

import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         NystromformerForMaskedLM,
         NystromformerForMultipleChoice,
         NystromformerForQuestionAnswering,
         NystromformerForSequenceClassification,
         NystromformerForTokenClassification,
         NystromformerModel,
     )
     from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST



class 		_lowercase		:

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       Union[str, Any]=13							,   __lowerCAmelCase			:       Optional[int]=7							,   __lowerCAmelCase			:       Tuple=True							,   __lowerCAmelCase			:       Tuple=True							,   __lowerCAmelCase			:       Union[str, Any]=True							,   __lowerCAmelCase			:       Optional[int]=True							,   __lowerCAmelCase			:       List[Any]=99							,   __lowerCAmelCase			:       str=32							,   __lowerCAmelCase			:       List[str]=5							,   __lowerCAmelCase			:       List[str]=4							,   __lowerCAmelCase			:       List[str]=37							,   __lowerCAmelCase			:       int="gelu"							,   __lowerCAmelCase			:       Any=0.1							,   __lowerCAmelCase			:       List[str]=0.1							,   __lowerCAmelCase			:       List[str]=512							,   __lowerCAmelCase			:       Any=16							,   __lowerCAmelCase			:       Any=2							,   __lowerCAmelCase			:       Dict=0.0_2							,   __lowerCAmelCase			:       Dict=3							,   __lowerCAmelCase			:       List[str]=4							,   __lowerCAmelCase			:       List[Any]=None							,   )       ->				Any:


    """simple docstring"""






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

   def 		A							(      self			:       int							)       ->				Dict:


    """simple docstring"""






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

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

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

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

    a						=		self.get_config()

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

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


    """simple docstring"""






    return NystromformerConfig(
        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							,   is_decoder=__lowerCAmelCase							,   initializer_range=self.initializer_range							,   )

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       List[str]							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		NystromformerModel(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							,   attention_mask=__lowerCAmelCase							,   token_type_ids=__lowerCAmelCase							)
    a						=		model(__lowerCAmelCase							,   token_type_ids=__lowerCAmelCase							)
    a						=		model(__lowerCAmelCase							)
    self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)							)

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       Optional[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		NystromformerForMaskedLM(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		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 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		NystromformerForQuestionAnswering(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		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 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[Any]							)       ->				str:


    """simple docstring"""






    a						=		self.num_labels
    a						=		NystromformerForSequenceClassification(__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							,   attention_mask=__lowerCAmelCase							,   token_type_ids=__lowerCAmelCase							,   labels=__lowerCAmelCase							)
    self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       str							)       ->				List[str]:


    """simple docstring"""






    a						=		self.num_labels
    a						=		NystromformerForTokenClassification(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		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 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       List[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Dict							)       ->				str:


    """simple docstring"""






    a						=		self.num_choices
    a						=		NystromformerForMultipleChoice(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		input_ids.unsqueeze(1							).expand(-1							,   self.num_choices							,   -1							).contiguous()
    a						=		token_type_ids.unsqueeze(1							).expand(-1							,   self.num_choices							,   -1							).contiguous()
    a						=		input_mask.unsqueeze(1							).expand(-1							,   self.num_choices							,   -1							).contiguous()
    a						=		model(
        __lowerCAmelCase							,   attention_mask=__lowerCAmelCase							,   token_type_ids=__lowerCAmelCase							,   labels=__lowerCAmelCase							,   )
    self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_choices)							)




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


    """simple docstring"""






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



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(
       (
           NystromformerModel,
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase     	=			(
       {
           '''feature-extraction''': NystromformerModel,
           '''fill-mask''': NystromformerForMaskedLM,
           '''question-answering''': NystromformerForQuestionAnswering,
           '''text-classification''': NystromformerForSequenceClassification,
           '''token-classification''': NystromformerForTokenClassification,
           '''zero-shot''': NystromformerForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False

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


    """simple docstring"""






    a						=		NystromformerModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__lowerCAmelCase							,   hidden_size=37							)

   def 		A							(      self			:       int							)       ->				str:


    """simple docstring"""






    self.config_tester.run_common_tests()

   def 		A							(      self			:       str							)       ->				List[str]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				Dict:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     a						=		type
     self.model_tester.create_and_check_model(*__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase							)

   def 		A							(      self			:       str							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase							)

   def 		A							(      self			:       Union[str, Any]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase							)

   def 		A							(      self			:       Any							)       ->				str:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase							)

   def 		A							(      self			:       List[str]							)       ->				Tuple:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase							)




