
import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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 transformers import OneFormerImageProcessor
										from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
										from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
					from PIL import Image


def 						UpperCAmelCase      (     a_		,       a_="shi-labs/oneformer_demo"						)		->     Any:







				"""simple docstring"""



				with open(hf_hub_download(a_		,       a_		,       repo_type="dataset"						)		,       "r"						) as f:
								__A             =			json.load(a_						)
				__A             =			{}
				__A             =			[]
				__A             =			[]
				for key, info in class_info.items():
								__A             =			info["name"]
								class_names.append(info["name"]						)
								if info["isthing"]:
												thing_ids.append(int(a_						)						)
				__A             =			thing_ids
				__A             =			class_names
				return metadata





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			str							,A :			Dict							,A :			List[str]=7							,A :			Tuple=3							,A :			Optional[int]=30							,A :			Union[str, Any]=4_00							,A :			Optional[Any]=None							,A :			Dict=True							,A :			str=True							,A :			List[Any]=[0.5, 0.5, 0.5]							,A :			List[Any]=[0.5, 0.5, 0.5]							,A :			List[Any]=10							,A :			Tuple=False							,A :			List[str]=2_55							,A :			str="shi-labs/oneformer_demo"							,A :			int="ade20k_panoptic.json"							,A :			Optional[Any]=10							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			num_channels
					__A             =			min_resolution
					__A             =			max_resolution
					__A             =			do_resize
					__A             =			{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
					__A             =			do_normalize
					__A             =			image_mean
					__A             =			image_std
					__A             =			class_info_file
					__A             =			prepare_metadata(A							,A			)
					__A             =			num_text
					__A             =			repo_path

					# for the post_process_functions
					__A             =			2
					__A             =			10
					__A             =			10
					__A             =			3
					__A             =			4
					__A             =			num_labels
					__A             =			do_reduce_labels
					__A             =			ignore_index






	def     UpperCamelCase_  (	self :			Tuple			):
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "num_labels": self.num_labels,
					    "do_reduce_labels": self.do_reduce_labels,
					    "ignore_index": self.ignore_index,
					    "class_info_file": self.class_info_file,
					    "metadata": self.metadata,
					    "num_text": self.num_text,
					}






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]							,A :			str=False			):
					if not batched:
									__A             =			image_inputs[0]
									if isinstance(A							,Image.Image			):
													__A      ,							__A             =			image.size
									else:
													__A      ,							__A             =			image.shape[1], image.shape[2]
									if w < h:
													__A             =			int(self.size["shortest_edge"] * h / w			)
													__A             =			self.size["shortest_edge"]
									elif w > h:
													__A             =			self.size["shortest_edge"]
													__A             =			int(self.size["shortest_edge"] * w / h			)
									else:
													__A             =			self.size["shortest_edge"]
													__A             =			self.size["shortest_edge"]

					else:
									__A             =			[]
									for image in image_inputs:
													__A      ,							__A             =			self.get_expected_values([image]			)
													expected_values.append((expected_height, expected_width)			)
									__A             =			max(A							,key=lambda A			: item[0]			)[0]
									__A             =			max(A							,key=lambda A			: item[1]			)[1]

					return expected_height, expected_width







	def     UpperCamelCase_  (	self :			List[Any]			):
					return OneFormerForUniversalSegmentationOutput(
					    # +1 for null class
					    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)			)							,masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)			)							,)





@require_torch
@require_vision
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	snake_case_    =					image_processing_class






	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			OneFormerImageProcessorTester(self			)






	@property
	def     UpperCamelCase_  (	self :			List[str]			):
					return self.image_processing_tester.prepare_image_processor_dict()






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(A							,"image_mean"			)			)
					self.assertTrue(hasattr(A							,"image_std"			)			)
					self.assertTrue(hasattr(A							,"do_normalize"			)			)
					self.assertTrue(hasattr(A							,"do_resize"			)			)
					self.assertTrue(hasattr(A							,"size"			)			)
					self.assertTrue(hasattr(A							,"ignore_index"			)			)
					self.assertTrue(hasattr(A							,"class_info_file"			)			)
					self.assertTrue(hasattr(A							,"num_text"			)			)
					self.assertTrue(hasattr(A							,"repo_path"			)			)
					self.assertTrue(hasattr(A							,"metadata"			)			)
					self.assertTrue(hasattr(A							,"do_reduce_labels"			)			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					pass






	def     UpperCamelCase_  (	self :			int			):
					# Initialize image_processor
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__A             =			prepare_image_inputs(self.image_processing_tester							,equal_resolution=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image			)

