


'''simple docstring'''







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


_SCREAMING_SNAKE_CASE							: int										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							: Any										=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}






class 		_snake_case     (					A_		):






						'''simple docstring'''







						__snake_case				=   "mgp-str"







						def __init__(	self:						Dict   ,				__UpperCamelCase:						str=[32, 128]   ,				__UpperCamelCase:						Union[str, Any]=4   ,				__UpperCamelCase:						int=3   ,				__UpperCamelCase:						Tuple=27   ,				__UpperCamelCase:						List[Any]=38   ,				__UpperCamelCase:						List[Any]=5_0257   ,				__UpperCamelCase:						Tuple=3_0522   ,				__UpperCamelCase:						Optional[int]=768   ,				__UpperCamelCase:						Dict=12   ,				__UpperCamelCase:						List[str]=12   ,				__UpperCamelCase:						List[str]=4.0   ,				__UpperCamelCase:						List[str]=True   ,				__UpperCamelCase:						Optional[int]=False   ,				__UpperCamelCase:						Tuple=1E-5   ,				__UpperCamelCase:						Tuple=0.0   ,				__UpperCamelCase:						Optional[Any]=0.0   ,				__UpperCamelCase:						Any=0.0   ,				__UpperCamelCase:						Optional[int]=False   ,				__UpperCamelCase:						Union[str, Any]=0.0_2   ,				**__UpperCamelCase:						Union[str, Any]   ,				)				->						Optional[Any]:
										super().__init__(**snake_case__  )

										__magic_name__					:       Union[str, Any]    							=							image_size
										__magic_name__					:       List[str]    							=							patch_size
										__magic_name__					:       Dict    							=							num_channels
										__magic_name__					:       str    							=							max_token_length
										__magic_name__					:       List[Any]    							=							num_character_labels
										__magic_name__					:       List[Any]    							=							num_bpe_labels
										__magic_name__					:       Tuple    							=							num_wordpiece_labels
										__magic_name__					:       str    							=							hidden_size
										__magic_name__					:       Optional[int]    							=							num_hidden_layers
										__magic_name__					:       Optional[Any]    							=							num_attention_heads
										__magic_name__					:       Any    							=							mlp_ratio
										__magic_name__					:       str    							=							distilled
										__magic_name__					:       Optional[int]    							=							layer_norm_eps
										__magic_name__					:       Any    							=							drop_rate
										__magic_name__					:       List[str]    							=							qkv_bias
										__magic_name__					:       List[Any]    							=							attn_drop_rate
										__magic_name__					:       str    							=							drop_path_rate
										__magic_name__					:       Optional[Any]    							=							output_aa_attentions
										__magic_name__					:       str    							=							initializer_range

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     UpperCAmelCase						:



   def __init__(self						:   int       ,		snake_case__						:   Optional[int]       ,		snake_case__						:   Optional[int]=13       ,		snake_case__						:   Tuple=32       ,		snake_case__						:   Optional[Any]=3       ,		snake_case__						:   Tuple=4       ,		snake_case__						:   List[Any]=[10, 20, 30, 40]       ,		snake_case__						:   Dict=[2, 2, 3, 2]       ,		snake_case__						:   Union[str, Any]=True       ,		snake_case__						:   Union[str, Any]=True       ,		snake_case__						:   List[str]=37       ,		snake_case__						:   List[Any]="gelu"       ,		snake_case__						:   Union[str, Any]=10       ,		snake_case__						:   int=0.02       ,		snake_case__						:   str=["stage2", "stage3", "stage4"]       ,		snake_case__						:   int=[2, 3, 4]       ,		snake_case__						:   List[Any]=None       ,		)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= parent
          snake_case			:  Optional[int]     							= batch_size
          snake_case			:  Optional[Any]     							= image_size
          snake_case			:  Union[str, Any]     							= num_channels
          snake_case			:  Tuple     							= num_stages
          snake_case			:  Union[str, Any]     							= hidden_sizes
          snake_case			:  List[Any]     							= depths
          snake_case			:  Dict     							= is_training
          snake_case			:  List[Any]     							= use_labels
          snake_case			:  str     							= intermediate_size
          snake_case			:  Dict     							= hidden_act
          snake_case			:  Optional[int]     							= num_labels
          snake_case			:  Dict     							= initializer_range
          snake_case			:  List[Any]     							= out_features
          snake_case			:  int     							= out_indices
          snake_case			:  List[Any]     							= scope



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


          '''simple docstring'''


          snake_case			:  List[Any]     							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

          snake_case			:  List[Any]     							= None
          if self.use_labels:
                 snake_case			:  List[Any]     							= ids_tensor([self.batch_size]       ,		self.num_labels					)

          snake_case			:  str     							= self.get_config()

          return config, pixel_values, labels



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      Dict:


          '''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=snake_case__       ,		initializer_range=self.initializer_range       ,		out_features=self.out_features       ,		out_indices=self.out_indices       ,		num_labels=self.num_labels       ,		)



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


          '''simple docstring'''


          snake_case			:  List[str]     							= ConvNextVaModel(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Any     							= model(snake_case__					)
          # 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 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Tuple:


          '''simple docstring'''


          snake_case			:  str     							= ConvNextVaForImageClassification(snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Tuple     							= model(snake_case__       ,		labels=snake_case__					)
          self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Dict       ,		snake_case__						:   int       ,		snake_case__						:   Optional[int]					)						->      int:


          '''simple docstring'''


          snake_case			:  int     							= ConvNextVaBackbone(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  Union[str, Any]     							= model(snake_case__					)

          # 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
          snake_case			:  Dict     							= None
          snake_case			:  Tuple     							= ConvNextVaBackbone(config=snake_case__					)
          model.to(snake_case__					)
          model.eval()
          snake_case			:  int     							= model(snake_case__					)

          # 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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      Union[str, Any]:


          '''simple docstring'''


          snake_case			:  str     							= self.prepare_config_and_inputs()
          snake_case							,   snake_case							,   snake_case			:  Union[str, Any]     							= config_and_inputs
          snake_case			:  int     							= {"pixel_values": pixel_values}
          return config, inputs_dict



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


          '''simple docstring'''


          snake_case			:  Optional[int]     							= self.prepare_config_and_inputs()
          snake_case							,   snake_case							,   snake_case			:  str     							= config_and_inputs
          snake_case			:  Optional[int]     							= {"pixel_values": pixel_values, "labels": labels}
          return config, inputs_dict






@require_torch
class     UpperCAmelCase						(		A_			,A_			,unittest.TestCase					):
   A__   :						Tuple			=	(
       (
           ConvNextVaModel,
           ConvNextVaForImageClassification,
           ConvNextVaBackbone,
       )
       if is_torch_available()
       else ()
   )
   A__   :						int			=	(
       {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__   :						str			=	False
   A__   :						Dict			=	False
   A__   :						Optional[int]			=	False
   A__   :						List[Any]			=	False
   A__   :						str			=	False



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


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= ConvNextVaModelTester(self					)
          snake_case			:  int     							= ConfigTester(self       ,		config_class=snake_case__       ,		has_text_modality=snake_case__       ,		hidden_size=37					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   str					)						->      Tuple:


          '''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 							_SCREAMING_SNAKE_CASE  (self						:   int					)						->      Dict:


          '''simple docstring'''


          return



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          pass



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


          '''simple docstring'''


          if not self.model_tester.is_training:
                 return

          for model_class in self.all_model_classes:
                 snake_case							,   snake_case			:  str     							= self.model_tester.prepare_config_and_inputs_with_labels()
                 snake_case			:  Tuple     							= True

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

                 snake_case			:  Union[str, Any]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.train()
                 snake_case			:  str     							= self._prepare_for_class(snake_case__       ,		snake_case__       ,		return_labels=snake_case__					)
                 snake_case			:  List[str]     							= model(**snake_case__					).loss
                 loss.backward()



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


          '''simple docstring'''


          if not self.model_tester.is_training:
                 return

          for model_class in self.all_model_classes:
                 snake_case							,   snake_case			:  List[str]     							= self.model_tester.prepare_config_and_inputs_with_labels()
                 snake_case			:  List[Any]     							= False
                 snake_case			:  Any     							= True

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

                 snake_case			:  Union[str, Any]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.gradient_checkpointing_enable()
                 model.train()
                 snake_case			:  List[str]     							= self._prepare_for_class(snake_case__       ,		snake_case__       ,		return_labels=snake_case__					)
                 snake_case			:  Any     							= model(**snake_case__					).loss
                 loss.backward()



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


          '''simple docstring'''


          snake_case							,   snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs_for_common()

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

                 snake_case			:  Any     							= ["pixel_values"]
                 self.assertListEqual(arg_names[:1]       ,		snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case__					)



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


          '''simple docstring'''


          def check_hidden_states_output(snake_case__						:   Dict       ,		snake_case__						:   List[Any]       ,		snake_case__						:   Dict					):
                 snake_case			:  List[str]     							= model_class(snake_case__					)
                 model.to(snake_case__					)
                 model.eval()

                 with torch.no_grad():
                        snake_case			:  Any     							= model(**self._prepare_for_class(snake_case__       ,		snake_case__					)					)

                 snake_case			:  List[Any]     							= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 snake_case			:  Dict     							= self.model_tester.num_stages
                 self.assertEqual(len(snake_case__					)       ,		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]       ,		)

          snake_case							,   snake_case			:  int     							= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 snake_case			:  Dict     							= True
                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)

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

                 check_hidden_states_output(snake_case__       ,		snake_case__       ,		snake_case__					)



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


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case__					)



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      List[Any]:


          '''simple docstring'''


          for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 snake_case			:  Optional[int]     							= ConvNextVaModel.from_pretrained(snake_case__					)
                 self.assertIsNotNone(snake_case__					)



def      UpperCamelCase   (       ):
       snake_case			:  Dict     							= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image






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



   @cached_property
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      Any:


          '''simple docstring'''


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



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple					)						->      List[Any]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= ConvNextVaForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224"					).to(snake_case__					)

          snake_case			:  List[str]     							= self.default_image_processor
          snake_case			:  str     							= prepare_img()
          snake_case			:  List[str]     							= preprocessor(images=snake_case__       ,		return_tensors="pt"					).to(snake_case__					)

          # forward pass
          with torch.no_grad():
                 snake_case			:  str     							= model(**snake_case__					)

          # verify the logits
          snake_case			:  Any     							= torch.Size((1, 10_00)					)
          self.assertEqual(outputs.logits.shape       ,		snake_case__					)

          snake_case			:  str     							= torch.tensor([0.9996, 0.1966, -0.4386]					).to(snake_case__					)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		snake_case__       ,		atol=1e-4					)					)





