelCase					: Union[str, Any]		    = 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 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: 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],
              ]  )
          _lowerCamelCase					: Tuple		    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2,embedding_dim=5_1_2  )
          emba([2, 1_6, 5_1_2]  )
          _lowerCamelCase					: List[str]		    = emba.weight[:3, :5]

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


@require_tf
class 			UpperCAmelCase__    (							unittest.TestCase			):
    lowerCAmelCase_					     =		1E-4



    def 	lowerCamelCase_	(						self   :							int  ):
          # 2,12,16,64
          _lowerCamelCase					: Optional[Any]		    = 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

          _lowerCamelCase					: Union[str, Any]		    = -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

          _lowerCamelCase					: Union[str, Any]		    = TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2,embedding_dim=6_4  )
          _lowerCamelCase					: Tuple		    = embed_positions([2, 1_6, 7_6_8]  )[None, None, :, :]

          _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
              __A,__A,__A  )

          _lowerCamelCase					: Union[str, 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],
              ]  )
          _lowerCamelCase					: Dict		    = 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  )

"""simple docstring"""


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def  lowercase						(	lowerCAmelCase__						):
			def wrapper(*lowerCAmelCase__	,**lowerCAmelCase__						):
						lowerCamelCase_					=  timeit.default_timer()
						lowerCamelCase_					=  func(*lowerCAmelCase__	,**lowerCAmelCase__						)
						lowerCamelCase_					=  timeit.default_timer() - starttime
						return delta

			lowerCamelCase_					=  func.__name__

			return wrapper






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  []
			lowerCamelCase_					=  seq_shapes or {}
			for i in range(lowerCAmelCase__						):
						lowerCamelCase_					=  {}
						for col_id, (k, v) in enumerate(features.items()						):
									if isinstance(lowerCAmelCase__	,_ArrayXD						):
												lowerCamelCase_					=  np.random.rand(*v.shape						).astype(v.dtype						)
									elif isinstance(lowerCAmelCase__	,datasets.Value						):
												if v.dtype == "string":
															lowerCamelCase_					=  '''The small grey turtle was surprisingly fast when challenged.'''
												else:
															lowerCamelCase_					=  np.random.randint(10	,size=1						).astype(v.dtype						).item()
									elif isinstance(lowerCAmelCase__	,datasets.Sequence						):
												while isinstance(lowerCAmelCase__	,datasets.Sequence						):
															lowerCamelCase_					=  v.feature
												lowerCamelCase_					=  seq_shapes[k]
												lowerCamelCase_					=  np.random.rand(*lowerCAmelCase__						).astype(v.dtype						)
									lowerCamelCase_					=  data

						dummy_data.append((i, example)						)

			return dummy_data






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=100	,lowerCAmelCase__=None						):
			lowerCamelCase_					=  generate_examples(lowerCAmelCase__	,num_examples=lowerCAmelCase__	,seq_shapes=lowerCAmelCase__						)

			with ArrowWriter(features=lowerCAmelCase__	,path=lowerCAmelCase__						) as writer:
						for key, record in dummy_data:
									lowerCamelCase_					=  features.encode_example(lowerCAmelCase__						)
									writer.write(lowerCAmelCase__						)

						lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."						)

			lowerCamelCase_					=  datasets.Dataset.from_file(filename=lowerCAmelCase__	,info=datasets.DatasetInfo(features=lowerCAmelCase__						)						)

			return dataset



