
'''simple docstring'''





import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCAmelCase            = 1_000

class     a :




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int=1.2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="g",     SCREAMING_SNAKE_CASE_						:					Any=0.5,     SCREAMING_SNAKE_CASE_						:					int="-",     SCREAMING_SNAKE_CASE_						:					Dict="test_out.jpg",     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					str=0.7,     ):
										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Dict			=     img.numpy().astype('''np.uint8'''   )
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[Any]			=     img_tensorize(SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray   )

										snake_case,       snake_case							:  Optional[Any]			=     img.shape[1], img.shape[0]
										snake_case							:  int			=     mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     fig.get_dpi()
										snake_case							:  Optional[int]			=     (width * scale + 1e-2) / dpi
										snake_case							:  List[Any]			=     (height * scale + 1e-2) / dpi
										fig.set_size_inches(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     fig.add_axes([0.0, 0.0, 1.0, 1.0]   )
										ax.axis('''off'''   )
										ax.set_xlim(0.0,     SCREAMING_SNAKE_CASE_   )
										ax.set_ylim(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     saveas
										snake_case							:  str			=     rgb
										snake_case							:  Tuple			=     pynb
										snake_case							:  Optional[int]			=     img
										snake_case							:  List[str]			=     edgecolor
										snake_case							:  str			=     0.5
										snake_case							:  Optional[Any]			=     linestyle
										snake_case							:  Optional[Any]			=     int(np.sqrt(min(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )   ) * scale // 3   )
										snake_case							:  Any			=     width
										snake_case							:  List[str]			=     height
										snake_case							:  Optional[Any]			=     scale
										snake_case							:  int			=     fig
										snake_case							:  Optional[int]			=     ax
										snake_case							:  List[str]			=     pad
										snake_case							:  Tuple			=     idaobj
										snake_case							:  Tuple			=     idaattr
										snake_case							:  Dict			=     FigureCanvasAgg(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=None   ):
										if color is None:
																snake_case							:  int			=     self.edgecolor
										((snake_case),       (snake_case),       (snake_case),       (snake_case))							:  Optional[int]			=     box
										snake_case							:  List[Any]			=     xa - xa
										snake_case							:  Optional[int]			=     ya - ya
										self.ax.add_patch(
										    mpl.patches.Rectangle(
										        (xa, ya),     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     fill=SCREAMING_SNAKE_CASE_,     edgecolor=SCREAMING_SNAKE_CASE_,     linewidth=self.font_size // 3,     alpha=self.alpha,     linestyle=self.linestyle,     )   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=None   ):
										if len(boxes.shape   ) > 2:
																snake_case							:  Dict			=     boxes[0]
										if len(obj_ids.shape   ) > 1:
																snake_case							:  Optional[int]			=     obj_ids[0]
										if len(obj_scores.shape   ) > 1:
																snake_case							:  str			=     obj_scores[0]
										if len(attr_ids.shape   ) > 1:
																snake_case							:  int			=     attr_ids[0]
										if len(attr_scores.shape   ) > 1:
																snake_case							:  Union[str, Any]			=     attr_scores[0]
										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     boxes.numpy()
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  str			=     np.array(SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray   )
										snake_case							:  Optional[int]			=     np.prod(boxes[:, 2:] - boxes[:, :2],     axis=1   )
										snake_case							:  str			=     np.argsort(-areas   ).tolist()
										snake_case							:  Union[str, Any]			=     boxes[sorted_idxs] if boxes is not None else None
										snake_case							:  Dict			=     obj_ids[sorted_idxs] if obj_ids is not None else None
										snake_case							:  Tuple			=     obj_scores[sorted_idxs] if obj_scores is not None else None
										snake_case							:  List[str]			=     attr_ids[sorted_idxs] if attr_ids is not None else None
										snake_case							:  Optional[int]			=     attr_scores[sorted_idxs] if attr_scores is not None else None

										snake_case							:  Union[str, Any]			=     [self._random_color(maximum=1   ) for _ in range(len(SCREAMING_SNAKE_CASE_   )   )]
										snake_case							:  List[Any]			=     [assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
																snake_case							:  Dict			=     self._create_text_labels_attr(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																for i in range(len(SCREAMING_SNAKE_CASE_   )   ):
																						snake_case							:  Any			=     assigned_colors[i]
																						self.add_box(boxes[i],     SCREAMING_SNAKE_CASE_   )
																						self.draw_labels(labels[i],     boxes[i],     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case,       snake_case,       snake_case,       snake_case							:  Dict			=     box
										snake_case							:  Union[str, Any]			=     (xa, ya)
										snake_case							:  Any			=     (ya - ya) * (xa - xa)
										snake_case							:  Any			=     _SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 40 * self.scale:
																if ya >= self.height - 5:
																						snake_case							:  Union[str, Any]			=     (xa, ya)
																else:
																						snake_case							:  Dict			=     (xa, ya)

										snake_case							:  List[str]			=     (ya - ya) / np.sqrt(self.height * self.width   )
										snake_case							:  Union[str, Any]			=     self._change_color_brightness(SCREAMING_SNAKE_CASE_,     brightness_factor=0.7   )
										snake_case							:  int			=     np.clip((height_ratio - 0.02) / 0.08 + 1,     1.2,     2   )
										font_size *= 0.75 * self.font_size

