



import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
    )
    from transformers.models.efficientformer.modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )


if is_vision_available():
    from PIL import Image

    from transformers import EfficientFormerImageProcessor



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = 13,			SCREAMING_SNAKE_CASE_ = 64,			SCREAMING_SNAKE_CASE_ = 2,			SCREAMING_SNAKE_CASE_ = 3,			SCREAMING_SNAKE_CASE_ = 3,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = 128,			SCREAMING_SNAKE_CASE_=[16, 32, 64, 128],			SCREAMING_SNAKE_CASE_ = 7,			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_ = 10,			SCREAMING_SNAKE_CASE_ = 0.02,			SCREAMING_SNAKE_CASE_ = 2,			SCREAMING_SNAKE_CASE_ = 1,			SCREAMING_SNAKE_CASE_ = 128,			SCREAMING_SNAKE_CASE_ = [2, 2, 2, 2],			SCREAMING_SNAKE_CASE_ = 2,			SCREAMING_SNAKE_CASE_ = 2,			)     ->		Tuple:
          UpperCamelCase    :  Dict							=	parent
          UpperCamelCase    :  Optional[Any]							=	batch_size
          UpperCamelCase    :  Dict							=	image_size
          UpperCamelCase    :  Union[str, Any]							=	patch_size
          UpperCamelCase    :  List[Any]							=	num_channels
          UpperCamelCase    :  Optional[int]							=	is_training
          UpperCamelCase    :  Optional[int]							=	use_labels
          UpperCamelCase    :  Optional[Any]							=	hidden_size
          UpperCamelCase    :  Optional[Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Optional[int]							=	intermediate_size
          UpperCamelCase    :  Any							=	hidden_act
          UpperCamelCase    :  List[Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	attention_probs_dropout_prob
          UpperCamelCase    :  int							=	type_sequence_label_size
          UpperCamelCase    :  Optional[int]							=	initializer_range
          UpperCamelCase    :  Tuple							=	encoder_stride
          UpperCamelCase    :  List[Any]							=	num_attention_outputs
          UpperCamelCase    :  int							=	embed_dim
          UpperCamelCase    :  Dict							=	embed_dim + 1
          UpperCamelCase    :  List[Any]							=	resolution
          UpperCamelCase    :  List[str]							=	depths
          UpperCamelCase    :  Optional[Any]							=	hidden_sizes
          UpperCamelCase    :  Optional[Any]							=	dim
          UpperCamelCase    :  Any							=	mlp_expansion_ratio
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Optional[Any]							=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          UpperCamelCase    :  Optional[Any]							=	None
          if self.use_labels:
             UpperCamelCase    :  Dict							=	ids_tensor([self.batch_size],			self.type_sequence_label_size       )

          UpperCamelCase    :  str							=	self.get_config()

          return config, pixel_values, labels
       def 					snake_case_       (			self       )     ->		List[str]:
          return EfficientFormerConfig(
              image_size=self.image_size,			patch_size=self.patch_size,			num_channels=self.num_channels,			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,			is_decoder=lowerCAmelCase_,			initializer_range=self.initializer_range,			encoder_stride=self.encoder_stride,			resolution=self.resolution,			depths=self.depths,			hidden_sizes=self.hidden_sizes,			dim=self.dim,			mlp_expansion_ratio=self.mlp_expansion_ratio,			)
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  List[str]							=	TFEfficientFormerModel(config=lowerCAmelCase_       )
          UpperCamelCase    :  List[str]							=	model(lowerCAmelCase_,			training=lowerCAmelCase_       )
          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_       )     ->		Any:
          UpperCamelCase    :  int							=	self.type_sequence_label_size
          UpperCamelCase    :  Optional[int]							=	TFEfficientFormerForImageClassification(lowerCAmelCase_       )
          UpperCamelCase    :  Any							=	model(lowerCAmelCase_,			labels=lowerCAmelCase_,			training=lowerCAmelCase_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)       )

          # test greyscale images
          UpperCamelCase    :  List[Any]							=	1
          UpperCamelCase    :  List[str]							=	TFEfficientFormerForImageClassification(lowerCAmelCase_       )

          UpperCamelCase    :  Tuple							=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
          UpperCamelCase    :  Optional[Any]							=	model(lowerCAmelCase_,			labels=lowerCAmelCase_       )
          self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.type_sequence_label_size)       )