   @slow
   def 		A							(      self			:       Optional[Any]							)       ->				Dict:


    """simple docstring"""






    for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a						=		NystromformerModel.from_pretrained(__lowerCAmelCase							)
     self.assertIsNotNone(__lowerCAmelCase							)



@require_torch
class 		_lowercase		(       unittest.TestCase	):

   @slow
   def 		A							(      self			:       int							)       ->				Optional[int]:


    """simple docstring"""






    a						=		NystromformerModel.from_pretrained("uw-madison/nystromformer-512"							)
    a						=		torch.tensor([[0, 1, 2, 3, 4, 5]]							)

    with torch.no_grad():
     a						=		model(__lowerCAmelCase							)[0]

    a						=		torch.Size((1, 6, 768)							)
    self.assertEqual(output.shape							,   __lowerCAmelCase							)

    a						=		torch.tensor(
        [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]							)

    self.assertTrue(torch.allclose(output[:, :3, :3]							,   __lowerCAmelCase							,   atol=1E-4							)							)




   @slow
   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		"the [MASK] of Belgium is Brussels"

    a						=		AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"							)
    a						=		NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"							)

    a						=		tokenizer(__lowerCAmelCase							,   return_tensors="pt"							)

    with torch.no_grad():
     a						=		model(encoding.input_ids							).logits

    a						=		token_logits[:, 2, :].argmax(-1							)[0]

    self.assertEqual(tokenizer.decode(__lowerCAmelCase							)							,   "capital"							)






import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch

     from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
     from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[int]							,   __lowerCAmelCase			:       Optional[int]=13							,   __lowerCAmelCase			:       str=32							,   __lowerCAmelCase			:       str=3							,   __lowerCAmelCase			:       int=4							,   __lowerCAmelCase			:       List[str]=[10, 20, 30, 40]							,   __lowerCAmelCase			:       Any=[2, 2, 3, 2]							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       int=True							,   __lowerCAmelCase			:       str=37							,   __lowerCAmelCase			:       List[Any]="gelu"							,   __lowerCAmelCase			:       int=10							,   __lowerCAmelCase			:       str=0.0_2							,   __lowerCAmelCase			:       int=["stage2", "stage3", "stage4"]							,   __lowerCAmelCase			:       List[str]=[2, 3, 4]							,   __lowerCAmelCase			:       str=None							,   )       ->				Optional[Any]:


    """simple docstring"""






    a						=		parent
    a						=		batch_size
    a						=		image_size
    a						=		num_channels
    a						=		num_stages
    a						=		hidden_sizes
    a						=		depths
    a						=		is_training
    a						=		use_labels
    a						=		intermediate_size
    a						=		hidden_act
    a						=		num_labels
    a						=		initializer_range
    a						=		out_features
    a						=		out_indices
    a						=		scope

   def 		A							(      self			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

    a						=		None
    if self.use_labels:
     a						=		ids_tensor([self.batch_size]							,   self.num_labels							)

    a						=		self.get_config()

    return config, pixel_values, labels

   def 		A							(      self			:       Union[str, Any]							)       ->				Optional[int]:


    """simple docstring"""






    return ConvNextVaConfig(
        num_channels=self.num_channels							,   hidden_sizes=self.hidden_sizes							,   depths=self.depths							,   num_stages=self.num_stages							,   hidden_act=self.hidden_act							,   is_decoder=__lowerCAmelCase							,   initializer_range=self.initializer_range							,   out_features=self.out_features							,   out_indices=self.out_indices							,   num_labels=self.num_labels							,   )

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		ConvNextVaModel(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape							,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,   )

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							)       ->				Dict:


    """simple docstring"""






    a						=		ConvNextVaForImageClassification(__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							,   labels=__lowerCAmelCase							)
    self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.num_labels)							)

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[Any]							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		ConvNextVaBackbone(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)

    # verify hidden states
    self.parent.assertEqual(len(result.feature_maps							)							,   len(config.out_features							)							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,   [self.batch_size, self.hidden_sizes[1], 4, 4]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)							,   len(config.out_features							)							)
    self.parent.assertListEqual(model.channels							,   config.hidden_sizes[1:]							)

    # verify backbone works with out_features=None
    a						=		None
    a						=		ConvNextVaBackbone(config=__lowerCAmelCase							)
    model.to(__lowerCAmelCase							)
    model.eval()
    a						=		model(__lowerCAmelCase							)

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps							)							,   1							)
    self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,   [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

