"""simple docstring"""




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    """files"""			,   [
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]			,   )
def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   tmp_path_factory.mktemp("""dset_infos_dir"""     )
		if "full:README.md" in files:
				with open(dataset_infos_dir / """README.md"""			,   """w"""     ) as f:
						f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""     )
		if "empty:README.md" in files:
				with open(dataset_infos_dir / """README.md"""			,   """w"""     ) as f:
						f.write(""""""     )
    # we want to support dataset_infos.json for backward compatibility
		if "dataset_infos.json" in files:
				with open(dataset_infos_dir / """dataset_infos.json"""			,   """w"""     ) as f:
						f.write("""{\"default\": {\"dataset_size\": 42}}"""     )
		lowerCAmelCase__        =   DatasetInfosDict.from_directory(lowerCamelCase__     )
		assert dataset_infos
		assert dataset_infos["default"].dataset_size == 42







@pytest.mark.parametrize(
    """dataset_info"""			,   [
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""			,   features=Features({"""a""": Value("""int32"""     )}     )			,   builder_name="""builder"""			,   config_name="""config"""			,   version="""1.0.0"""			,   splits=[{"""name""": """train"""}]			,   download_size=42			,   ),
    ]			,   )
def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   str(lowerCamelCase__     )
		dataset_info.write_to_directory(lowerCamelCase__     )
		lowerCAmelCase__        =   DatasetInfo.from_directory(lowerCamelCase__     )
		assert dataset_info == reloaded
		assert os.path.exists(os.path.join(lowerCamelCase__			,   """dataset_info.json"""     )     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   DatasetInfo(
		    description="""foo"""			,   citation="""bar"""			,   homepage="""https://foo.bar"""			,   license="""CC0"""			,   features=Features({"""a""": Value("""int32"""     )}     )			,   post_processed={}			,   supervised_keys=()			,   task_templates=[]			,   builder_name="""builder"""			,   config_name="""config"""			,   version="""1.0.0"""			,   splits=[{"""name""": """train""", """num_examples""": 42}]			,   download_checksums={}			,   download_size=1337			,   post_processing_size=442			,   dataset_size=1234			,   size_in_bytes=1337 + 442 + 1234			,   )
		lowerCAmelCase__        =   dataset_info._to_yaml_dict()
		assert sorted(lowerCamelCase__     ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML     )
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				assert key in dataset_info_yaml_dict
				assert isinstance(dataset_info_yaml_dict[key]			,   (list, dict, int, str)     )
		lowerCAmelCase__        =   yaml.safe_dump(lowerCamelCase__     )
		lowerCAmelCase__        =   yaml.safe_load(lowerCamelCase__     )
		assert dataset_info_yaml_dict == reloaded







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""




		lowerCAmelCase__        =   DatasetInfo()
		lowerCAmelCase__        =   dataset_info._to_yaml_dict()
		assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    """dataset_infos_dict"""			,   [
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}     ),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}     ),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""			,   features=Features({"""a""": Value("""int32"""     )}     )			,   builder_name="""builder"""			,   config_name="""config"""			,   version="""1.0.0"""			,   splits=[{"""name""": """train"""}]			,   download_size=42			,   )
            }     ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42     ),
                """v2""": DatasetInfo(dataset_size=1337     ),
            }     ),
    ]			,   )
def 							_UpperCAmelCase		(       lowerCamelCase__			,   lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   str(lowerCamelCase__     )
		dataset_infos_dict.write_to_directory(lowerCamelCase__     )
		lowerCAmelCase__        =   DatasetInfosDict.from_directory(lowerCamelCase__     )

		# the config_name of the dataset_infos_dict take over the attribute
		for config_name, dataset_info in dataset_infos_dict.items():
				lowerCAmelCase__        =   config_name
				# the yaml representation doesn't include fields like description or citation
				# so we just test that we can recover what we can from the yaml
				lowerCAmelCase__        =   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()     )
		assert dataset_infos_dict == reloaded

		if dataset_infos_dict:
				assert os.path.exists(os.path.join(lowerCamelCase__			,   """README.md"""     )     )





"""simple docstring"""




# Copyright 2023 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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
			from PIL import Image

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	List[str]			=    (
					    "This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."
					    "It takes two arguments named `image` which should be the original image, and `label` which should be a text "
					    "describing the elements what should be identified in the segmentation mask. The tool returns the mask."
					)
					UpperCamelCase_ :	str			=    "CIDAS/clipseg-rd64-refined"
					UpperCamelCase_ :	Any			=    "image_segmenter"
					UpperCamelCase_ :	Optional[Any]			=    CLIPSegForImageSegmentation

					UpperCamelCase_ :	List[str]			=    ["image", "text"]
					UpperCamelCase_ :	int			=    ["image"]




					def __init__(	self						:     Tuple  ,						*snake_case__						:     str  ,						**snake_case__						:     Optional[Any]     ):
							requires_backends(self  ,						["""vision"""]     )
							super().__init__(*snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     "Image"  ,						snake_case__						:     str     ):
							return self.pre_processor(text=[label]  ,						images=[image]  ,						padding=snake_case__  ,						return_tensors="""pt"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							with torch.no_grad():
									lowerCAmelCase__        =   self.model(**snake_case__     ).logits
							return logits




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[Any]     ):
							lowerCAmelCase__        =   outputs.cpu().detach().numpy()
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   1
							return Image.fromarray((array * 255).astype(np.uinta     )     )




