





"""simple docstring"""






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									super().__init__(
									    _UpperCAmelCase		,     split=_UpperCAmelCase		,     features=_UpperCAmelCase		,     cache_dir=_UpperCAmelCase		,     keep_in_memory=_UpperCAmelCase		,     streaming=_UpperCAmelCase		,     num_proc=_UpperCAmelCase		,     **_UpperCAmelCase		,     )
									__a	:  List[str]         =					field
									__a	:  Union[str, Any]         =					path_or_paths if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ) else {self.split: path_or_paths}
									__a	:  List[Any]         =					Json(
									    cache_dir=_UpperCAmelCase		,     data_files=_UpperCAmelCase		,     features=_UpperCAmelCase		,     field=_UpperCAmelCase		,     **_UpperCAmelCase		,     )







					def 	_lowerCamelCase						(  self       ):
									# Build iterable dataset
									if self.streaming:
													__a	:  int         =					self.builder.as_streaming_dataset(split=self.split       )
									# Build regular (map-style) dataset
									else:
													__a	:  Dict         =					None
													__a	:  Optional[int]         =					None
													__a	:  List[Any]         =					None
													__a	:  Tuple         =					None

													self.builder.download_and_prepare(
													    download_config=_UpperCAmelCase		,     download_mode=_UpperCAmelCase		,     verification_mode=_UpperCAmelCase		,     base_path=_UpperCAmelCase		,     num_proc=self.num_proc		,     )
													__a	:  Optional[int]         =					self.builder.as_dataset(
													    split=self.split		,     verification_mode=_UpperCAmelCase		,     in_memory=self.keep_in_memory       )
									return dataset






class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									if num_proc is not None and num_proc <= 0:
													raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""       )

									__a	:  Optional[int]         =					dataset
									__a	:  str         =					path_or_buf
									__a	:  Any         =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									__a	:  Tuple         =					num_proc
									__a	:  List[str]         =					'''utf-8'''
									__a	:  int         =					to_json_kwargs







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					self.to_json_kwargs.pop('''path_or_buf'''		,     _UpperCAmelCase       )
									__a	:  int         =					self.to_json_kwargs.pop('''orient'''		,     '''records'''       )
									__a	:  List[Any]         =					self.to_json_kwargs.pop('''lines'''		,     True if orient == '''records''' else False       )
									__a	:  Dict         =					self.to_json_kwargs.pop('''index'''		,     False if orient in ['''split''', '''table'''] else True       )
									__a	:  Tuple         =					self.to_json_kwargs.pop('''compression'''		,     _UpperCAmelCase       )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
													raise NotImplementedError(f"""`datasets` currently does not support {compression} compression"""       )

									if isinstance(self.path_or_buf		,     (str, bytes, os.PathLike)       ):
													with fsspec.open(self.path_or_buf		,     '''wb'''		,     compression=_UpperCAmelCase       ) as buffer:
																	__a	:  Dict         =					self._write(file_obj=_UpperCAmelCase		,     orient=_UpperCAmelCase		,     lines=_UpperCAmelCase		,     index=_UpperCAmelCase		,     **self.to_json_kwargs       )
									else:
													if compression:
																	raise NotImplementedError(
																	    f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
																	    ''' was passed. Please provide a local path instead.'''       )
													__a	:  Any         =					self._write(
													    file_obj=self.path_or_buf		,     orient=_UpperCAmelCase		,     lines=_UpperCAmelCase		,     index=_UpperCAmelCase		,     **self.to_json_kwargs       )
									return written







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a    ,  __a    ,  __a    ,  __a    ,  __a	:  Optional[int]         =					args

									__a	:  List[Any]         =					query_table(
									    table=self.dataset.data		,     key=slice(_UpperCAmelCase		,     offset + self.batch_size       )		,     indices=self.dataset._indices		,     )
									__a	:  Union[str, Any]         =					batch.to_pandas().to_json(
									    path_or_buf=_UpperCAmelCase		,     orient=_UpperCAmelCase		,     lines=_UpperCAmelCase		,     index=_UpperCAmelCase		,     **_UpperCAmelCase       )
									if not json_str.endswith('''\n'''       ):
													json_str += "\n"
									return json_str.encode(self.encoding       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     **_UpperCAmelCase		,     ):
									__a	:  List[str]         =					0

									if self.num_proc is None or self.num_proc == 1:
													for offset in logging.tqdm(
													    range(0		,     len(self.dataset       )		,     self.batch_size       )		,     unit='''ba'''		,     disable=not logging.is_progress_bar_enabled()		,     desc='''Creating json from Arrow format'''		,     ):
																	__a	:  int         =					self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
																	written += file_obj.write(_UpperCAmelCase       )
									else:
													__a    ,  __a	:  List[str]         =					len(self.dataset       ), self.batch_size
													with multiprocessing.Pool(self.num_proc       ) as pool:
																	for json_str in logging.tqdm(
																	    pool.imap(
																	        self._batch_json		,     [(offset, orient, lines, index, to_json_kwargs) for offset in range(0		,     _UpperCAmelCase		,     _UpperCAmelCase       )]		,     )		,     total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size		,     unit='''ba'''		,     disable=not logging.is_progress_bar_enabled()		,     desc='''Creating json from Arrow format'''		,     ):
																					written += file_obj.write(_UpperCAmelCase       )

									return written






"""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 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    '''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.'''
					)
					__lowerCAmelCase					=					'''CIDAS/clipseg-rd64-refined'''
					__lowerCAmelCase					=					'''image_segmenter'''
					__lowerCAmelCase					=					CLIPSegForImageSegmentation

					__lowerCAmelCase					=					['''image''', '''text''']
					__lowerCAmelCase					=					['''image''']







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									requires_backends(self		,     ['''vision''']       )
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					outputs.cpu().detach().numpy()
									__a	:  int         =					0
									__a	:  Optional[int]         =					1
									return Image.fromarray((array * 255).astype(np.uinta       )       )