       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  List[str]							=	self.prepare_config_and_inputs()
          UpperCamelCase    :  List[Any]							=	config_and_inputs
          UpperCamelCase    :  str							=	{"pixel_values": pixel_values}
          return config, inputs_dict






@require_tf
class 				lowerCAmelCase_		(  __A       ,    __A       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  (
           (
               TFEfficientFormerModel,
               TFEfficientFormerForImageClassificationWithTeacher,
               TFEfficientFormerForImageClassification,
           )
           if is_tf_available()
           else ()
       )
       UpperCAmelCase__				:					Optional[int]        =  (
           {
               "feature-extraction": TFEfficientFormerModel,
               "image-classification": (
                   TFEfficientFormerForImageClassification,
                   TFEfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_tf_available()
           else {}
       )

       UpperCAmelCase__				:					List[Any]        =  False

       UpperCAmelCase__				:					Dict        =  False
       UpperCAmelCase__				:					Any        =  False
       UpperCAmelCase__				:					Tuple        =  False
       UpperCAmelCase__				:					List[Any]        =  False
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Tuple							=	TFEfficientFormerModelTester(self       )
          UpperCamelCase    :  Dict							=	ConfigTester(
              self,			config_class=lowerCAmelCase_,			has_text_modality=lowerCAmelCase_,			hidden_size=37       )
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          self.config_tester.run_common_tests()
       @unittest.skip(reason='EfficientFormer does not use inputs_embeds'       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          pass
       @unittest.skip(reason='EfficientFormer does not support input and output embeddings'       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          pass
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Union[str, Any]							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[int]							=	model_class(lowerCAmelCase_       )
             UpperCamelCase    :  List[str]							=	inspect.signature(model.call       )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase    :  List[Any]							=	[*signature.parameters.keys()]

             UpperCamelCase    :  Tuple							=	["pixel_values"]
             self.assertListEqual(arg_names[:1],			lowerCAmelCase_       )
       def 					snake_case_       (			self       )     ->		int:
          def check_hidden_states_output(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  List[Any]							=	model_class(lowerCAmelCase_       )

             UpperCamelCase    :  Dict							=	model(**self._prepare_for_class(lowerCAmelCase_,			lowerCAmelCase_       ),			training=lowerCAmelCase_       )
             UpperCamelCase    :  Optional[Any]							=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             UpperCamelCase    :  List[str]							=	getattr(
                 self.model_tester,			'expected_num_hidden_layers',			self.model_tester.num_hidden_layers + 1       )
             self.assertEqual(len(lowerCAmelCase_       ),			lowerCAmelCase_       )

             if hasattr(self.model_tester,			'encoder_seq_length'       ):
                UpperCamelCase    :  Tuple							=	self.model_tester.encoder_seq_length
                if hasattr(self.model_tester,			'chunk_length'       ) and self.model_tester.chunk_length > 1:
                   UpperCamelCase    :  int							=	seq_length * self.model_tester.chunk_length
             else:
                UpperCamelCase    :  List[str]							=	self.model_tester.seq_length

             self.assertListEqual(
                 list(hidden_states[-1].shape[-2:]       ),			[seq_length, self.model_tester.hidden_size],			)

             if config.is_encoder_decoder:
                UpperCamelCase    :  List[str]							=	outputs.decoder_hidden_states

                self.asseretIsInstance(lowerCAmelCase_,			(list, tuple)       )
                self.assertEqual(len(lowerCAmelCase_       ),			lowerCAmelCase_       )
                UpperCamelCase    :  Dict							=	getattr(self.model_tester,			'seq_length',			lowerCAmelCase_       )
                UpperCamelCase    :  List[str]							=	getattr(self.model_tester,			'decoder_seq_length',			lowerCAmelCase_       )

                self.assertListEqual(
                    list(hidden_states[-1].shape[-2:]       ),			[decoder_seq_length, self.model_tester.hidden_size],			)

          UpperCamelCase    :  Any							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             UpperCamelCase    :  str							=	True
             check_hidden_states_output(lowerCAmelCase_,			lowerCAmelCase_,			lowerCAmelCase_       )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCamelCase    :  Optional[int]							=	True

             check_hidden_states_output(lowerCAmelCase_,			lowerCAmelCase_,			lowerCAmelCase_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=False       )     ->		Any:
          UpperCamelCase    :  Any							=	super()._prepare_for_class(lowerCAmelCase_,			lowerCAmelCase_,			return_labels=lowerCAmelCase_       )

          if return_labels:
             if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                del inputs_dict["labels"]

          return inputs_dict
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase_       )
       @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'       )
       def 					snake_case_       (			self       )     ->		Tuple:
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  List[Any]							=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_       )
       @slow
       def 					snake_case_       (			self       )     ->		Optional[int]:
          for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase    :  Dict							=	TFEfficientFormerModel.from_pretrained(lowerCAmelCase_       )
             self.assertIsNotNone(lowerCAmelCase_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  int							=	self.model_tester.prepare_config_and_inputs_for_common()
          UpperCamelCase    :  List[Any]							=	True

