

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


A_       : Tuple         =			{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : str         =			['DPTFeatureExtractor']
	A_       : Tuple         =			['DPTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Tuple         =			[
	    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DPTForDepthEstimation',
	    'DPTForSemanticSegmentation',
	    'DPTModel',
	    'DPTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_dpt import DPTFeatureExtractor
		from .image_processing_dpt import DPTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dpt import (
		    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPTForDepthEstimation,
		    DPTForSemanticSegmentation,
		    DPTModel,
		    DPTPreTrainedModel,
		)


else:
	import sys

	A_       : Optional[Any]         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""





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 A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=30				,   lowercase_=2				,   lowercase_=3				,   lowercase_=True				,   lowercase_=True				,   lowercase_=32				,   lowercase_=5				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=10				,   lowercase_=0.02				,   ):

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

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

     UpperCAmelCase_					:					Dict			     =    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=lowercase_				,   initializer_range=self.initializer_range				,   )

     return config, pixel_values







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    FlaxViTModel(config=lowercase_  )
     UpperCAmelCase_					:					int			     =    model(lowercase_  )
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
     UpperCAmelCase_					:					Optional[Any]			     =    (self.image_size, self.image_size)
     UpperCAmelCase_					:					List[Any]			     =    (self.patch_size, self.patch_size)
     UpperCAmelCase_					:					str			     =    (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        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.type_sequence_label_size
     UpperCAmelCase_					:					Tuple			     =    FlaxViTForImageClassification(config=lowercase_  )
     UpperCAmelCase_					:					str			     =    model(lowercase_  )
     self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.type_sequence_label_size)  )

     # test greyscale images
     UpperCAmelCase_					:					Any			     =    1
     UpperCAmelCase_					:					Optional[int]			     =    FlaxViTForImageClassification(lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
     UpperCAmelCase_					:					List[Any]			     =    model(lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.prepare_config_and_inputs()
     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Tuple			     =    config_and_inputs
     UpperCAmelCase_					:					Union[str, Any]			     =    {"pixel_values": pixel_values}
     return config, inputs_dict




@require_flax
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Any       =		(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    FlaxViTModelTester(self  )
     UpperCAmelCase_					:					Dict			     =    ConfigTester(self				,   config_class=lowercase_				,   has_text_modality=lowercase_				,   hidden_size=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_					:					List[str]			     =    ["pixel_values"]
        self.assertListEqual(arg_names[:1]				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Union[str, Any]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Tuple			     =    model_class(lowercase_  )

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

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					Union[str, Any]			     =    model_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					Tuple			     =    model_jitted(**lowercase_  ).to_tuple()

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







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_					:					Union[str, Any]			     =    model_class_name.from_pretrained("google/vit-base-patch16-224"  )
        UpperCAmelCase_					:					List[str]			     =    model(np.ones((1, 3, 224, 224)  )  )
        self.assertIsNotNone(lowercase_  )


