from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= Dict[str, Any]
UpperCamelCase__    							= List[Prediction]





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""		)

         requires_backends(self	,  "vision"		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)


   def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



         return super().__call__(*__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_outputs["target_size"]
         if self.tokenizer is not None:
               # This is a LayoutLMForTokenClassification variant.
               # The OCR got the boxes and the model classified the words.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               UpperCAmelCase__		,				UpperCAmelCase__								=  model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
               UpperCAmelCase__								=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
               UpperCAmelCase__								=  [unnormalize(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  raw_annotations[0]
               UpperCAmelCase__								=  raw_annotation["scores"]
               UpperCAmelCase__								=  raw_annotation["labels"]
               UpperCAmelCase__								=  raw_annotation["boxes"]

               UpperCAmelCase__								=  scores.tolist()
               UpperCAmelCase__								=  [self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__								=  [self._get_bounding_box(__UpperCAmelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [
                   dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		)
                   for vals in zip(raw_annotation["scores"]	,  raw_annotation["labels"]	,  raw_annotation["boxes"]		)
               ]

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



         if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
         UpperCAmelCase__								=  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox




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 					A							:


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



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  1_3
         UpperCAmelCase__								=  7
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  9_9
         UpperCAmelCase__								=  3_2
         UpperCAmelCase__								=  2
         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  3_7
         UpperCAmelCase__								=  "gelu"
         UpperCAmelCase__								=  0.1
         UpperCAmelCase__								=  0.1
         UpperCAmelCase__								=  5_1_2
         UpperCAmelCase__								=  1_6
         UpperCAmelCase__								=  2
         UpperCAmelCase__								=  0.02
         UpperCAmelCase__								=  3
         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  None


   def       lowercase_				(self					:						Union[str, Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size		)

         UpperCAmelCase__								=  None
         if self.use_input_mask:
               UpperCAmelCase__								=  random_attention_mask([self.batch_size, self.seq_length]		)

         UpperCAmelCase__								=  None
         if self.use_token_type_ids:
               UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size		)

         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  None
         if self.use_labels:
               UpperCAmelCase__								=  ids_tensor([self.batch_size]	,  self.type_sequence_label_size		)
               UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	,  self.num_labels		)
               UpperCAmelCase__								=  ids_tensor([self.batch_size]	,  self.num_choices		)

         UpperCAmelCase__								=  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       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Any		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TFRoFormerModel(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

         UpperCAmelCase__								=  [input_ids, input_mask]
         UpperCAmelCase__								=  model(__UpperCAmelCase		)

         UpperCAmelCase__								=  model(__UpperCAmelCase		)

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


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						List[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  TFRoFormerForCausalLM(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         UpperCAmelCase__								=  model(__UpperCAmelCase		)["logits"]
         self.parent.assertListEqual(
             list(prediction_scores.numpy().shape		)	,  [self.batch_size, self.seq_length, self.vocab_size]		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						int		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  TFRoFormerForMaskedLM(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size)		)


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  TFRoFormerForSequenceClassification(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Optional[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_choices
         UpperCAmelCase__								=  TFRoFormerForMultipleChoice(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  tf.tile(tf.expand_dims(__UpperCAmelCase	,  1		)	,  (1, self.num_choices, 1)		)
         UpperCAmelCase__								=  tf.tile(tf.expand_dims(__UpperCAmelCase	,  1		)	,  (1, self.num_choices, 1)		)
         UpperCAmelCase__								=  tf.tile(tf.expand_dims(__UpperCAmelCase	,  1		)	,  (1, self.num_choices, 1)		)
         UpperCAmelCase__								=  {
             "input_ids": multiple_choice_inputs_ids,
             "attention_mask": multiple_choice_input_mask,
             "token_type_ids": multiple_choice_token_type_ids,
         }
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_choices)		)


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						int	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						int		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  TFRoFormerForTokenClassification(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }
         UpperCAmelCase__								=  model(__UpperCAmelCase		)
         self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.num_labels)		)


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Dict		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  TFRoFormerForQuestionAnswering(config=__UpperCAmelCase		)
         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         UpperCAmelCase__								=  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       lowercase_				(self					:						Dict		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				(
             UpperCAmelCase__
         )		,				
         )								=  config_and_inputs
         UpperCAmelCase__								=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict





@require_tf
class 					A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			str              =      (
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   __UpperCAmelCase			:			List[str]              =      (
       {
           '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			:			Optional[int]              =      False
   __UpperCAmelCase			:			List[Any]              =      False


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any		)						->    int:
         """simple docstring"""



         if pipeline_test_casse_name == "TextGenerationPipelineTests":
               return True

         return False


   def       lowercase_				(self					:						Union[str, Any]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  TFRoFormerModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__UpperCAmelCase	,  hidden_size=3_7		)


   def       lowercase_				(self					:						Optional[int]		)						->    Any:
         """simple docstring"""



         self.config_tester.run_common_tests()


   def       lowercase_				(self					:						Union[str, Any]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase		)


   def       lowercase_				(self					:						int		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase		)


   def       lowercase_				(self					:						List[str]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase		)





   @slow
   def       lowercase_				(self					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"		)
         self.assertIsNotNone(__UpperCAmelCase		)





@require_tf
class 					A							(    unittest.TestCase					):


   @slow
   def       lowercase_				(self					:						List[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
         UpperCAmelCase__								=  tf.constant([[0, 1, 2, 3, 4, 5]]		)
         UpperCAmelCase__								=  model(__UpperCAmelCase		)[0]

         # TODO Replace vocab size
         UpperCAmelCase__								=  5_0_0_0_0

         UpperCAmelCase__								=  [1, 6, vocab_size]
         self.assertEqual(output.shape	,  __UpperCAmelCase		)

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

         # TODO Replace values below with what was printed above.
         UpperCAmelCase__								=  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]	,  __UpperCAmelCase	,  atol=1E-4		)





@require_tf
class 					A							(    unittest.TestCase					):
   __UpperCAmelCase			:			Tuple              =      1E-4


   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  tf.constant([[4, 1_0]]		)
         UpperCAmelCase__								=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,  embedding_dim=6		)

         UpperCAmelCase__								=  emba(input_ids.shape		)
         UpperCAmelCase__								=  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(__UpperCAmelCase	,  __UpperCAmelCase	,  atol=self.tolerance		)





   def       lowercase_				(self					:						List[Any]		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  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],
             ]		)
         UpperCAmelCase__								=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2	,  embedding_dim=5_1_2		)
         emba([2, 1_6, 5_1_2]		)
         UpperCAmelCase__								=  emba.weight[:3, :5]

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





@require_tf
class 					A							(    unittest.TestCase					):
   __UpperCAmelCase			:			Any              =      1E-4


   def       lowercase_				(self					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  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

         UpperCAmelCase__								=  -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

         UpperCAmelCase__								=  TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2	,  embedding_dim=6_4		)
         UpperCAmelCase__								=  embed_positions([2, 1_6, 7_6_8]		)[None, None, :, :]

         UpperCAmelCase__		,				UpperCAmelCase__								=  TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)

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



