



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__   :					Optional[Any] 						=      logging.get_logger(__name__)

a__   :					int 						=      {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     "roformer"


   def __init__(    self	:      List[str]      ,					lowerCAmelCase	:      List[Any]=5_00_00      ,					lowerCAmelCase	:      Tuple=None      ,					lowerCAmelCase	:      Any=7_68      ,					lowerCAmelCase	:      Union[str, Any]=12      ,					lowerCAmelCase	:      int=12      ,					lowerCAmelCase	:      Any=30_72      ,					lowerCAmelCase	:      Any="gelu"      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      Tuple=0.1      ,					lowerCAmelCase	:      Optional[int]=15_36      ,					lowerCAmelCase	:      Union[str, Any]=2      ,					lowerCAmelCase	:      List[str]=0.02      ,					lowerCAmelCase	:      Optional[Any]=1E-1_2      ,					lowerCAmelCase	:      str=0      ,					lowerCAmelCase	:      Optional[int]=False      ,					lowerCAmelCase	:      Any=True      ,					**lowerCAmelCase	:      List[str]      ,					)						->					List[str]:




         """simple docstring"""

         super().__init__(pad_token_id=lowerCAmelCase      ,					**lowerCAmelCase)

         lowercase__  =	vocab_size
         lowercase__  =	hidden_size if embedding_size is None else embedding_size
         lowercase__  =	hidden_size
         lowercase__  =	num_hidden_layers
         lowercase__  =	num_attention_heads
         lowercase__  =	hidden_act
         lowercase__  =	intermediate_size
         lowercase__  =	hidden_dropout_prob
         lowercase__  =	attention_probs_dropout_prob
         lowercase__  =	max_position_embeddings
         lowercase__  =	type_vocab_size
         lowercase__  =	initializer_range
         lowercase__  =	layer_norm_eps
         lowercase__  =	rotary_value
         lowercase__  =	use_cache



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   @property
   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         if self.task == "multiple-choice":
               lowercase__  =	{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
               lowercase__  =	{0: 'batch', 1: 'sequence'}
         lowercase__  =	{0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ])





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _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 ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
       from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      str      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Optional[int]=13      ,					lowerCAmelCase	:      Optional[int]=64      ,					lowerCAmelCase	:      List[Any]=2      ,					lowerCAmelCase	:      int=3      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Optional[int]=True      ,					lowerCAmelCase	:      Tuple=32      ,					lowerCAmelCase	:      Union[str, Any]=5      ,					lowerCAmelCase	:      Dict=4      ,					lowerCAmelCase	:      Any=37      ,					lowerCAmelCase	:      Tuple="gelu"      ,					lowerCAmelCase	:      int=0.1      ,					lowerCAmelCase	:      Optional[int]=0.1      ,					lowerCAmelCase	:      Optional[Any]=10      ,					lowerCAmelCase	:      List[str]=0.02      ,					lowerCAmelCase	:      Optional[int]=[1, 16, 4, 4]      ,					lowerCAmelCase	:      Optional[int]=None      ,					)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	batch_size
         lowercase__  =	image_size
         lowercase__  =	patch_size
         lowercase__  =	num_channels
         lowercase__  =	is_training
         lowercase__  =	use_labels
         lowercase__  =	hidden_size
         lowercase__  =	num_hidden_layers
         lowercase__  =	num_attention_heads
         lowercase__  =	intermediate_size
         lowercase__  =	hidden_act
         lowercase__  =	hidden_dropout_prob
         lowercase__  =	attention_probs_dropout_prob
         lowercase__  =	type_sequence_label_size
         lowercase__  =	initializer_range
         lowercase__  =	scope
         lowercase__  =	backbone_featmap_shape

         # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         # the number of patches is based on the feature map of the backbone, which by default uses an output stride
         # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
         lowercase__  =	(self.image_size // 32) ** 2
         lowercase__  =	num_patches + 1


   def       UpperCAmelCase		(    self	:      Dict)						->					Any:




         """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.type_sequence_label_size)

         lowercase__  =	self.get_config()

         return config, pixel_values, labels


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




         """simple docstring"""

         lowercase__  =	{
             'global_padding': 'same',
             'layer_type': 'bottleneck',
             'depths': [3, 4, 9],
             'out_features': ['stage1', 'stage2', 'stage3'],
             'embedding_dynamic_padding': True,
             'hidden_sizes': [4, 8, 16, 32],
             'num_groups': 2,
         }

         return ViTHybridConfig(
             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      ,					backbone_featmap_shape=self.backbone_featmap_shape      ,					backbone_config=lowerCAmelCase      ,					)


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




         """simple docstring"""

         lowercase__  =	ViTHybridModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size))


   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      int      ,					lowerCAmelCase	:      List[str])						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.type_sequence_label_size
         lowercase__  =	ViTHybridForImageClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__  =	model(lowerCAmelCase      ,					labels=lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.type_sequence_label_size))



   def       UpperCAmelCase		(    self	:      str)						->					Optional[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_torch
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Any												=     (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   A     :	int												=     (
       {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   A     :	Tuple												=     False
   A     :	Tuple												=     False
   A     :	Union[str, Any]												=     False


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




         """simple docstring"""

         lowercase__  =	ViTHybridModelTester(self)
         lowercase__  =	ConfigTester(self      ,					config_class=lowerCAmelCase      ,					has_text_modality=lowerCAmelCase      ,					hidden_size=37)


   def       UpperCAmelCase		(    self	:      Dict)						->					Any:




         """simple docstring"""

         self.config_tester.run_common_tests()


   @unittest.skip(reason='ViT does not use inputs_embeds')
   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         pass


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, 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(lowerCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()      ,					(nn.Module))
               lowercase__  =	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase      ,					nn.Linear))


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[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(lowerCAmelCase)
               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]      ,					lowerCAmelCase)


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




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


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




         """simple docstring"""

         lowercase__  =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      str)						->					Tuple:




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__  =	_config_zero_init(lowerCAmelCase)
         for model_class in self.all_model_classes:
               lowercase__  =	model_class(config=lowerCAmelCase)
               # Skip the check for the backbone
               for name, module in model.named_modules():
                     if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                           lowercase__  =	[f'''{name}.{key}''' for key in module.state_dict().keys()]
                           break

               for name, param in model.named_parameters():
                     if param.requires_grad:
                           if name in backbone_params:
                                 continue
                           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'''      ,					)



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




         """simple docstring"""

         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__  =	ViTHybridModel.from_pretrained(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)

def 	_lowerCAmelCase (    ):
      lowercase__  =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
      return image



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

   '''simple docstring'''




   @cached_property
   def       UpperCAmelCase		(    self	:      int)						->					Union[str, Any]:




         """simple docstring"""

         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
             if is_vision_available()
             else None
         )


   @slow
   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
             lowerCAmelCase)

         lowercase__  =	self.default_image_processor
         lowercase__  =	prepare_img()
         lowercase__  =	image_processor(images=lowerCAmelCase      ,					return_tensors='pt').to(lowerCAmelCase)

         # forward pass
         with torch.no_grad():
               lowercase__  =	model(**lowerCAmelCase)

         # verify the logits
         lowercase__  =	torch.Size((1, 10_00))
         self.assertEqual(outputs.logits.shape      ,					lowerCAmelCase)

         lowercase__  =	torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(lowerCAmelCase)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,					lowerCAmelCase      ,					atol=1E-4))



   @slow
   @require_accelerate
   def       UpperCAmelCase		(    self	:      int)						->					Dict:




         """simple docstring"""

         lowercase__  =	ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
         lowercase__  =	ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'      ,					device_map='auto')

         lowercase__  =	prepare_img()

         lowercase__  =	image_processor(images=lowerCAmelCase      ,					return_tensors='pt')
         lowercase__  =	model(**lowerCAmelCase)
         lowercase__  =	outputs.logits
         # model predicts one of the 1000 ImageNet classes
         lowercase__  =	logits.argmax(-1).item()

         self.assertTrue(model.config.idalabel[predicted_class_idx]      ,					'tabby, tabby cat')