										self.draw_text(
										    text=SCREAMING_SNAKE_CASE_,     position=SCREAMING_SNAKE_CASE_,     color=SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str]="g",     SCREAMING_SNAKE_CASE_						:					List[str]="left",     ):
										snake_case							:  Optional[int]			=     0
										snake_case							:  Tuple			=     self.font_size
										snake_case							:  Tuple			=     np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE_   )   ),     0.2   )
										snake_case							:  Dict			=     max(0.8,     np.max(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[Any]			=     {
										    '''facecolor''': '''black''',
										    '''alpha''': self.alpha,
										    '''pad''': self.pad,
										    '''edgecolor''': '''none''',
										}
										snake_case,       snake_case							:  str			=     position
										self.ax.text(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     size=font_size * self.scale,     family='''sans-serif''',     bbox=SCREAMING_SNAKE_CASE_,     verticalalignment='''top''',     horizontalalignment=SCREAMING_SNAKE_CASE_,     color=SCREAMING_SNAKE_CASE_,     zorder=10,     rotation=SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										if saveas is None:
																snake_case							:  List[str]			=     self.saveas
										if saveas.lower().endswith('''.jpg'''   ) or saveas.lower().endswith('''.png'''   ):
																cva.imwrite(
																    SCREAMING_SNAKE_CASE_,     self._get_buffer()[:, :, ::-1],     )
										else:
																self.fig.savefig(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  int			=     [self.idaobj[i] for i in classes]
										snake_case							:  str			=     [self.idaattr[i] for i in attr_classes]
										snake_case							:  Optional[Any]			=     [
										    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
										    for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										]
										return labels




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										snake_case							:  Union[str, Any]			=     [self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						snake_case							:  Optional[Any]			=     ['''{:.0f}%'''.format(s * 1_00   ) for s in scores]
																else:
																						snake_case							:  str			=     ['''{} {:.0f}%'''.format(SCREAMING_SNAKE_CASE_,     s * 1_00   ) for li, s in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )]
										return labels




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=2_55   ):
										snake_case							:  Union[str, Any]			=     np.random.randint(0,     len(_COLORS   )   )
										snake_case							:  Optional[int]			=     _COLORS[idx] * maximum
										if not self.rgb:
																snake_case							:  str			=     ret[::-1]
										return ret




				def 						__snake_case				(				self						:					List[Any]   ):
										if not self.pynb:
																snake_case,       snake_case							:  int			=     self.canvas.print_to_buffer()
																if (width, height) != (self.width, self.height):
																						snake_case							:  List[Any]			=     cva.resize(self.img,     (width, height)   )
																else:
																						snake_case							:  Tuple			=     self.img
										else:
																snake_case							:  List[str]			=     io.BytesIO()  # works for cairo backend
																self.canvas.print_rgba(SCREAMING_SNAKE_CASE_   )
																snake_case,       snake_case							:  Union[str, Any]			=     self.width, self.height
																snake_case							:  Optional[Any]			=     buf.getvalue()
																snake_case							:  List[Any]			=     self.img

										snake_case							:  Union[str, Any]			=     np.frombuffer(SCREAMING_SNAKE_CASE_,     dtype='''uint8'''   )
										snake_case							:  Union[str, Any]			=     buffer.reshape(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     4   )
										snake_case,       snake_case							:  Any			=     np.split(SCREAMING_SNAKE_CASE_,     [3],     axis=2   )

										try:
																import numexpr as ne  # fuse them with numexpr

																snake_case							:  str			=     ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''   )
										except ImportError:
																snake_case							:  Dict			=     alpha.astype('''float32'''   ) / 255.0
																snake_case							:  Tuple			=     img * (1 - alpha) + rgb * alpha

										return visualized_image.astype('''uint8'''   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Any   ):
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										snake_case							:  Any			=     mplc.to_rgb(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  Union[str, Any]			=     polygon_color[1] + (brightness_factor * polygon_color[1])
										snake_case							:  List[Any]			=     0.0 if modified_lightness < 0.0 else modified_lightness
										snake_case							:  Dict			=     1.0 if modified_lightness > 1.0 else modified_lightness
										snake_case							:  Dict			=     colorsys.hls_to_rgb(polygon_color[0],     SCREAMING_SNAKE_CASE_,     polygon_color[2]   )
										return modified_color




UpperCAmelCase            = (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)









