





"""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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor





class 							_lowerCamelCase   (							unittest.TestCase			):


						def __init__(self ,				__a ,				__a=7 ,				__a=3 ,				__a=18 ,				__a=30 ,				__a=4_00 ,				__a=True ,				__a=None ,				__a=True ,				__a=False ,				__a=[0.48145466, 0.4578275, 0.40821073] ,				__a=[0.26862954, 0.26130258, 0.27577711] ,				__a=True ,				)			-> Union[str, Any]:
								UpperCamelCase =						size if size is not None else {'''height''': 20, '''width''': 20}
								UpperCamelCase =						parent
								UpperCamelCase =						batch_size
								UpperCamelCase =						num_channels
								UpperCamelCase =						image_size
								UpperCamelCase =						min_resolution
								UpperCamelCase =						max_resolution
								UpperCamelCase =						do_resize
								UpperCamelCase =						size
								UpperCamelCase =						do_normalize
								UpperCamelCase =						image_mean
								UpperCamelCase =						image_std
								UpperCamelCase =						do_pad
								UpperCamelCase =						do_convert_rgb


						def      snake_case_ (self		)			-> List[str]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								    "do_pad": self.do_pad,
								}




						def      snake_case_ (self ,				__a=False ,				__a=False ,				__a=False		)			-> Optional[int]:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
										UpperCamelCase =						[]
										for i in range(self.batch_size		):
												image_inputs.append(
												    np.random.randint(
												        2_55 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta		)		)
								else:
										UpperCamelCase =						[]
										for i in range(self.batch_size		):
												UpperCamelCase =						np.random.choice(np.arange(self.min_resolution ,				self.max_resolution		) ,				2		)
												image_inputs.append(np.random.randint(2_55 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta		)		)

								if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										UpperCamelCase =						[Image.fromarray(np.moveaxis(lowercase_ ,				0 ,				-1		)		) for x in image_inputs]

								if torchify:
										UpperCamelCase =						[torch.from_numpy(lowercase_		) for x in image_inputs]

								return image_inputs





@require_torch
@require_vision
class 							_lowerCamelCase   (							snake_case__	,      unittest.TestCase			):
						UpperCAmelCase_			=      BlipImageProcessor if is_vision_available() else None


						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase =						BlipImageProcessingTester(self		)


						@property
						def      snake_case_ (self		)			-> Union[str, Any]:
								return self.image_processor_tester.prepare_image_processor_dict()


						def      snake_case_ (self		)			-> Tuple:
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(lowercase_ ,				"do_resize"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"size"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"do_normalize"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"image_mean"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"image_std"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"do_convert_rgb"		)		)


						def      snake_case_ (self		)			-> int:
								pass


						def      snake_case_ (self		)			-> Optional[int]:
								# Initialize image_processor
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								UpperCamelCase =						self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_		)
								for image in image_inputs:
										self.assertIsInstance(lowercase_ ,				Image.Image		)

								# Test not batched input
								UpperCamelCase =						image_processor(image_inputs[0] ,				return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape ,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processor(lowercase_ ,				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.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)


						def      snake_case_ (self		)			-> Optional[Any]:
								# Initialize image_processor
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								UpperCamelCase =						self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_ ,				numpify=lowercase_		)
								for image in image_inputs:
										self.assertIsInstance(lowercase_ ,				np.ndarray		)

								# Test not batched input
								UpperCamelCase =						image_processor(image_inputs[0] ,				return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape ,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processor(lowercase_ ,				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.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)




						def      snake_case_ (self		)			-> str:
								# Initialize image_processor
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								UpperCamelCase =						self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_ ,				torchify=lowercase_		)
								for image in image_inputs:
										self.assertIsInstance(lowercase_ ,				torch.Tensor		)

								# Test not batched input
								UpperCamelCase =						image_processor(image_inputs[0] ,				return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape ,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processor(lowercase_ ,				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.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)





@require_torch
@require_vision
class 							_lowerCamelCase   (							snake_case__	,      unittest.TestCase			):
						UpperCAmelCase_			=      BlipImageProcessor if is_vision_available() else None


						def      snake_case_ (self		)			-> Any:
								UpperCamelCase =						BlipImageProcessingTester(self ,				num_channels=4		)
								UpperCamelCase =						3


						@property
						def      snake_case_ (self		)			-> List[str]:
								return self.image_processor_tester.prepare_image_processor_dict()


						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(lowercase_ ,				"do_resize"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"size"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"do_normalize"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"image_mean"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"image_std"		)		)
								self.assertTrue(hasattr(lowercase_ ,				"do_convert_rgb"		)		)


						def      snake_case_ (self		)			-> List[str]:
								pass




						def      snake_case_ (self		)			-> int:
								# Initialize image_processor
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								UpperCamelCase =						self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_		)
								for image in image_inputs:
										self.assertIsInstance(lowercase_ ,				Image.Image		)

								# Test not batched input
								UpperCamelCase =						image_processor(image_inputs[0] ,				return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape ,				(
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processor(lowercase_ ,				return_tensors="pt"		).pixel_values
								self.assertEqual(
								    encoded_images.shape ,				(
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    ) ,				)









"""simple docstring"""





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


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # 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 (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

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

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


