

import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:

					class  a     :


											@staticmethod
											def 				_UpperCAmelCase (				*A_  ,    **A_		):







															'''simple docstring'''


															pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class  a     (		unittest.TestCase ):
						_lowercase			  =	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						_lowercase			  =	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			ImageClassificationPipeline(model=A_  ,    image_processor=A_  ,    top_k=2		)
										_UpperCAmelCase				:						Optional[Any]       						=			[
										    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
										    "http://images.cocodataset.org/val2017/000000039769.jpg",
										]
										return image_classifier, examples


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)

										self.assertEqual(
										    A_  ,    [
										        {"score": ANY(A_		), "label": ANY(A_		)},
										        {"score": ANY(A_		), "label": ANY(A_		)},
										    ]  ,    )

										import datasets

										_UpperCAmelCase				:						int       						=			datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,    "image"  ,    split="test"		)

										# Accepts URL + PIL.Image + lists
										_UpperCAmelCase				:						Any       						=			image_classifier(
										    [
										        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        # RGBA
										        dataset[0]["file"],
										        # LA
										        dataset[1]["file"],
										        # L
										        dataset[2]["file"],
										    ]		)
										self.assertEqual(
										    A_  ,    [
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										        [
										            {"score": ANY(A_		), "label": ANY(A_		)},
										            {"score": ANY(A_		), "label": ANY(A_		)},
										        ],
										    ]  ,    )


						@require_torch
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			"hf-internal-testing/tiny-random-vit"
										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model=A_		)

										_UpperCAmelCase				:						Optional[int]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]  ,    )

										_UpperCAmelCase				:						List[str]       						=			image_classifier(
										    [
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										    ]  ,    top_k=2  ,    )
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										    ]  ,    )


						@require_tf
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			"hf-internal-testing/tiny-random-vit"
										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model=A_  ,    framework="tf"		)

										_UpperCAmelCase				:						Union[str, Any]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}]  ,    )

										_UpperCAmelCase				:						Optional[int]       						=			image_classifier(
										    [
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										        "http://images.cocodataset.org/val2017/000000039769.jpg",
										    ]  ,    top_k=2  ,    )
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										        [{"label": "LABEL_1", "score": 0.5_74}, {"label": "LABEL_0", "score": 0.4_26}],
										    ]  ,    )


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			PreTrainedTokenizer()

										# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
										_UpperCAmelCase				:						int       						=			pipeline(
										    "image-classification"  ,    model="hf-internal-testing/tiny-random-vit"  ,    tokenizer=A_		)

										self.assertIs(image_classifier.tokenizer  ,    A_		)





						@slow
						@require_torch
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-conv"		)
										_UpperCAmelCase				:						Union[str, Any]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.43_85, "label": "tabby, tabby cat"},
										        {"score": 0.3_21, "label": "tiger cat"},
										        {"score": 0.05_02, "label": "Egyptian cat"},
										        {"score": 0.01_37, "label": "crib, cot"},
										        {"score": 0.0_07, "label": "radiator"},
										    ]  ,    )

										_UpperCAmelCase				:						str       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-fourier"		)
										_UpperCAmelCase				:						Optional[int]       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.56_58, "label": "tabby, tabby cat"},
										        {"score": 0.13_09, "label": "tiger cat"},
										        {"score": 0.07_22, "label": "Egyptian cat"},
										        {"score": 0.07_07, "label": "remote control, remote"},
										        {"score": 0.00_82, "label": "computer keyboard, keypad"},
										    ]  ,    )

										_UpperCAmelCase				:						List[Any]       						=			pipeline("image-classification"  ,    model="deepmind/vision-perceiver-learned"		)
										_UpperCAmelCase				:						str       						=			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
										self.assertEqual(
										    nested_simplify(A_  ,    decimals=4		)  ,    [
										        {"score": 0.30_22, "label": "tabby, tabby cat"},
										        {"score": 0.23_62, "label": "Egyptian cat"},
										        {"score": 0.18_56, "label": "tiger cat"},
										        {"score": 0.03_24, "label": "remote control, remote"},
										        {"score": 0.00_96, "label": "quilt, comforter, comfort, puff"},
										    ]  ,    )