          UpperCamelCase    :  Optional[Any]							=	getattr(self.model_tester,			'seq_length',			lowerCAmelCase_       )
          UpperCamelCase    :  Optional[Any]							=	getattr(self.model_tester,			'encoder_seq_length',			lowerCAmelCase_       )
          UpperCamelCase    :  Tuple							=	getattr(self.model_tester,			'key_length',			lowerCAmelCase_       )
          UpperCamelCase    :  Dict							=	getattr(self.model_tester,			'chunk_length',			lowerCAmelCase_       )

          if chunk_length is not None and hasattr(self.model_tester,			'num_hashes'       ):
             UpperCamelCase    :  Tuple							=	encoder_seq_length * self.model_tester.num_hashes

          for model_class in self.all_model_classes:
             UpperCamelCase    :  Optional[Any]							=	True
             UpperCamelCase    :  Optional[int]							=	False
             UpperCamelCase    :  Optional[Any]							=	True
             UpperCamelCase    :  Any							=	model_class(lowerCAmelCase_       )

             UpperCamelCase    :  Union[str, Any]							=	model(**self._prepare_for_class(lowerCAmelCase_,			lowerCAmelCase_       ),			training=lowerCAmelCase_       )
             UpperCamelCase    :  str							=	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(lowerCAmelCase_       ),			self.model_tester.num_attention_outputs       )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCamelCase    :  Any							=	True
             UpperCamelCase    :  Tuple							=	model_class(lowerCAmelCase_       )
             UpperCamelCase    :  Dict							=	model(**self._prepare_for_class(lowerCAmelCase_,			lowerCAmelCase_       ),			training=lowerCAmelCase_       )

             UpperCamelCase    :  int							=	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
             self.assertEqual(len(lowerCAmelCase_       ),			self.model_tester.num_attention_outputs       )

             if chunk_length is not None:
                self.assertListEqual(
                    list(attentions[0].shape[-4:]       ),			[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],			)
             else:
                self.assertListEqual(
                    list(attentions[0].shape[-3:]       ),			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],			)






       def 					snake_case_       (			self       )     ->		Optional[Any]:
          # We use a simplified version of this test for EfficientFormer because it requires training=False
          # and Keras refuses to let us force that during functional construction
          UpperCamelCase    :  Tuple							=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             # Prepare our model
             UpperCamelCase    :  List[Any]							=	model_class(lowerCAmelCase_       )
             # These are maximally general inputs for the model, with multiple None dimensions
             # Hopefully this will catch any conditionals that fail for flexible shapes
             UpperCamelCase    :  str							=	{
                 key: tf.keras.Input(shape=val.shape[1:],			dtype=val.dtype,			name=lowerCAmelCase_       )
                 for key, val in model.input_signature.items()
                 if key in model.dummy_inputs
             }
             UpperCamelCase    :  List[Any]							=	model(lowerCAmelCase_       )
             self.assertTrue(outputs_dict is not None       )






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Union[str, Any]							=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
   return image



@require_tf
@require_vision
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @cached_property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return (
              EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'       )
              if is_vision_available()
              else None
          )
       @slow
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[int]							=	TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'       )
          UpperCamelCase    :  Optional[int]							=	self.default_image_processor
          UpperCamelCase    :  Optional[int]							=	prepare_img()
          UpperCamelCase    :  Optional[Any]							=	image_processor(images=lowerCAmelCase_,			return_tensors='tf'       )
          # forward pass
          UpperCamelCase    :  Optional[Any]							=	model(**lowerCAmelCase_,			training=lowerCAmelCase_       )
          # verify the logits
          UpperCamelCase    :  Any							=	tf.TensorShape((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			lowerCAmelCase_       )
          UpperCamelCase    :  Union[str, Any]							=	tf.constant([-0.05_55, 0.48_25, -0.08_52]       )
          self.assertTrue(np.allclose(outputs.logits[0, :3],			lowerCAmelCase_,			atol=1e-4       )       )






