






'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 							lowercase_				(    __A :       Union[str, Any]			)       ->      Union[str, Any]:



 """simple docstring"""




 lowercase					:   str				  =SwinConfig(image_size=1_9_2			)

 if "base" in model_name:
  lowercase					:   Dict				  =6
  lowercase					:   Union[str, Any]				  =1_2_8
  lowercase					:   int				  =(2, 2, 1_8, 2)
  lowercase					:   str				  =(4, 8, 1_6, 3_2)
 elif "large" in model_name:
  lowercase					:   List[str]				  =1_2
  lowercase					:   Optional[Any]				  =1_9_2
  lowercase					:   int				  =(2, 2, 1_8, 2)
  lowercase					:   int				  =(6, 1_2, 2_4, 4_8)
 else:
  raise ValueError('''Model not supported, only supports base and large variants'''			)

 lowercase					:   Union[str, Any]				  =window_size
 lowercase					:   List[Any]				  =embed_dim
 lowercase					:   Union[str, Any]				  =depths
 lowercase					:   List[Any]				  =num_heads

 return config


def 							lowercase_				(    __A :       Tuple			)       ->      List[str]:



 """simple docstring"""




 if "encoder.mask_token" in name:
  lowercase					:   List[Any]				  =name.replace('''encoder.mask_token'''					,      '''embeddings.mask_token'''			)
 if "encoder.patch_embed.proj" in name:
  lowercase					:   List[Any]				  =name.replace('''encoder.patch_embed.proj'''					,      '''embeddings.patch_embeddings.projection'''			)
 if "encoder.patch_embed.norm" in name:
  lowercase					:   Union[str, Any]				  =name.replace('''encoder.patch_embed.norm'''					,      '''embeddings.norm'''			)
 if "attn.proj" in name:
  lowercase					:   Optional[Any]				  =name.replace('''attn.proj'''					,      '''attention.output.dense'''			)
 if "attn" in name:
  lowercase					:   Dict				  =name.replace('''attn'''					,      '''attention.self'''			)
 if "norm1" in name:
  lowercase					:   Any				  =name.replace('''norm1'''					,      '''layernorm_before'''			)
 if "norm2" in name:
  lowercase					:   int				  =name.replace('''norm2'''					,      '''layernorm_after'''			)
 if "mlp.fc1" in name:
  lowercase					:   int				  =name.replace('''mlp.fc1'''					,      '''intermediate.dense'''			)
 if "mlp.fc2" in name:
  lowercase					:   List[str]				  =name.replace('''mlp.fc2'''					,      '''output.dense'''			)

 if name == "encoder.norm.weight":
  lowercase					:   List[Any]				  ='''layernorm.weight'''
 if name == "encoder.norm.bias":
  lowercase					:   Union[str, Any]				  ='''layernorm.bias'''

 if "decoder" in name:
  pass
 else:
  lowercase					:   Optional[int]				  ='''swin.''' + name

 return name


def 							lowercase_				(    __A :       Optional[Any]					,      __A :       List[Any]			)       ->      Any:



 """simple docstring"""




 for key in orig_state_dict.copy().keys():
  lowercase					:   Tuple				  =orig_state_dict.pop(__A			)

  if "attn_mask" in key:
   pass
  elif "qkv" in key:
   lowercase					:   int				  =key.split('''.'''			)
   lowercase					:   Union[str, Any]				  =int(key_split[2]			)
   lowercase					:   Any				  =int(key_split[4]			)
   lowercase					:   str				  =model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    lowercase					:   Tuple				  =val[:dim, :]
    lowercase					:   str				  =val[
        dim : dim * 2, :
    ]
    lowercase					:   Optional[int]				  =val[-dim:, :]
   else:
    lowercase					:   int				  =val[
        :dim
    ]
    lowercase					:   List[Any]				  =val[
        dim : dim * 2
    ]
    lowercase					:   Optional[Any]				  =val[
        -dim:
    ]
  else:
   lowercase					:   Optional[Any]				  =val

 return orig_state_dict


def 							lowercase_				(    __A :       List[str]					,      __A :       str					,      __A :       str					,      __A :       List[Any]			)       ->      int:



 """simple docstring"""




 lowercase					:   Any				  =torch.load(__A					,      map_location='''cpu'''			)['''model''']

 lowercase					:   Optional[int]				  =get_swin_config(__A			)
 lowercase					:   Optional[Any]				  =SwinForMaskedImageModeling(__A			)
 model.eval()

 lowercase					:   Optional[Any]				  =convert_state_dict(__A					,      __A			)
 model.load_state_dict(__A			)

 lowercase					:   int				  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''

 lowercase					:   Union[str, Any]				  =ViTImageProcessor(size={'''height''': 1_9_2, '''width''': 1_9_2}			)
 lowercase					:   Any				  =Image.open(requests.get(__A					,      stream=__A			).raw			)
 lowercase					:   Any				  =image_processor(images=__A					,      return_tensors='''pt'''			)

 with torch.no_grad():
  lowercase					:   Tuple				  =model(**__A			).logits

 print(outputs.keys()			)
 print('''Looks ok!'''			)

 if pytorch_dump_folder_path is not None:
  print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
  model.save_pretrained(__A			)

  print(F'Saving image processor to {pytorch_dump_folder_path}'			)
  image_processor.save_pretrained(__A			)

 if push_to_hub:
  print(F'Pushing model and image processor for {model_name} to hub'			)
  model.push_to_hub(F'microsoft/{model_name}'			)
  image_processor.push_to_hub(F'microsoft/{model_name}'			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE						=	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model_name',
          default='swin-base-simmim-window6-192',
          type=str,
          choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
          help='Name of the Swin SimMIM model you\'d like to convert.',
      )
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
          type=str,
          help='Path to the original PyTorch checkpoint (.pth file).',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )
      parser.add_argument(
          '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
      )

      SCREAMING_SNAKE_CASE						=	parser.parse_args()
      convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











'''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		)



