"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
      )





class 					A							:


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



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  seq_length
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_input_mask
         UpperCAmelCase__								=  use_token_type_ids
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  type_vocab_size
         UpperCAmelCase__								=  type_sequence_label_size
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  num_labels
         UpperCAmelCase__								=  num_choices
         UpperCAmelCase__								=  relative_attention
         UpperCAmelCase__								=  position_biased_input
         UpperCAmelCase__								=  pos_att_type
         UpperCAmelCase__								=  scope


   def       lowercase_				(self					:						Optional[Any]		)						->    Dict:
         """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__								=  DebertaVaConfig(
             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	,  relative_attention=self.relative_attention	,  position_biased_input=self.position_biased_input	,  initializer_range=self.initializer_range	,  return_dict=lowerCAmelCase__	,  )

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


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



         UpperCAmelCase__								=  TFDebertaVaModel(config=lowerCAmelCase__		)
         UpperCAmelCase__								=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

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

         UpperCAmelCase__								=  model(lowerCAmelCase__		)

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


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



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


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



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

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


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



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


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



         UpperCAmelCase__								=  TFDebertaVaForQuestionAnswering(config=lowerCAmelCase__		)
         UpperCAmelCase__								=  {
             "input_ids": input_ids,
             "attention_mask": input_mask,
             "token_type_ids": token_type_ids,
         }

         UpperCAmelCase__								=  model(lowerCAmelCase__		)
         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					:						Union[str, Any]		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.prepare_config_and_inputs()
         (
             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							(    a__		,    a__		,    unittest.TestCase					):
   __UpperCAmelCase			:			Union[str, Any]              =      (
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   __UpperCAmelCase			:			List[str]              =      (
       {
           """feature-extraction""": TFDebertaVaModel,
           """fill-mask""": TFDebertaVaForMaskedLM,
           """question-answering""": TFDebertaVaForQuestionAnswering,
           """text-classification""": TFDebertaVaForSequenceClassification,
           """token-classification""": TFDebertaVaForTokenClassification,
           """zero-shot""": TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   __UpperCAmelCase			:			Union[str, Any]              =      False
   __UpperCAmelCase			:			Dict              =      False


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



         UpperCAmelCase__								=  TFDebertaVaModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=lowerCAmelCase__	,  hidden_size=3_7		)


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



         self.config_tester.run_common_tests()


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



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


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



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


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



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


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



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


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



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





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



         UpperCAmelCase__								=  TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
         self.assertIsNotNone(lowerCAmelCase__		)





@require_tf
class 					A							(    unittest.TestCase					):


   @unittest.skip(reason="Model not available yet"		)
   def       lowercase_				(self					:						Tuple		)						->    int:
         """simple docstring"""



         pass





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



         UpperCAmelCase__								=  TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"		)
         UpperCAmelCase__								=  tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]		)
         UpperCAmelCase__								=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
         UpperCAmelCase__								=  model(lowerCAmelCase__	,  attention_mask=lowerCAmelCase__		)[0]

         UpperCAmelCase__								=  tf.constant(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]		)
         tf.debugging.assert_near(output[:, 1:4, 1:4]	,  lowerCAmelCase__	,  atol=1E-4		)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