					# Test not batched input
					__A             =			image_processor(image_inputs[0]							,["semantic"]							,return_tensors="pt"			).pixel_values

					__A      ,							__A             =			self.image_processing_tester.get_expected_values(A			)

					self.assertEqual(
					    encoded_images.shape							,(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,)

					# Test batched
					__A      ,							__A             =			self.image_processing_tester.get_expected_values(A							,batched=A			)

					__A             =			image_processor(
					    A							,["semantic"] * len(A			)							,return_tensors="pt"			).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,)






	def     UpperCamelCase_  (	self :			Any			):
					# Initialize image_processor
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__A             =			prepare_image_inputs(self.image_processing_tester							,equal_resolution=A							,numpify=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,np.ndarray			)

					# Test not batched input
					__A             =			image_processor(image_inputs[0]							,["semantic"]							,return_tensors="pt"			).pixel_values

					__A      ,							__A             =			self.image_processing_tester.get_expected_values(A			)

					self.assertEqual(
					    encoded_images.shape							,(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,)

					# Test batched
					__A      ,							__A             =			self.image_processing_tester.get_expected_values(A							,batched=A			)

					__A             =			image_processor(
					    A							,["semantic"] * len(A			)							,return_tensors="pt"			).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					# Initialize image_processor
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__A             =			prepare_image_inputs(self.image_processing_tester							,equal_resolution=A							,torchify=A			)
					for image in image_inputs:
									self.assertIsInstance(A							,torch.Tensor			)

					# Test not batched input
					__A             =			image_processor(image_inputs[0]							,["semantic"]							,return_tensors="pt"			).pixel_values

					__A      ,							__A             =			self.image_processing_tester.get_expected_values(A			)

					self.assertEqual(
					    encoded_images.shape							,(1, self.image_processing_tester.num_channels, expected_height, expected_width)							,)

					# Test batched
					__A      ,							__A             =			self.image_processing_tester.get_expected_values(A							,batched=A			)

					__A             =			image_processor(
					    A							,["semantic"] * len(A			)							,return_tensors="pt"			).pixel_values
					self.assertEqual(
					    encoded_images.shape							,(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    )							,)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			str=False							,A :			Union[str, Any]=False							,A :			Optional[int]="np"			):
					__A             =			self.image_processing_class(**self.image_processor_dict			)
					# prepare image and target
					__A             =			self.image_processing_tester.num_labels
					__A             =			None
					__A             =			None
					__A             =			prepare_image_inputs(self.image_processing_tester							,equal_resolution=A			)
					if with_segmentation_maps:
									__A             =			num_labels
									if is_instance_map:
													__A             =			list(range(A			)			) * 2
													__A             =			dict(enumerate(A			)			)
									__A             =			[
									    np.random.randint(0							,high * 2							,(img.size[1], img.size[0])			).astype(np.uinta			) for img in image_inputs
									]
									if segmentation_type == "pil":
													__A             =			[Image.fromarray(A			) for annotation in annotations]

					__A             =			image_processor(
					    A							,["semantic"] * len(A			)							,A							,return_tensors="pt"							,instance_id_to_semantic_id=A							,pad_and_return_pixel_mask=A							,)

					return inputs






	def     UpperCamelCase_  (	self :			List[Any]			):
					pass






	def     UpperCamelCase_  (	self :			Tuple			):
					def common(A :			Dict=False							,A :			Optional[int]=None			):
									__A             =			self.comm_get_image_processor_inputs(
									    with_segmentation_maps=A							,is_instance_map=A							,segmentation_type=A			)

									__A             =			inputs["mask_labels"]
									__A             =			inputs["class_labels"]
									__A             =			inputs["pixel_values"]
									__A             =			inputs["text_inputs"]

									# check the batch_size
									for mask_label, class_label, text_input in zip(A							,A							,A			):
													self.assertEqual(mask_label.shape[0]							,class_label.shape[0]			)
													# this ensure padding has happened
													self.assertEqual(mask_label.shape[1:]							,pixel_values.shape[2:]			)
													self.assertEqual(len(A			)							,self.image_processing_tester.num_text			)

					common()
					common(is_instance_map=A			)
					common(is_instance_map=A							,segmentation_type="pil"			)
					common(is_instance_map=A							,segmentation_type="pil"			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			np.zeros((20, 50)			)
					__A             =			1
					__A             =			1
					__A             =			1

					__A             =			binary_mask_to_rle(A			)
					self.assertEqual(len(A			)							,4			)
					self.assertEqual(rle[0]							,21			)
					self.assertEqual(rle[1]							,45			)






