






'''simple docstring'''



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE						=	[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]


def 							lowercase_				(    __A :       Tuple			)       ->      int:



 """simple docstring"""




 for pegasus_name, hf_name in PATTERNS:
  lowercase					:   Optional[int]				  =k.replace(__A					,      __A			)
 return k


def 							lowercase_				(    __A :       dict					,      __A :       dict			)       ->      PegasusForConditionalGeneration:



 """simple docstring"""




 lowercase					:   List[str]				  =DEFAULTS.copy()
 cfg_kwargs.update(__A			)
 lowercase					:   Dict				  =PegasusConfig(**__A			)
 lowercase					:   Optional[int]				  =PegasusForConditionalGeneration(__A			)
 lowercase					:   Union[str, Any]				  =torch_model.model.state_dict()
 lowercase					:   Union[str, Any]				  ={}
 for k, v in tf_weights.items():
  lowercase					:   Dict				  =rename_state_dict_key(__A			)
  if new_k not in sd:
   raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'			)

  if "dense" in k or "proj" in new_k:
   lowercase					:   Union[str, Any]				  =v.T
  lowercase					:   Any				  =torch.tensor(__A					,      dtype=sd[new_k].dtype			)
  assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
 # make sure embedding.padding_idx is respected
 lowercase					:   int				  =torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]			)
 lowercase					:   Dict				  =mapping['''shared.weight''']
 lowercase					:   List[str]				  =mapping['''shared.weight''']
 lowercase					:   Optional[int]				  ={k: torch.zeros_like(__A			) for k, v in sd.items() if k.endswith('''bias'''			) and k not in mapping}
 mapping.update(**__A			)
 lowercase					:   List[str]				  =torch_model.model.load_state_dict(__A					,      strict=__A			)
 lowercase					:   Optional[int]				  =[
     k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
 ]
 assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
 assert extra == [], F'no matches found for the following tf keys {extra}'
 return torch_model


def 							lowercase_				(    __A :       Optional[int]="./ckpt/aeslc/model.ckpt-32000"			)       ->      Dict:



 """simple docstring"""




 lowercase					:   Dict				  =tf.train.list_variables(__A			)
 lowercase					:   str				  ={}
 lowercase					:   Union[str, Any]				  =['''Adafactor''', '''global_step''']
 for name, shape in tqdm(__A					,      desc='''converting tf checkpoint to dict'''			):
  lowercase					:   int				  =any(pat in name for pat in ignore_name			)
  if skip_key:
   continue
  lowercase					:   List[Any]				  =tf.train.load_variable(__A					,      __A			)
  lowercase					:   Tuple				  =array
 return tf_weights


def 							lowercase_				(    __A :       str					,      __A :       str			)       ->      Dict:



 """simple docstring"""




 lowercase					:   Any				  =Path(__A			).parent.name
 lowercase					:   List[Any]				  =task_specific_params[F'summarization_{dataset}']['''max_position_embeddings''']
 lowercase					:   List[str]				  =PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''					,      model_max_length=__A			)
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(__A			)

 # convert model
 lowercase					:   str				  =get_tf_weights_as_numpy(__A			)
 lowercase					:   List[Any]				  =task_specific_params[F'summarization_{dataset}']
 if dataset == "large":
  lowercase					:   Tuple				  =task_specific_params
 lowercase					:   Optional[Any]				  =convert_pegasus(__A					,      __A			)
 torch_model.save_pretrained(__A			)
 lowercase					:   int				  =torch_model.state_dict()
 sd.pop('''model.decoder.embed_positions.weight'''			)
 sd.pop('''model.encoder.embed_positions.weight'''			)
 torch.save(__A					,      Path(__A			) / '''pytorch_model.bin'''			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
      parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      if args.save_dir is None:
            SCREAMING_SNAKE_CASE						=	Path(args.tf_ckpt_path).parent.name
            SCREAMING_SNAKE_CASE						=	os.path.join('pegasus', dataset)
      convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)











'''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_					:




      """simple docstring"""




