






import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import DinatBackbone, DinatForImageClassification, DinatModel
   from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class 					snake_case_							:







   """simple docstring"""



   def __init__(    self:	Optional[int]   ,       snake_case__:	Dict   ,       snake_case__:	Tuple=13   ,       snake_case__:	List[Any]=64   ,       snake_case__:	Dict=4   ,       snake_case__:	Optional[int]=3   ,       snake_case__:	Union[str, Any]=16   ,       snake_case__:	List[str]=[1, 2, 1]   ,       snake_case__:	Tuple=[2, 4, 8]   ,       snake_case__:	Any=3   ,       snake_case__:	Dict=[[3], [1, 2], [1]]   ,       snake_case__:	Union[str, Any]=2.0   ,       snake_case__:	Dict=True   ,       snake_case__:	Tuple=0.0   ,       snake_case__:	int=0.0   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	Optional[int]="gelu"   ,       snake_case__:	List[str]=True   ,       snake_case__:	int=0.02   ,       snake_case__:	List[Any]=1E-5   ,       snake_case__:	List[Any]=True   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	Union[str, Any]=True   ,       snake_case__:	Any=10   ,       snake_case__:	Optional[Any]=["stage1", "stage2"]   ,       snake_case__:	str=[1, 2]   ,       )  ->		Any:


       """simple docstring"""



       _lowercase     =	parent
       _lowercase     =	batch_size
       _lowercase     =	image_size
       _lowercase     =	patch_size
       _lowercase     =	num_channels
       _lowercase     =	embed_dim
       _lowercase     =	depths
       _lowercase     =	num_heads
       _lowercase     =	kernel_size
       _lowercase     =	dilations
       _lowercase     =	mlp_ratio
       _lowercase     =	qkv_bias
       _lowercase     =	hidden_dropout_prob
       _lowercase     =	attention_probs_dropout_prob
       _lowercase     =	drop_path_rate
       _lowercase     =	hidden_act
       _lowercase     =	patch_norm
       _lowercase     =	layer_norm_eps
       _lowercase     =	initializer_range
       _lowercase     =	is_training
       _lowercase     =	scope
       _lowercase     =	use_labels
       _lowercase     =	num_labels
       _lowercase     =	out_features
       _lowercase     =	out_indices

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Dict:


       """simple docstring"""



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

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

       _lowercase     =	self.get_config()

       return config, pixel_values, labels

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


       """simple docstring"""



       return DinatConfig(
           num_labels=self.num_labels   ,       image_size=self.image_size   ,       patch_size=self.patch_size   ,       num_channels=self.num_channels   ,       embed_dim=self.embed_dim   ,       depths=self.depths   ,       num_heads=self.num_heads   ,       kernel_size=self.kernel_size   ,       dilations=self.dilations   ,       mlp_ratio=self.mlp_ratio   ,       qkv_bias=self.qkv_bias   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       drop_path_rate=self.drop_path_rate   ,       hidden_act=self.hidden_act   ,       patch_norm=self.patch_norm   ,       layer_norm_eps=self.layer_norm_eps   ,       initializer_range=self.initializer_range   ,       out_features=self.out_features   ,       out_indices=self.out_indices   ,       )

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


       """simple docstring"""



       _lowercase     =	DinatModel(config=snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	model(snake_case__	)

       _lowercase     =	_lowercase     =	(config.image_size // config.patch_size) // (2 ** (len(config.depths	) - 1))
       _lowercase     =	int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

       self.parent.assertEqual(
           result.last_hidden_state.shape   ,       (self.batch_size, expected_height, expected_width, expected_dim)	)

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


       """simple docstring"""



       _lowercase     =	DinatForImageClassification(snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
       self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)	)

       # test greyscale images
       _lowercase     =	1
       _lowercase     =	DinatForImageClassification(snake_case__	)
       model.to(snake_case__	)
       model.eval()

       _lowercase     =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
       _lowercase     =	model(snake_case__	)
       self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)	)

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


       """simple docstring"""