	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes							,max_seq_length=77							,task_seq_length=77							,class_info_file="ade20k_panoptic.json"							,num_text=self.image_processing_tester.num_text							,repo_path="shi-labs/oneformer_demo"							,)
					__A             =			self.image_processing_tester.get_fake_oneformer_outputs()

					__A             =			fature_extractor.post_process_semantic_segmentation(A			)

					self.assertEqual(len(A			)							,self.image_processing_tester.batch_size			)
					self.assertEqual(
					    segmentation[0].shape							,(
					        self.image_processing_tester.height,
					        self.image_processing_tester.width,
					    )							,)

					__A             =			[(1, 4) for i in range(self.image_processing_tester.batch_size			)]
					__A             =			fature_extractor.post_process_semantic_segmentation(A							,target_sizes=A			)

					self.assertEqual(segmentation[0].shape							,target_sizes[0]			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes							,max_seq_length=77							,task_seq_length=77							,class_info_file="ade20k_panoptic.json"							,num_text=self.image_processing_tester.num_text							,repo_path="shi-labs/oneformer_demo"							,)
					__A             =			self.image_processing_tester.get_fake_oneformer_outputs()
					__A             =			image_processor.post_process_instance_segmentation(A							,threshold=0			)

					self.assertTrue(len(A			) == self.image_processing_tester.batch_size			)
					for el in segmentation:
									self.assertTrue("segmentation" in el			)
									self.assertTrue("segments_info" in el			)
									self.assertEqual(type(el["segments_info"]			)							,A			)
									self.assertEqual(
									    el["segmentation"].shape							,(self.image_processing_tester.height, self.image_processing_tester.width)			)







	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes							,max_seq_length=77							,task_seq_length=77							,class_info_file="ade20k_panoptic.json"							,num_text=self.image_processing_tester.num_text							,repo_path="shi-labs/oneformer_demo"							,)
					__A             =			self.image_processing_tester.get_fake_oneformer_outputs()
					__A             =			image_processor.post_process_panoptic_segmentation(A							,threshold=0			)

					self.assertTrue(len(A			) == self.image_processing_tester.batch_size			)
					for el in segmentation:
									self.assertTrue("segmentation" in el			)
									self.assertTrue("segments_info" in el			)
									self.assertEqual(type(el["segments_info"]			)							,A			)
									self.assertEqual(
									    el["segmentation"].shape							,(self.image_processing_tester.height, self.image_processing_tester.width)			)


# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE							:Tuple										=    'pytorch_model.bin'
SCREAMING_SNAKE_CASE							:str										=    'pytorch_model.bin.index.json'
SCREAMING_SNAKE_CASE							:int										=    'adapter_config.json'
SCREAMING_SNAKE_CASE							:List[str]										=    'adapter_model.bin'
SCREAMING_SNAKE_CASE							:Any										=    'adapter_model.safetensors'
SCREAMING_SNAKE_CASE							:int										=    'tf_model.h5'
SCREAMING_SNAKE_CASE							:Tuple										=    'tf_model.h5.index.json'
SCREAMING_SNAKE_CASE							:List[Any]										=    'model.ckpt'
SCREAMING_SNAKE_CASE							:Optional[int]										=    'flax_model.msgpack'
SCREAMING_SNAKE_CASE							:List[Any]										=    'flax_model.msgpack.index.json'
SCREAMING_SNAKE_CASE							:List[Any]										=    'model.safetensors'
SCREAMING_SNAKE_CASE							:Any										=    'model.safetensors.index.json'
SCREAMING_SNAKE_CASE							:int										=    'config.json'
SCREAMING_SNAKE_CASE							:List[str]										=    'preprocessor_config.json'
SCREAMING_SNAKE_CASE							:Optional[int]										=    FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE							:Optional[Any]										=    'generation_config.json'
SCREAMING_SNAKE_CASE							:Dict										=    'modelcard.json'

SCREAMING_SNAKE_CASE							:Optional[Any]										=    '▁'
SCREAMING_SNAKE_CASE							:Any										=    SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE							:Tuple										=    [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE							:Union[str, Any]										=    [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE							:Tuple										=    [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def 						UpperCAmelCase      (     a_						)		->     List[str]:







				"""simple docstring"""



				if version.parse(a_						) < version.parse(a_						):
								if "dev" in min_version:
												__A             =			(
												    "This example requires a source install from HuggingFace Transformers (see "
												    "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
												)
								else:
												__A             =			F'''This example requires a minimum version of {min_version},'''
								error_message += F''' but the version found is {__version__}.\n'''
								raise ImportError(
								    error_message
								    + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
								    "versions of HuggingFace Transformers."						)