    # verify channels
    self.parent.assertEqual(len(model.channels							)							,   1							)
    self.parent.assertListEqual(model.channels							,   [config.hidden_sizes[-1]]							)

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


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a		,  a		,  a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values}
    return config, inputs_dict




   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






    a						=		self.prepare_config_and_inputs()
    a		,  a		,  a						=		config_and_inputs
    a						=		{"pixel_values": pixel_values, "labels": labels}
    return config, inputs_dict



@require_torch
class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			(
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase     	=			(
       {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False
   _UpperCAmelCase     	=			False

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		ConvNextVaModelTester(self							)
    a						=		ConfigTester(self							,   config_class=__lowerCAmelCase							,   has_text_modality=__lowerCAmelCase							,   hidden_size=37							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    return

   @unittest.skip(reason="ConvNextV2 does not use inputs_embeds"							)
   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="ConvNextV2 does not support input and output embeddings"							)
   def 		A							(      self			:       int							)       ->				List[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="ConvNextV2 does not use feedforward chunking"							)
   def 		A							(      self			:       Optional[int]							)       ->				Dict:


    """simple docstring"""






    pass

   def 		A							(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    for model_class in self.all_model_classes:
     a		,  a						=		self.model_tester.prepare_config_and_inputs_with_labels()
     a						=		True

     if model_class.__name__ in [
         *get_values(__lowerCAmelCase							),
         *get_values(__lowerCAmelCase							),
     ]:
      continue

     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.train()
     a						=		self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							,   return_labels=__lowerCAmelCase							)
     a						=		model(**__lowerCAmelCase							).loss
     loss.backward()

   def 		A							(      self			:       Optional[int]							)       ->				List[str]:


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    for model_class in self.all_model_classes:
     a		,  a						=		self.model_tester.prepare_config_and_inputs_with_labels()
     a						=		False
     a						=		True

     if (
         model_class.__name__
         in [*get_values(__lowerCAmelCase							), *get_values(__lowerCAmelCase							)]
         or not model_class.supports_gradient_checkpointing
     ):
      continue

     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.gradient_checkpointing_enable()
     model.train()
     a						=		self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							,   return_labels=__lowerCAmelCase							)
     a						=		model(**__lowerCAmelCase							).loss
     loss.backward()

   def 		A							(      self			:       List[Any]							)       ->				Any:


    """simple docstring"""






    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a						=		model_class(__lowerCAmelCase							)
     a						=		inspect.signature(model.forward							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     a						=		[*signature.parameters.keys()]

     a						=		["pixel_values"]
     self.assertListEqual(arg_names[:1]							,   __lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    def check_hidden_states_output(__lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Tuple							):
     a						=		model_class(__lowerCAmelCase							)
     model.to(__lowerCAmelCase							)
     model.eval()

     with torch.no_grad():
      a						=		model(**self._prepare_for_class(__lowerCAmelCase							,   __lowerCAmelCase							)							)

     a						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

     a						=		self.model_tester.num_stages
     self.assertEqual(len(__lowerCAmelCase							)							,   expected_num_stages + 1							)

     # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]							)							,   [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,   )

    a		,  a						=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a						=		True
     check_hidden_states_output(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     a						=		True

     check_hidden_states_output(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							)

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


    """simple docstring"""






    a						=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)




   @slow
   def 		A							(      self			:       Tuple							)       ->				List[str]:


    """simple docstring"""






    for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a						=		ConvNextVaModel.from_pretrained(__lowerCAmelCase							)
     self.assertIsNotNone(__lowerCAmelCase							)


def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image




@require_torch
@require_vision
class 		_lowercase		(       unittest.TestCase	):

   @cached_property
   def 		A							(      self			:       Optional[int]							)       ->				str:


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224"							) if is_vision_available() else None




   @slow
   def 		A							(      self			:       List[str]							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"							).to(__lowerCAmelCase							)

    a						=		self.default_image_processor
    a						=		prepare_img()
    a						=		preprocessor(images=__lowerCAmelCase							,   return_tensors="pt"							).to(__lowerCAmelCase							)

    # forward pass
    with torch.no_grad():
     a						=		model(**__lowerCAmelCase							)

    # verify the logits
    a						=		torch.Size((1, 1000)							)
    self.assertEqual(outputs.logits.shape							,   __lowerCAmelCase							)

    a						=		torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6]							).to(__lowerCAmelCase							)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]							,   __lowerCAmelCase							,   atol=1E-4							)							)

