



import json
import os
import tempfile

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


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  str=False		,						__lowerCamelCase :  Optional[Any]=False		,						__lowerCamelCase :  int=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor_tester.min_resolution
   if getattr(__lowerCamelCase		,						"size_divisor"		,						__lowerCamelCase  ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    __SCREAMING_SNAKE_CASE							:     str							=						max(image_processor_tester.size_divisor		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						np.random.choice(np.arange(__lowerCamelCase		,						image_processor_tester.max_resolution  )		,						2  )
  image_inputs.append(
      np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

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

 if torchify:
  __SCREAMING_SNAKE_CASE							:     Dict							=						[torch.from_numpy(__lowerCamelCase  ) for image in image_inputs]

 return image_inputs






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]=10		,						__lowerCamelCase :  Any=10		,						__lowerCamelCase :  int=False		,						__lowerCamelCase :  List[str]=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 for i in range(image_processor_tester.num_frames  ):
  video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for frame in video]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						[torch.from_numpy(__lowerCamelCase  ) for frame in video]

 return video






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Optional[int]=False		,						__lowerCamelCase :  Any=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						np.random.choice(
       np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution  )		,						2  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_video(
       image_processor_tester=__lowerCamelCase		,						width=__lowerCamelCase		,						height=__lowerCamelCase		,						numpify=__lowerCamelCase		,						torchify=__lowerCamelCase		,						)
  video_inputs.append(__lowerCamelCase  )

 return video_inputs






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      __SCREAMING_SNAKE_CASE							:     str							=						json.loads(image_processor.to_json_string()					)
      for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Dict							=						os.path.join(UpperCamelCase  ,	"image_processor.json"					)
       image_processor_first.to_json_file(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_json_file(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor_first.save_pretrained(UpperCamelCase					)[0]
       check_json_file_has_correct_format(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class.from_pretrained(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class()
      self.assertIsNotNone(UpperCamelCase					)






     @require_torch
     @require_vision
     def 						__snake_case     (							self			:				Any					)->List[Any]:
      if self.test_cast_dtype is not None:
       # Initialize image_processor
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.floataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to("cpu"  ,	torch.bfloataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.bfloataa					)

       with self.assertRaises(UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.bfloataa  ,	"cpu"					)

       # Try with text + image feature
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						encoding.to(torch.floataa					)

       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)
       self.assertEqual(encoding.input_ids.dtype  ,	torch.long					)











from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
      import pyximport

      pyximport.install(setup_args={"""include_dirs""": np.get_include()})
      from . import algos_graphormer  # noqa E402






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  int = 512  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						x.shape[1] if len(x.shape  ) > 1 else 1
 __SCREAMING_SNAKE_CASE							:     int							=						1 + np.arange(0		,						feature_num * offset		,						__lowerCamelCase		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						x + feature_offset
 return x






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  int=True  ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,						["cython"]  )

 if keep_features and "edge_attr" in item.keys():  # edge_attr
  __SCREAMING_SNAKE_CASE							:     List[str]							=						np.asarray(item["edge_attr"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						np.ones((len(item["edge_index"][0]  ), 1)		,						dtype=np.intaa  )  # same embedding for all

 if keep_features and "node_feat" in item.keys():  # input_nodes
  __SCREAMING_SNAKE_CASE							:     Dict							=						np.asarray(item["node_feat"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						np.ones((item["num_nodes"], 1)		,						dtype=np.intaa  )  # same embedding for all

 __SCREAMING_SNAKE_CASE							:     Any							=						np.asarray(item["edge_index"]		,						dtype=np.intaa  )

 __SCREAMING_SNAKE_CASE							:     Any							=						convert_to_single_emb(__lowerCamelCase  ) + 1
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						item["num_nodes"]

 if len(edge_attr.shape  ) == 1:
  __SCREAMING_SNAKE_CASE							:     Any							=						edge_attr[:, None]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_to_single_emb(__lowerCamelCase  ) + 1

 # node adj matrix [num_nodes, num_nodes] bool
 __SCREAMING_SNAKE_CASE							:     int							=						np.zeros([num_nodes, num_nodes]		,						dtype=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						True

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.floyd_warshall(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.amax(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.gen_edge_input(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single  )  # with graph token

 # combine
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						input_nodes + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     Dict							=						attn_bias
 __SCREAMING_SNAKE_CASE							:     Tuple							=						attn_edge_type
 __SCREAMING_SNAKE_CASE							:     Any							=						shortest_path_result.astype(np.intaa  ) + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     int							=						np.sum(__lowerCamelCase		,						axis=1  ).reshape(-1  ) + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						item["in_degree"]  # for undirected graph
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						input_edges + 1  # we shift all indices by one for padding
 if "labels" not in item:
  __SCREAMING_SNAKE_CASE							:     Any							=						item["y"]

 return item






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				List[str]=2_0  ,	UpperCamelCase			:				Dict=False					)->Any:
      if not is_cython_available():
       raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						spatial_pos_max
      __SCREAMING_SNAKE_CASE							:     Dict							=						on_the_fly_processing






     def __call__(							self			:				Optional[int]  ,	UpperCamelCase			:				List[dict]					)->Dict[str, Any]:
      if self.on_the_fly_processing:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[preprocess_item(UpperCamelCase					) for i in features]

      if not isinstance(features[0]  ,	UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						[vars(UpperCamelCase					) for f in features]
      __SCREAMING_SNAKE_CASE							:     str							=						{}

      __SCREAMING_SNAKE_CASE							:     int							=						max(len(i["input_nodes"]					) for i in features					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						len(features[0]["input_nodes"][0]					)
      __SCREAMING_SNAKE_CASE							:     Any							=						len(features[0]["attn_edge_type"][0][0]					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						max(len(i["input_edges"][0][0]					) for i in features					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						len(features[0]["input_edges"][0][0][0]					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						len(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.zeros(UpperCamelCase  ,	max_node_num + 1  ,	max_node_num + 1  ,	dtype=torch.float					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Any							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.zeros(
          UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)

      for ix, f in enumerate(UpperCamelCase					):
       for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
        __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.tensor(f[k]					)

       if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
        __SCREAMING_SNAKE_CASE							:     int							=						float("-inf"					)

       __SCREAMING_SNAKE_CASE							:     str							=						f["attn_bias"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f[
           "attn_edge_type"
       ]
       __SCREAMING_SNAKE_CASE							:     Dict							=						f["spatial_pos"]
       __SCREAMING_SNAKE_CASE							:     int							=						f["in_degree"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f["input_nodes"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						f["input_edges"]

      __SCREAMING_SNAKE_CASE							:     Any							=						batch["in_degree"]

      __SCREAMING_SNAKE_CASE							:     int							=						features[0]["labels"]
      if len(UpperCamelCase					) == 1:  # one task
       if isinstance(sample[0]  ,	UpperCamelCase					):  # regression
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
       else:  # binary classification
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
      else:  # multi task classification, left to float to keep the NaNs
       __SCREAMING_SNAKE_CASE							:     int							=						torch.from_numpy(np.stack([i["labels"] for i in features]  ,	axis=0					)					)

      return batch