       @slow
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Optional[Any]							=	TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
              'snap-research/efficientformer-l1-300'       )
          UpperCamelCase    :  int							=	self.default_image_processor
          UpperCamelCase    :  List[str]							=	prepare_img()
          UpperCamelCase    :  List[str]							=	image_processor(images=lowerCAmelCase_,			return_tensors='tf'       )
          # forward pass
          UpperCamelCase    :  Union[str, Any]							=	model(**lowerCAmelCase_,			training=lowerCAmelCase_       )
          # verify the logits
          UpperCamelCase    :  List[Any]							=	tf.TensorShape((1, 1000)       )
          self.assertEqual(outputs.logits.shape,			lowerCAmelCase_       )
          UpperCamelCase    :  List[str]							=	tf.constant([-0.13_12, 0.43_53, -1.04_99]       )
          self.assertTrue(np.allclose(outputs.logits[0, :3],			lowerCAmelCase_,			atol=1e-4       )       )











"""simple docstring"""
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    DebertaForMaskedLM,
			    DebertaForQuestionAnswering,
			    DebertaForSequenceClassification,
			    DebertaForTokenClassification,
			    DebertaModel,
			)
			from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[Any]=13	,	lowerCAmelCase_					: Tuple=7	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: List[str]=99	,	lowerCAmelCase_					: int=32	,	lowerCAmelCase_					: List[str]=5	,	lowerCAmelCase_					: Optional[int]=4	,	lowerCAmelCase_					: str=37	,	lowerCAmelCase_					: List[Any]="gelu"	,	lowerCAmelCase_					: Dict=0.1	,	lowerCAmelCase_					: List[str]=0.1	,	lowerCAmelCase_					: List[Any]=512	,	lowerCAmelCase_					: Optional[int]=16	,	lowerCAmelCase_					: Union[str, Any]=2	,	lowerCAmelCase_					: List[str]=0.0_2	,	lowerCAmelCase_					: List[Any]=False	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: Union[str, Any]="None"	,	lowerCAmelCase_					: List[Any]=3	,	lowerCAmelCase_					: Optional[int]=4	,	lowerCAmelCase_					: int=None	,	)						->			Dict:
				UpperCAmelCase_     : Dict			     =					parent
				UpperCAmelCase_     : Union[str, Any]			     =					batch_size
				UpperCAmelCase_     : Optional[Any]			     =					seq_length
				UpperCAmelCase_     : List[Any]			     =					is_training
				UpperCAmelCase_     : Optional[int]			     =					use_input_mask
				UpperCAmelCase_     : int			     =					use_token_type_ids
				UpperCAmelCase_     : Any			     =					use_labels
				UpperCAmelCase_     : Optional[int]			     =					vocab_size
				UpperCAmelCase_     : Any			     =					hidden_size
				UpperCAmelCase_     : Dict			     =					num_hidden_layers
				UpperCAmelCase_     : List[Any]			     =					num_attention_heads
				UpperCAmelCase_     : List[Any]			     =					intermediate_size
				UpperCAmelCase_     : int			     =					hidden_act
				UpperCAmelCase_     : Optional[Any]			     =					hidden_dropout_prob
				UpperCAmelCase_     : Union[str, Any]			     =					attention_probs_dropout_prob
				UpperCAmelCase_     : Any			     =					max_position_embeddings
				UpperCAmelCase_     : Union[str, Any]			     =					type_vocab_size
				UpperCAmelCase_     : Union[str, Any]			     =					type_sequence_label_size
				UpperCAmelCase_     : Tuple			     =					initializer_range
				UpperCAmelCase_     : int			     =					num_labels
				UpperCAmelCase_     : Optional[Any]			     =					num_choices
				UpperCAmelCase_     : List[str]			     =					relative_attention
				UpperCAmelCase_     : List[Any]			     =					position_biased_input
				UpperCAmelCase_     : Dict			     =					pos_att_type
				UpperCAmelCase_     : Optional[Any]			     =					scope





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Dict:
				UpperCAmelCase_     : Any			     =					ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size						)

				UpperCAmelCase_     : Tuple			     =					None
				if self.use_input_mask:
							UpperCAmelCase_     : List[str]			     =					ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2						)

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

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

				UpperCAmelCase_     : int			     =					self.get_config()