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							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							int = 5_1_2     ):
					SCREAMING_SNAKE_CASE				=				x.shape[1] if len(x.shape     ) > 1 else 1
					SCREAMING_SNAKE_CASE				=				1 + np.arange(0					,       feature_num * offset					,       UpperCAmelCase__					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				x + feature_offset
					return x





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							Optional[Any]=True     ):
					requires_backends(UpperCAmelCase__					,       ["cython"]     )

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

					SCREAMING_SNAKE_CASE				=				np.asarray(item["edge_index"]					,       dtype=np.intaa     )

					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1
					SCREAMING_SNAKE_CASE				=				item["num_nodes"]

					if len(edge_attr.shape     ) == 1:
										SCREAMING_SNAKE_CASE				=				edge_attr[:, None]
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,       dtype=np.intaa     )
					SCREAMING_SNAKE_CASE				=				convert_to_single_emb(UpperCAmelCase__     ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes, num_nodes]					,       dtype=UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				True

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				algos_graphormer.floyd_warshall(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.amax(UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE				=				algos_graphormer.gen_edge_input(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				np.zeros([num_nodes + 1, num_nodes + 1]					,       dtype=np.single     )  # with graph token

					# combine
					SCREAMING_SNAKE_CASE				=				input_nodes + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				attn_bias
					SCREAMING_SNAKE_CASE				=				attn_edge_type
					SCREAMING_SNAKE_CASE				=				shortest_path_result.astype(np.intaa     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				np.sum(UpperCAmelCase__					,       axis=1     ).reshape(-1     ) + 1  # we shift all indices by one for padding
					SCREAMING_SNAKE_CASE				=				item["in_degree"]  # for undirected graph
					SCREAMING_SNAKE_CASE				=				input_edges + 1  # we shift all indices by one for padding
					if "labels" not in item:
										SCREAMING_SNAKE_CASE				=				item["y"]

					return item


class   lowercase			:




							def __init__(  self      :    int		,							_UpperCamelCase      :    Any=20		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												if not is_cython_available():
																	raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

												SCREAMING_SNAKE_CASE				=				spatial_pos_max
												SCREAMING_SNAKE_CASE				=				on_the_fly_processing





							def __call__(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[dict]					)    -> Dict[str, Any]:



												'''simple docstring'''

												if self.on_the_fly_processing:
																	SCREAMING_SNAKE_CASE				=				[preprocess_item(_UpperCamelCase					) for i in features]

												if not isinstance(features[0]		,							_UpperCamelCase					):
																	SCREAMING_SNAKE_CASE				=				[vars(_UpperCamelCase					) for f in features]
												SCREAMING_SNAKE_CASE				=				{}

												SCREAMING_SNAKE_CASE				=				max(len(i["input_nodes"]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_nodes"][0]					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["attn_edge_type"][0][0]					)
												SCREAMING_SNAKE_CASE				=				max(len(i["input_edges"][0][0]					) for i in features					)
												SCREAMING_SNAKE_CASE				=				len(features[0]["input_edges"][0][0][0]					)
												SCREAMING_SNAKE_CASE				=				len(_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							max_node_num + 1		,							max_node_num + 1		,							dtype=torch.float					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				torch.zeros(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							dtype=torch.long					)
												SCREAMING_SNAKE_CASE				=				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				=				torch.tensor(f[k]					)

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

																	SCREAMING_SNAKE_CASE				=				f["attn_bias"]
																	SCREAMING_SNAKE_CASE				=				f[
																	    "attn_edge_type"
																	]
																	SCREAMING_SNAKE_CASE				=				f["spatial_pos"]
																	SCREAMING_SNAKE_CASE				=				f["in_degree"]
																	SCREAMING_SNAKE_CASE				=				f["input_nodes"]
																	SCREAMING_SNAKE_CASE				=				f["input_edges"]

												SCREAMING_SNAKE_CASE				=				batch["in_degree"]

												SCREAMING_SNAKE_CASE				=				features[0]["labels"]
												if len(_UpperCamelCase					) == 1:  # one task
																	if isinstance(sample[0]		,							_UpperCamelCase					):  # regression
																						SCREAMING_SNAKE_CASE				=				torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
																	else:  # binary classification
																						SCREAMING_SNAKE_CASE				=				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				=				torch.from_numpy(np.stack([i["labels"] for i in features]		,							axis=0					)					)

												return batch




