import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel



class       _snake_case   (	unittest.TestCase							):


 def __init__(					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=13					,  SCREAMING_SNAKE_CASE_=30					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=5					,  SCREAMING_SNAKE_CASE_=4					,  SCREAMING_SNAKE_CASE_=37					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=10					,  SCREAMING_SNAKE_CASE_=0.0_2					,  ):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      parent
   lowercase__  :   Optional[Any]    		=      batch_size
   lowercase__  :   Optional[int]    		=      image_size
   lowercase__  :   List[Any]    		=      patch_size
   lowercase__  :   Optional[int]    		=      num_channels
   lowercase__  :   List[str]    		=      is_training
   lowercase__  :   Union[str, Any]    		=      use_labels
   lowercase__  :   int    		=      hidden_size
   lowercase__  :   Optional[int]    		=      num_hidden_layers
   lowercase__  :   List[Any]    		=      num_attention_heads
   lowercase__  :   Union[str, Any]    		=      intermediate_size
   lowercase__  :   Optional[int]    		=      hidden_act
   lowercase__  :   Tuple    		=      hidden_dropout_prob
   lowercase__  :   Optional[Any]    		=      attention_probs_dropout_prob
   lowercase__  :   int    		=      type_sequence_label_size
   lowercase__  :   List[str]    		=      initializer_range

   # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   lowercase__  :   Any    		=      (image_size // patch_size) ** 2
   lowercase__  :   Optional[Any]    		=      num_patches + 1


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   lowercase__  :   str    		=      ViTConfig(
       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=_a					,  initializer_range=self.initializer_range					,  )

   return config, pixel_values


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Any    		=      FlaxViTModel(config=_a)
   lowercase__  :   str    		=      model(_a)
   # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
   lowercase__  :   str    		=      (self.image_size, self.image_size)
   lowercase__  :   List[str]    		=      (self.patch_size, self.patch_size)
   lowercase__  :   Optional[int]    		=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, num_patches + 1, self.hidden_size))


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      self.type_sequence_label_size
   lowercase__  :   Tuple    		=      FlaxViTForImageClassification(config=_a)
   lowercase__  :   int    		=      model(_a)
   self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   # test greyscale images
   lowercase__  :   List[str]    		=      1
   lowercase__  :   str    		=      FlaxViTForImageClassification(_a)

   lowercase__  :   int    		=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowercase__  :   Any    		=      model(_a)



 def      lowercase__     (					self):


   '''simple docstring'''




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



@require_flax
class       _snake_case   (	__SCREAMING_SNAKE_CASE  ,						unittest.TestCase							):
 __lowerCAmelCase  :     str        =			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      FlaxViTModelTester(self)
   lowercase__  :   str    		=      ConfigTester(self					,  config_class=_a					,  has_text_modality=_a					,  hidden_size=37)


 def      lowercase__     (					self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_a)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   int    		=      self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_a)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowercase__  :   List[Any]    		=      model_class(_a)
     lowercase__  :   Optional[int]    		=      inspect.signature(model.__call__)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     lowercase__  :   List[str]    		=      [*signature.parameters.keys()]

     lowercase__  :   Tuple    		=      ["""pixel_values"""]
     self.assertListEqual(arg_names[:1]					,  _a)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowercase__  :   List[str]    		=      self._prepare_for_class(_a					,  _a)
       lowercase__  :   Tuple    		=      model_class(_a)

       @jax.jit
       def model_jitted(SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):
         return model(pixel_values=_a					,  **_a)

       with self.subTest("""JIT Enabled"""):
         lowercase__  :   List[str]    		=      model_jitted(**_a).to_tuple()

       with self.subTest("""JIT Disabled"""):
         with jax.disable_jit():
           lowercase__  :   Union[str, Any]    		=      model_jitted(**_a).to_tuple()

       self.assertEqual(len(_a)					,  len(_a))
       for jitted_output, output in zip(_a					,  _a):
         self.assertEqual(jitted_output.shape					,  output.shape)



 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__  :   int    		=      model_class_name.from_pretrained("""google/vit-base-patch16-224""")
     lowercase__  :   str    		=      model(np.ones((1, 3, 2_24, 2_24)))
     self.assertIsNotNone(_a)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__    :		List[str]          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   Any    		=      {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       lowercase__  :   Optional[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   List[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       lowercase__  :   int    		=      backbone_config
     else:
       raise ValueError(
           f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.')

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""")
   else:
     lowercase__  :   Optional[int]    		=      None
     lowercase__  :   Union[str, Any]    		=      None
     lowercase__  :   Tuple    		=      []

   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   Union[str, Any]    		=      num_attention_heads
   lowercase__  :   List[Any]    		=      intermediate_size
   lowercase__  :   Optional[Any]    		=      hidden_act
   lowercase__  :   int    		=      hidden_dropout_prob
   lowercase__  :   str    		=      attention_probs_dropout_prob
   lowercase__  :   Dict    		=      initializer_range
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Tuple    		=      patch_size
   lowercase__  :   Tuple    		=      num_channels
   lowercase__  :   Dict    		=      qkv_bias
   lowercase__  :   List[Any]    		=      backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""")
   lowercase__  :   Dict    		=      readout_type
   lowercase__  :   Tuple    		=      reassemble_factors
   lowercase__  :   Any    		=      neck_hidden_sizes
   lowercase__  :   Dict    		=      fusion_hidden_size
   lowercase__  :   Tuple    		=      head_in_index
   lowercase__  :   Tuple    		=      use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowercase__  :   Tuple    		=      use_auxiliary_head
   lowercase__  :   int    		=      auxiliary_loss_weight
   lowercase__  :   Union[str, Any]    		=      semantic_loss_ignore_index
   lowercase__  :   str    		=      semantic_classifier_dropout



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