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Dict:
				return DebertaConfig(
				    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	,	relative_attention=self.relative_attention	,	position_biased_input=self.position_biased_input	,	pos_att_type=self.pos_att_type	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Union[str, Any]:
				UpperCAmelCase_     : List[str]			     =					self.get_config()
				UpperCAmelCase_     : int			     =					300
				return config





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: int						)						->			List[Any]:
				self.parent.assertListEqual(list(result.loss.size()						)	,	[]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: List[Any]						)						->			List[Any]:
				UpperCAmelCase_     : Optional[Any]			     =					DebertaModel(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_						)[0]
				UpperCAmelCase_     : Optional[int]			     =					model(lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_						)[0]
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_						)[0]

				self.parent.assertListEqual(list(sequence_output.size()						)	,	[self.batch_size, self.seq_length, self.hidden_size]						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Optional[int]						)						->			List[Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					DebertaForMaskedLM(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : List[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_	,	labels=lowerCAmelCase_						)

				self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[Any]						)						->			Optional[Any]:
				UpperCAmelCase_     : Any			     =					self.num_labels
				UpperCAmelCase_     : List[Any]			     =					DebertaForSequenceClassification(lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_	,	labels=lowerCAmelCase_						)
				self.parent.assertListEqual(list(result.logits.size()						)	,	[self.batch_size, self.num_labels]						)
				self.check_loss_output(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: Optional[Any]	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple						)						->			str:
				UpperCAmelCase_     : Optional[int]			     =					self.num_labels
				UpperCAmelCase_     : Optional[int]			     =					DebertaForTokenClassification(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_	,	labels=lowerCAmelCase_						)
				self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.num_labels)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: str						)						->			List[Any]:
				UpperCAmelCase_     : Dict			     =					DebertaForQuestionAnswering(config=lowerCAmelCase_						)
				model.to(lowerCAmelCase_						)
				model.eval()
				UpperCAmelCase_     : Any			     =					model(
				    lowerCAmelCase_	,	attention_mask=lowerCAmelCase_	,	token_type_ids=lowerCAmelCase_	,	start_positions=lowerCAmelCase_	,	end_positions=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      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	(
				    UpperCAmelCase_
				)     ,	
				)     : Tuple			     =					config_and_inputs
				UpperCAmelCase_     : List[Any]			     =					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict







@require_torch
class 			UpperCamelCase_       (__A				,						__A				,						unittest.TestCase     ):
	__magic_name__	      =				(
	    (
	        DebertaModel,
	        DebertaForMaskedLM,
	        DebertaForSequenceClassification,
	        DebertaForTokenClassification,
	        DebertaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	__magic_name__	      =				(
	    {
	        '''feature-extraction''': DebertaModel,
	        '''fill-mask''': DebertaForMaskedLM,
	        '''question-answering''': DebertaForQuestionAnswering,
	        '''text-classification''': DebertaForSequenceClassification,
	        '''token-classification''': DebertaForTokenClassification,
	        '''zero-shot''': DebertaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__	      =				True
	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False
	__magic_name__	      =				False





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			int:
				UpperCAmelCase_     : int			     =					DebertaModelTester(self						)
				UpperCAmelCase_     : Any			     =					ConfigTester(self	,	config_class=lowerCAmelCase_	,	hidden_size=37						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Optional[Any]:
				self.config_tester.run_common_tests()





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Optional[Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				UpperCAmelCase_     : int			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Optional[int]:
				UpperCAmelCase_     : Any			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Union[str, Any]:
				UpperCAmelCase_     : Tuple			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Any			     =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Tuple:
				for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_     : Optional[int]			     =					DebertaModel.from_pretrained(lowerCAmelCase_						)
							self.assertIsNotNone(lowerCAmelCase_						)







@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase_       (unittest.TestCase     ):





	@unittest.skip(reason="Model not available yet"						)
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Optional[Any]:
				pass





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			List[Any]:
				UpperCAmelCase_     : Optional[int]			     =					DebertaModel.from_pretrained("microsoft/deberta-base"						)

				UpperCAmelCase_     : List[Any]			     =					torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
				UpperCAmelCase_     : Tuple			     =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
				with torch.no_grad():
							UpperCAmelCase_     : Optional[Any]			     =					model(lowerCAmelCase_	,	attention_mask=lowerCAmelCase_						)[0]
				# compare the actual values for a slice.
				UpperCAmelCase_     : Tuple			     =					torch.tensor(
				    [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]						)
				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,	lowerCAmelCase_	,	atol=1e-4						)	,	f"""{output[:, 1:4, 1:4]}"""						)