'''simple docstring'''







import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def     _UpperCamelCase				(  snake_case__				,      **snake_case__				):
      return RunBeamCommand(
          args.dataset				,      args.name				,      args.cache_dir				,      args.beam_pipeline_options				,      args.data_dir				,      args.all_configs				,      args.save_info or args.save_infos				,      args.ignore_verifications				,      args.force_redownload				,      **snake_case__				,      )






class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     @staticmethod
     def      _UpperCamelCase (lowercase__     :						ArgumentParser       )	->							List[Any]:
           __A		 =   parser.add_parser("run_beam"					,					help="Run a Beam dataset processing pipeline"       )
           run_beam_parser.add_argument("dataset"					,					type=lowercase__					,					help="Name of the dataset to download"       )
           run_beam_parser.add_argument("--name"					,					type=lowercase__					,					default=lowercase__					,					help="Dataset config name"       )
           run_beam_parser.add_argument(
               "--cache_dir"					,					type=lowercase__					,					default=lowercase__					,					help="Cache directory where the datasets are stored"					,					)
           run_beam_parser.add_argument(
               "--beam_pipeline_options"					,					type=lowercase__					,					default=""					,					help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"					,					)
           run_beam_parser.add_argument(
               "--data_dir"					,					type=lowercase__					,					default=lowercase__					,					help="Can be used to specify a manual directory to get the files from"					,					)
           run_beam_parser.add_argument("--all_configs"					,					action="store_true"					,					help="Test all dataset configurations"       )
           run_beam_parser.add_argument("--save_info"					,					action="store_true"					,					help="Save the dataset infos file"       )
           run_beam_parser.add_argument(
               "--ignore_verifications"					,					action="store_true"					,					help="Run the test without checksums and splits checks"       )
           run_beam_parser.add_argument("--force_redownload"					,					action="store_true"					,					help="Force dataset redownload"       )
           # aliases
           run_beam_parser.add_argument("--save_infos"					,					action="store_true"					,					help="alias for save_info"       )
           run_beam_parser.set_defaults(func=lowercase__       )






     def __init__(self     :						Any					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						str					,					lowercase__     :						bool					,					lowercase__     :						bool					,					lowercase__     :						bool					,					lowercase__     :						bool					,					**lowercase__     :						Tuple					,					)	->							List[Any]:
           __A		 =   dataset
           __A		 =   name
           __A		 =   cache_dir
           __A		 =   beam_pipeline_options
           __A		 =   data_dir
           __A		 =   all_configs
           __A		 =   save_infos
           __A		 =   ignore_verifications
           __A		 =   force_redownload
           __A		 =   config_kwargs






     def      _UpperCamelCase (self     :						int       )	->							Dict:
           import apache_beam as beam

           if self._name is not None and self._all_configs:
                 print("Both parameters `name` and `all_configs` can't be used at once."       )
                 exit(1       )
           __A ,					__A		 =   self._dataset, self._name
           __A		 =   dataset_module_factory(lowercase__       )
           __A		 =   import_main_class(dataset_module.module_path       )
           __A		 =   []
           if self._beam_pipeline_options:
                 __A		 =   beam.options.pipeline_options.PipelineOptions(
                     flags=[f"--{opt.strip()}" for opt in self._beam_pipeline_options.split(","       ) if opt]       )
           else:
                 __A		 =   None
           if self._all_configs and len(builder_cls.BUILDER_CONFIGS       ) > 0:
                 for builder_config in builder_cls.BUILDER_CONFIGS:
                       builders.append(
                           builder_cls(
                               config_name=builder_config.name					,					data_dir=self._data_dir					,					hash=dataset_module.hash					,					beam_options=lowercase__					,					cache_dir=self._cache_dir					,					base_path=dataset_module.builder_kwargs.get("base_path"       )					,					)       )
           else:
                 builders.append(
                     builder_cls(
                         config_name=lowercase__					,					data_dir=self._data_dir					,					beam_options=lowercase__					,					cache_dir=self._cache_dir					,					base_path=dataset_module.builder_kwargs.get("base_path"       )					,					**self._config_kwargs					,					)       )

           for builder in builders:
                 builder.download_and_prepare(
                     download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                     if not self._force_redownload
                     else DownloadMode.FORCE_REDOWNLOAD					,					download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH       )					,					verification_mode=VerificationMode.NO_CHECKS
                     if self._ignore_verifications
                     else VerificationMode.ALL_CHECKS					,					try_from_hf_gcs=lowercase__					,					)
                 if self._save_infos:
                       builder._save_infos()

           print("Apache beam run successful."       )

           # If save_infos=True, the dataset infos file is created next to the loaded module file.
           # Let's move it to the original directory of the dataset script, to allow the user to
           # upload them on S3 at the same time afterwards.
           if self._save_infos:
                 __A		 =   os.path.join(builder_cls.get_imported_module_dir()					,					config.DATASETDICT_INFOS_FILENAME       )

                 __A		 =   Path(lowercase__       ).name + ".py"

                 __A		 =   os.path.join(lowercase__					,					lowercase__       )
                 if os.path.isfile(lowercase__       ):
                       __A		 =   os.path.dirname(lowercase__       )
                 elif os.path.isfile(lowercase__       ):
                       __A		 =   path
                 else:  # in case of a remote dataset
                       print(f"Dataset Infos file saved at {dataset_infos_path}"       )
                       exit(1       )

                 # Move datasetinfo back to the user
                 __A		 =   os.path.join(lowercase__					,					config.DATASETDICT_INFOS_FILENAME       )
                 copyfile(lowercase__					,					lowercase__       )
                 print(f"Dataset Infos file saved at {user_dataset_infos_path}"       )


