
"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor

class 		a__						(				unittest.TestCase						):
   def __init__(      self     ,   _a     ,   _a=7     ,   _a=3     ,   _a=18     ,   _a=30     ,   _a=400     ,   _a=True     ,   _a=None     ,   _a=True     ,   _a=None     ,   ):
          lowercase   :    List[str]			=							size if size is not None else {"shortest_edge": 20}
          lowercase   :    Tuple			=							crop_size if crop_size is not None else {"height": 18, "width": 18}
          lowercase   :    Optional[int]			=							parent
          lowercase   :    str			=							batch_size
          lowercase   :    Dict			=							num_channels
          lowercase   :    List[Any]			=							image_size
          lowercase   :    Dict			=							min_resolution
          lowercase   :    Dict			=							max_resolution
          lowercase   :    Tuple			=							do_resize
          lowercase   :    Dict			=							size
          lowercase   :    Tuple			=							do_center_crop
          lowercase   :    Dict			=							crop_size

   def       __magic_name__      (      self    ):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }


@require_torch
@require_vision
class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    MobileNetVaImageProcessor if is_vision_available() else None
   def       __magic_name__      (      self    ):
          lowercase   :    Tuple			=							MobileNetVaImageProcessingTester(self    )
   @property
   def       __magic_name__      (      self    ):
          return self.image_processor_tester.prepare_image_processor_dict()
   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, Any]			=							self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(_a     ,   "do_resize"    )    )
          self.assertTrue(hasattr(_a     ,   "size"    )    )
          self.assertTrue(hasattr(_a     ,   "do_center_crop"    )    )
          self.assertTrue(hasattr(_a     ,   "center_crop"    )    )
   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, Any]			=							self.image_processing_class.from_dict(self.image_processor_dict    )
          self.assertEqual(image_processor.size     ,   {"shortest_edge": 20}    )
          self.assertEqual(image_processor.crop_size     ,   {"height": 18, "width": 18}    )

          lowercase   :    Dict			=							self.image_processing_class.from_dict(self.image_processor_dict     ,   size=42     ,   crop_size=84    )
          self.assertEqual(image_processor.size     ,   {"shortest_edge": 42}    )
          self.assertEqual(image_processor.crop_size     ,   {"height": 84, "width": 84}    )
   def       __magic_name__      (      self    ):
          pass
   def       __magic_name__      (      self    ):
          # Initialize image_processing
          lowercase   :    List[Any]			=							self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          lowercase   :    int			=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=_a    )
          for image in image_inputs:
                 self.assertIsInstance(_a     ,   Image.Image    )

          # Test not batched input
          lowercase   :    Tuple			=							image_processing(image_inputs[0]     ,   return_tensors="pt"    ).pixel_values
          self.assertEqual(
              encoded_images.shape     ,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )     ,   )

          # Test batched
          lowercase   :    Union[str, Any]			=							image_processing(_a     ,   return_tensors="pt"    ).pixel_values
          self.assertEqual(
              encoded_images.shape     ,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )     ,   )
   def       __magic_name__      (      self    ):
          # Initialize image_processing
          lowercase   :    Optional[int]			=							self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          lowercase   :    Any			=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=_a     ,   numpify=_a    )
          for image in image_inputs:
                 self.assertIsInstance(_a     ,   np.ndarray    )

          # Test not batched input
          lowercase   :    Any			=							image_processing(image_inputs[0]     ,   return_tensors="pt"    ).pixel_values
          self.assertEqual(
              encoded_images.shape     ,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )     ,   )

          # Test batched
          lowercase   :    Dict			=							image_processing(_a     ,   return_tensors="pt"    ).pixel_values
          self.assertEqual(
              encoded_images.shape     ,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )     ,   )

   def       __magic_name__      (      self    ):
          # Initialize image_processing
          lowercase   :    Any			=							self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          lowercase   :    List[Any]			=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=_a     ,   torchify=_a    )
          for image in image_inputs:
                 self.assertIsInstance(_a     ,   torch.Tensor    )

          # Test not batched input
          lowercase   :    str			=							image_processing(image_inputs[0]     ,   return_tensors="pt"    ).pixel_values
          self.assertEqual(
              encoded_images.shape     ,   (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )     ,   )

          # Test batched
          lowercase   :    List[str]			=							image_processing(_a     ,   return_tensors="pt"    ).pixel_values
          self.assertEqual(
              encoded_images.shape     ,   (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )     ,   )




"""simple docstring"""




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


_A      :	List[Any]       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """graphormer"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a = 1     ,   _a = 512 * 9     ,   _a = 512 * 3     ,   _a = 512     ,   _a = 512     ,   _a = 512     ,   _a = 128     ,   _a = 5     ,   _a = 1_024     ,   _a = "multi_hop"     ,   _a = 512     ,   _a = False     ,   _a = 12     ,   _a = 768     ,   _a = 768     ,   _a = 32     ,   _a = 0.1     ,   _a = 0.1     ,   _a = 0.0     ,   _a = False     ,   _a = False     ,   _a = False     ,   _a = "gelu"     ,   _a = None     ,   _a = False     ,   _a = 0     ,   _a = False     ,   _a = 0.0     ,   _a = 8     ,   _a = None     ,   _a = None     ,   _a = True     ,   _a = True     ,   _a=0     ,   _a=1     ,   _a=2     ,   **_a     ,   ):
          lowercase   :    Any			=							num_classes
          lowercase   :    Optional[Any]			=							num_atoms
          lowercase   :    Optional[Any]			=							num_in_degree
          lowercase   :    Any			=							num_out_degree
          lowercase   :    Optional[int]			=							num_edges
          lowercase   :    Any			=							num_spatial
          lowercase   :    Any			=							num_edge_dis
          lowercase   :    str			=							edge_type
          lowercase   :    Union[str, Any]			=							multi_hop_max_dist
          lowercase   :    List[str]			=							spatial_pos_max
          lowercase   :    Dict			=							max_nodes
          lowercase   :    str			=							num_hidden_layers
          lowercase   :    Union[str, Any]			=							embedding_dim
          lowercase   :    Optional[int]			=							embedding_dim
          lowercase   :    int			=							ffn_embedding_dim
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[int]			=							dropout
          lowercase   :    Dict			=							attention_dropout
          lowercase   :    int			=							layerdrop
          lowercase   :    int			=							encoder_normalize_before
          lowercase   :    List[str]			=							pre_layernorm
          lowercase   :    Union[str, Any]			=							apply_graphormer_init
          lowercase   :    Tuple			=							activation_fn
          lowercase   :    Tuple			=							embed_scale
          lowercase   :    List[Any]			=							freeze_embeddings
          lowercase   :    List[Any]			=							num_trans_layers_to_freeze
          lowercase   :    str			=							share_input_output_embed
          lowercase   :    List[str]			=							traceable
          lowercase   :    str			=							q_noise
          lowercase   :    List[str]			=							qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          lowercase   :    Optional[int]			=							kdim
          lowercase   :    str			=							vdim
          lowercase   :    List[Any]			=							self_attention
          lowercase   :    Any			=							bias

          super().__init__(
              pad_token_id=_a     ,   bos_token_id=_a     ,   eos_token_id=_a     ,   **_a     ,   )