       _lowercase     =	DinatBackbone(config=snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	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, model.channels[0], 16, 16]	)

       # verify channels
       self.parent.assertEqual(len(model.channels	)   ,       len(config.out_features	)	)

       # verify backbone works with out_features=None
       _lowercase     =	None
       _lowercase     =	DinatBackbone(config=snake_case__	)
       model.to(snake_case__	)
       model.eval()
       _lowercase     =	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, model.channels[-1], 4, 4]	)

       # verify channels
       self.parent.assertEqual(len(model.channels	)   ,       1	)

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


       """simple docstring"""



       _lowercase     =	self.prepare_config_and_inputs()
       _lowercase ,    _lowercase ,    _lowercase     =	config_and_inputs
       _lowercase     =	{'pixel_values': pixel_values}
       return config, inputs_dict






@require_natten
@require_torch
class 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):







   """simple docstring"""



   __lowerCAmelCase      =						(
       (
           DinatModel,
           DinatForImageClassification,
           DinatBackbone,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase      =						(
       {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase      =						False

   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False
   __lowerCAmelCase      =						False

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


       """simple docstring"""



       _lowercase     =	DinatModelTester(self	)
       _lowercase     =	ConfigTester(self   ,       config_class=snake_case__   ,       embed_dim=37	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[str]:


       """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:	List[Any]	)  ->		List[Any]:


       """simple docstring"""



       return

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


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__	)

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


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case__	)

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


       """simple docstring"""



       _lowercase     =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case__	)

   @unittest.skip(reason='Dinat does not use inputs_embeds'	)
   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		List[str]:


       """simple docstring"""



       pass

   @unittest.skip(reason='Dinat does not use feedforward chunking'	)
   def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		str:


       """simple docstring"""



       pass

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Any:


       """simple docstring"""



       _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           _lowercase     =	model_class(snake_case__	)
           self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)	)
           _lowercase     =	model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(snake_case__   ,       nn.Linear	)	)

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


       """simple docstring"""



       _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase     =	['pixel_values']
           self.assertListEqual(arg_names[:1]   ,       snake_case__	)

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


       """simple docstring"""



       self.skipTest('Dinat\'s attention operation is handled entirely by NATTEN.'	)

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


       """simple docstring"""



       _lowercase     =	model_class(snake_case__	)
       model.to(snake_case__	)
       model.eval()

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

       _lowercase     =	outputs.hidden_states

       _lowercase     =	getattr(
           self.model_tester   ,       'expected_num_hidden_layers'   ,       len(self.model_tester.depths	) + 1	)
       self.assertEqual(len(snake_case__	)   ,       snake_case__	)

       # Dinat has a different seq_length
       _lowercase     =	(
           config.patch_size
           if isinstance(config.patch_size   ,       collections.abc.Iterable	)
           else (config.patch_size, config.patch_size)
       )

       _lowercase     =	image_size[0] // patch_size[0]
       _lowercase     =	image_size[1] // patch_size[1]

       self.assertListEqual(
           list(hidden_states[0].shape[-3:]	)   ,       [height, width, self.model_tester.embed_dim]   ,       )

       if model_class.__name__ != "DinatBackbone":
           _lowercase     =	outputs.reshaped_hidden_states
           self.assertEqual(len(snake_case__	)   ,       snake_case__	)

           _lowercase ,    _lowercase ,    _lowercase ,    _lowercase     =	reshaped_hidden_states[0].shape
           _lowercase     =	(
               reshaped_hidden_states[0].view(snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__	).permute(0   ,       2   ,       3   ,       1	)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-3:]	)   ,       [height, width, self.model_tester.embed_dim]   ,       )

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


       """simple docstring"""



       _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

       _lowercase     =	(
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size   ,       collections.abc.Iterable	)
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

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

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

           self.check_hidden_states_output(snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__	)

   @slow
   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		int:


       """simple docstring"""



       for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase     =	DinatModel.from_pretrained(snake_case__	)
           self.assertIsNotNone(snake_case__	)

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


       """simple docstring"""



