


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   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=7				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=99				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=37				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=512				,       _SCREAMING_SNAKE_CASE=16				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE="None"				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=None				,       )->Dict:




         '''simple docstring'''





         A_      :				int		     =					parent
         A_      :				Union[str, Any]		     =					batch_size
         A_      :				List[Any]		     =					seq_length
         A_      :				Dict		     =					is_training
         A_      :				Optional[Any]		     =					use_input_mask
         A_      :				int		     =					use_token_type_ids
         A_      :				Optional[int]		     =					use_labels
         A_      :				Union[str, Any]		     =					vocab_size
         A_      :				Tuple		     =					hidden_size
         A_      :				List[str]		     =					num_hidden_layers
         A_      :				str		     =					num_attention_heads
         A_      :				Optional[Any]		     =					intermediate_size
         A_      :				int		     =					hidden_act
         A_      :				Dict		     =					hidden_dropout_prob
         A_      :				Optional[Any]		     =					attention_probs_dropout_prob
         A_      :				Dict		     =					max_position_embeddings
         A_      :				Dict		     =					type_vocab_size
         A_      :				Optional[int]		     =					type_sequence_label_size
         A_      :				int		     =					initializer_range
         A_      :				int		     =					num_labels
         A_      :				Dict		     =					num_choices
         A_      :				int		     =					relative_attention
         A_      :				List[str]		     =					position_biased_input
         A_      :				List[Any]		     =					pos_att_type
         A_      :				List[str]		     =					scope



       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				int		     =					ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size		)

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

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

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

         A_      :				Optional[Any]		     =					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=_SCREAMING_SNAKE_CASE				,       )

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Dict:




         '''simple docstring'''





         A_      :				Dict		     =					TFDebertaVaModel(config=_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A_      :				str		     =					[input_ids, input_mask]
         A_      :				Optional[Any]		     =					model(_SCREAMING_SNAKE_CASE		)

         A_      :				Tuple		     =					model(_SCREAMING_SNAKE_CASE		)

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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Any:




         '''simple docstring'''





         A_      :				Dict		     =					TFDebertaVaForMaskedLM(config=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_      :				Tuple		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					self.num_labels
         A_      :				int		     =					TFDebertaVaForSequenceClassification(config=_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_      :				Dict		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				str		     =					self.num_labels
         A_      :				List[Any]		     =					TFDebertaVaForTokenClassification(config=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_      :				List[Any]		     =					model(_SCREAMING_SNAKE_CASE		)
         self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





         A_      :				List[Any]		     =					TFDebertaVaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_      :				int		     =					model(_SCREAMING_SNAKE_CASE		)
         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        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.prepare_config_and_inputs()
         (
             (
             A_
         )     ,       (
             A_
         )     ,       (
             A_
         )     ,       (
             A_
         )     ,       (
             A_
         )     ,       (
             A_
         )     ,       (
             A_
         )     ,       
         )      :				str		     =					config_and_inputs
         A_      :				Optional[int]		     =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict






@require_tf
class   _lowerCamelCase (					UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (
           (
               TFDebertaVaModel,
               TFDebertaVaForMaskedLM,
               TFDebertaVaForQuestionAnswering,
               TFDebertaVaForSequenceClassification,
               TFDebertaVaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       snake_case      =    (
           {
               "feature-extraction": TFDebertaVaModel,
               "fill-mask": TFDebertaVaForMaskedLM,
               "question-answering": TFDebertaVaForQuestionAnswering,
               "text-classification": TFDebertaVaForSequenceClassification,
               "token-classification": TFDebertaVaForTokenClassification,
               "zero-shot": TFDebertaVaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       snake_case      =    False
       snake_case      =    False



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				Dict		     =					TFDebertaVaModelTester(self		)
         A_      :				Optional[int]		     =					ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE				,       hidden_size=37		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         self.config_tester.run_common_tests()



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Tuple		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE		)



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




         '''simple docstring'''





         A_      :				List[Any]		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				Dict		     =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE		)







       @slow
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				List[Any]		     =					TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''		)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)






@require_tf
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @unittest.skip(reason='''Model not available yet'''		)
       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         pass







       @slow
       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_      :				List[str]		     =					TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''		)
         A_      :				Union[str, Any]		     =					tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
         A_      :				int		     =					tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
         A_      :				str		     =					model(_SCREAMING_SNAKE_CASE				,       attention_mask=_SCREAMING_SNAKE_CASE		)[0]

         A_      :				List[str]		     =					tf.constant(
             [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]		)
         tf.debugging.assert_near(output[:, 1:4, 1:4]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-4		)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """vinvino02/glpn-kitti""": """https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json""",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




