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 __snake_case				:




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      image_size
     SCREAMING_SNAKE_CASE__										=      patch_size
     SCREAMING_SNAKE_CASE__										=      num_channels
     SCREAMING_SNAKE_CASE__										=      is_training
     SCREAMING_SNAKE_CASE__										=      use_labels
     SCREAMING_SNAKE_CASE__										=      hidden_size
     SCREAMING_SNAKE_CASE__										=      num_hidden_layers
     SCREAMING_SNAKE_CASE__										=      num_attention_heads
     SCREAMING_SNAKE_CASE__										=      intermediate_size
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__										=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__										=      type_sequence_label_size
     SCREAMING_SNAKE_CASE__										=      initializer_range
     SCREAMING_SNAKE_CASE__										=      scope
     SCREAMING_SNAKE_CASE__										=      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
     SCREAMING_SNAKE_CASE__										=      (self.image_size // 32) ** 2
     SCREAMING_SNAKE_CASE__										=      num_patches + 1




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      None
     if self.use_labels:
       SCREAMING_SNAKE_CASE__										=      ids_tensor([self.batch_size]       ,	self.type_sequence_label_size      )

     SCREAMING_SNAKE_CASE__										=      self.get_config()

     return config, pixel_values, labels




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {
         """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=_lowercase       ,	initializer_range=self.initializer_range       ,	backbone_featmap_shape=self.backbone_featmap_shape       ,	backbone_config=_lowercase       ,	)




   def 	__a						(    self			:				Any       ,	_lowercase			:				Any       ,	_lowercase			:				int       ,	_lowercase			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Any       ,	_lowercase			:				Dict       ,	_lowercase			:				Dict       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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







@require_torch
class __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   lowerCAmelCase_														=							(
       {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ViTHybridModelTester(self      )
     SCREAMING_SNAKE_CASE__										=      ConfigTester(self       ,	config_class=_lowercase       ,	has_text_modality=_lowercase       ,	hidden_size=37      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   @unittest.skip(reason="""ViT does not use inputs_embeds"""      )
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     pass




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

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




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__										=      ["""pixel_values"""]
       self.assertListEqual(arg_names[:1]       ,	_lowercase      )




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__										=      _config_zero_init(_lowercase      )
     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__										=      model_class(config=_lowercase      )
       # Skip the check for the backbone
       for name, module in model.named_modules():
         if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
           SCREAMING_SNAKE_CASE__										=      [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 	__a						(    self			:				str      ):



     """simple docstring"""






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





def 	__SCREAMING_SNAKE_CASE  (					)							->					int:







  """simple docstring"""





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







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




   @cached_property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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




   @slow
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(
         _lowercase      )

     SCREAMING_SNAKE_CASE__										=      self.default_image_processor
     SCREAMING_SNAKE_CASE__										=      prepare_img()
     SCREAMING_SNAKE_CASE__										=      image_processor(images=_lowercase       ,	return_tensors="""pt"""      ).to(_lowercase      )

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

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

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

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




   @slow
   @require_accelerate
   def 	__a						(    self			:				str      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      prepare_img()

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

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

import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCamelCase			: List[str]							   =		object()

# For specifying empty leaf dict `{}`
__lowerCamelCase			: Optional[int]							   =		object()



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       List[str]	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      tuple((re.compile(x + """$"""	) for x in qs)	)
  for i in range(len(__UpperCamelCase	) - len(__UpperCamelCase	) + 1	):
    SCREAMING_SNAKE_CASE__										=      [x.match(__UpperCamelCase	) for x, y in zip(__UpperCamelCase ,     ks[i:]	)]
    if matches and all(__UpperCamelCase	):
      return True
  return False



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  def replace(__UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       Any	):
    for rule, replacement in rules:
      if _match(__UpperCamelCase ,     __UpperCamelCase	):
        return replacement
    return val

  return replace



def 	__SCREAMING_SNAKE_CASE  (					)							->					List[Any]:







  """simple docstring"""





  return [
      # embeddings
      (("transformer", "wpe", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      (("transformer", "wte", "embedding"), P("""mp""" ,     __UpperCamelCase	)),
      # atention
      (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("attention", "out_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("attention", "out_proj", "bias"), None),
      # mlp
      (("mlp", "c_fc", "kernel"), P(__UpperCamelCase ,     """mp"""	)),
      (("mlp", "c_fc", "bias"), P("""mp"""	)),
      (("mlp", "c_proj", "kernel"), P("""mp""" ,     __UpperCamelCase	)),
      (("mlp", "c_proj", "bias"), None),
      # layer norms
      ((r"ln_\d+", "bias"), None),
      ((r"\d+", r"ln_\d+", "scale"), None),
      (("ln_f", "bias"), None),
      (("ln_f", "scale"), None),
  ]






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      _get_partition_rules()
  SCREAMING_SNAKE_CASE__										=      _replacement_rules(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      {k: _unmatched for k in flatten_dict(__UpperCamelCase	)}
  SCREAMING_SNAKE_CASE__										=      {k: replace(__UpperCamelCase ,     __UpperCamelCase	) for k, v in initd.items()}
  assert _unmatched not in result.values(), "Incomplete partition spec."
  return freeze(unflatten_dict(__UpperCamelCase	)	)