      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Union[str, Any]=13     ,      UpperCAmelCase			:		int=7     ,      UpperCAmelCase			:		Union[str, Any]=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Any=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Any=99     ,      UpperCAmelCase			:		List[Any]=32     ,      UpperCAmelCase			:		str=2     ,      UpperCAmelCase			:		str=4     ,      UpperCAmelCase			:		List[Any]=37     ,      UpperCAmelCase			:		str="gelu"     ,      UpperCAmelCase			:		List[Any]=0.1     ,      UpperCAmelCase			:		int=0.1     ,      UpperCAmelCase			:		Dict=512     ,      UpperCAmelCase			:		List[Any]=16     ,      UpperCAmelCase			:		List[str]=2     ,      UpperCAmelCase			:		Any=0.0_2     ,      UpperCAmelCase			:		List[str]=3     ,      UpperCAmelCase			:		Dict=4     ,      UpperCAmelCase			:		Optional[int]=None     ,      )  ->  Optional[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]		)  ->  int:


       '''simple docstring'''

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

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

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

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

       lowercase					:   List[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=UpperCAmelCase     ,      )

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerModel(config=UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

       lowercase					:   Tuple				  =[input_ids, input_mask]
       lowercase					:   str				  =model(UpperCAmelCase		)

       lowercase					:   Dict				  =model(UpperCAmelCase		)

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






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[str]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Dict				  =True
       lowercase					:   List[Any]				  =TFRoFormerForCausalLM(config=UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       lowercase					:   Optional[Any]				  =model(UpperCAmelCase		)['''logits''']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape		)     ,      [self.batch_size, self.seq_length, self.vocab_size]		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Tuple		)  ->  Dict:


       '''simple docstring'''

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






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.num_labels
       lowercase					:   Optional[int]				  =TFRoFormerForSequenceClassification(config=UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       lowercase					:   Optional[Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)		)






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple		)  ->  Union[str, Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		int		)  ->  Optional[int]:


       '''simple docstring'''

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






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		str		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Tuple				  =TFRoFormerForQuestionAnswering(config=UpperCAmelCase		)
       lowercase					:   List[str]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       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 				A__      ( self			:		List[Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.prepare_config_and_inputs()
       (
           (
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		
       )					:   Optional[int]				  =config_and_inputs
       lowercase					:   str				  ={'''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				):




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_								=			(
          {
              '''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 {}
      )

      UpperCamelCase_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		str		)  ->  Tuple:


       '''simple docstring'''

       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

       return False






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerModelTester(self		)
       lowercase					:   Union[str, Any]				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      hidden_size=37		)






      def 				A__      ( self			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*UpperCAmelCase		)






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  Any:


       '''simple docstring'''

       lowercase					:   int				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase		)






      @slow
      def 				A__      ( self			:		str		)  ->  str:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''		)
       self.assertIsNotNone(UpperCAmelCase		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      @slow
      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

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

       # TODO Replace vocab size
       lowercase					:   Tuple				  =5_0000

       lowercase					:   List[str]				  =[1, 6, vocab_size]
       self.assertEqual(output.shape     ,      UpperCAmelCase		)

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

       # TODO Replace values below with what was printed above.
       lowercase					:   Dict				  =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]     ,      UpperCAmelCase     ,      atol=1e-4		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			1e-4






      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =tf.constant([[4, 10]]		)
       lowercase					:   List[Any]				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,      embedding_dim=6		)

       lowercase					:   Any				  =emba(input_ids.shape		)
       lowercase					:   List[str]				  =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(UpperCAmelCase     ,      UpperCAmelCase     ,      atol=self.tolerance		)






      def 				A__      ( self			:		Optional[Any]		)  ->  int:


       '''simple docstring'''

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

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





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			1e-4






      def 				A__      ( self			:		Dict		)  ->  Dict:


       '''simple docstring'''

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

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

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

       lowercase  ,		lowercase					:   Optional[int]				  =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

       lowercase					:   Any				  =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],
           ]		)
       lowercase					:   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]     ,      UpperCAmelCase     ,      atol=self.tolerance		)
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,      UpperCAmelCase     ,      atol=self.tolerance		)