       _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()

       _lowercase     =	_config_zero_init(snake_case__	)
       for model_class in self.all_model_classes:
           _lowercase     =	model_class(config=snake_case__	)
           for name, param in model.named_parameters():
               if "embeddings" not in name and param.requires_grad:
                   self.assertIn(
                       ((param.data.mean() * 1E9).round() / 1E9).item()   ,       [0.0, 1.0]   ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''   ,       )






@require_natten
@require_vision
@require_torch
class 					snake_case_							(       unittest.TestCase      ):







   """simple docstring"""



   @cached_property
   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[int]:


       """simple docstring"""



       return AutoImageProcessor.from_pretrained('shi-labs/dinat-mini-in1k-224'	) if is_vision_available() else None

   @slow
   def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Tuple:


       """simple docstring"""



       _lowercase     =	DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224'	).to(snake_case__	)
       _lowercase     =	self.default_image_processor

       _lowercase     =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
       _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)

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

       # verify the logits
       _lowercase     =	torch.Size((1, 1_000)	)
       self.assertEqual(outputs.logits.shape   ,       snake_case__	)
       _lowercase     =	torch.tensor([-0.15_45, -0.76_67, 0.46_42]	).to(snake_case__	)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,       snake_case__   ,       atol=1E-4	)	)






@require_torch
@require_natten
class 					snake_case_							(       unittest.TestCase      ,			lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						(DinatBackbone,) if is_torch_available() else ()
   __lowerCAmelCase      =						DinatConfig

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


       """simple docstring"""



       _lowercase     =	DinatModelTester(self	)











from typing import List, Union

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


lowerCamelCase										=  logging.get_logger(__name__)

lowerCamelCase										=  {
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}

class 					snake_case_							(       lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						"""led"""
   __lowerCAmelCase      =						{
       """num_attention_heads""": """encoder_attention_heads""",
       """hidden_size""": """d_model""",
       """attention_probs_dropout_prob""": """attention_dropout""",
       """initializer_range""": """init_std""",
   }

   def __init__(    self:	int   ,       snake_case__:	str=50_265   ,       snake_case__:	Dict=16_384   ,       snake_case__:	str=1_024   ,       snake_case__:	Tuple=12   ,       snake_case__:	int=4_096   ,       snake_case__:	Union[str, Any]=16   ,       snake_case__:	int=12   ,       snake_case__:	Union[str, Any]=4_096   ,       snake_case__:	Optional[Any]=16   ,       snake_case__:	str=0.0   ,       snake_case__:	Optional[Any]=0.0   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]="gelu"   ,       snake_case__:	Optional[int]=1_024   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=0.0   ,       snake_case__:	List[str]=0.0   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=2   ,       snake_case__:	int=0.0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	Tuple=0   ,       snake_case__:	Union[str, Any]=2   ,       snake_case__:	Union[List[int], int] = 512   ,       **snake_case__:	int   ,       )  ->		List[Any]:


       """simple docstring"""



       _lowercase     =	vocab_size
       _lowercase     =	max_encoder_position_embeddings
       _lowercase     =	max_decoder_position_embeddings
       _lowercase     =	d_model
       _lowercase     =	encoder_ffn_dim
       _lowercase     =	encoder_layers
       _lowercase     =	encoder_attention_heads
       _lowercase     =	decoder_ffn_dim
       _lowercase     =	decoder_layers
       _lowercase     =	decoder_attention_heads
       _lowercase     =	dropout
       _lowercase     =	attention_dropout
       _lowercase     =	activation_dropout
       _lowercase     =	activation_function
       _lowercase     =	init_std
       _lowercase     =	encoder_layerdrop
       _lowercase     =	decoder_layerdrop
       _lowercase     =	classifier_dropout
       _lowercase     =	use_cache
       _lowercase     =	encoder_layers
       _lowercase     =	attention_window

       super().__init__(
           pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__   ,       is_encoder_decoder=snake_case__   ,       decoder_start_token_id=snake_case__   ,       **snake_case__   ,       )
