




"""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   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						UpperCamelCase_ = False				,						UpperCamelCase_ = False				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       List[Any]:
					super().__init__(
					    UpperCamelCase_				,						split=UpperCamelCase_				,						features=UpperCamelCase_				,						cache_dir=UpperCamelCase_				,						keep_in_memory=UpperCamelCase_				,						streaming=UpperCamelCase_				,						num_proc=UpperCamelCase_				,						**UpperCamelCase_				,						)
					__lowercase			:  List[Any]								=       field
					__lowercase			:  List[str]								=       path_or_paths if isinstance(UpperCamelCase_				,						UpperCamelCase_       ) else {self.split: path_or_paths}
					__lowercase			:  Dict								=       Json(
					    cache_dir=UpperCamelCase_				,						data_files=UpperCamelCase_				,						features=UpperCamelCase_				,						field=UpperCamelCase_				,						**UpperCamelCase_				,						)
		def 	_lowerCamelCase							(						self       )						->       str:
					# Build iterable dataset
					if self.streaming:
								__lowercase			:  Any								=       self.builder.as_streaming_dataset(split=self.split       )
					# Build regular (map-style) dataset
					else:
								__lowercase			:  List[Any]								=       None
								__lowercase			:  Any								=       None
								__lowercase			:  Union[str, Any]								=       None
								__lowercase			:  Optional[int]								=       None

								self.builder.download_and_prepare(
								    download_config=UpperCamelCase_				,						download_mode=UpperCamelCase_				,						verification_mode=UpperCamelCase_				,						base_path=UpperCamelCase_				,						num_proc=self.num_proc				,						)
								__lowercase			:  str								=       self.builder.as_dataset(
								    split=self.split				,						verification_mode=UpperCamelCase_				,						in_memory=self.keep_in_memory       )
					return dataset


class   UpperCAmelCase_    :
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = None				,						UpperCamelCase_ = None				,						**UpperCamelCase_				,						)						->       str:
					if num_proc is not None and num_proc <= 0:
								raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""       )

					__lowercase			:  Any								=       dataset
					__lowercase			:  Dict								=       path_or_buf
					__lowercase			:  Any								=       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					__lowercase			:  List[str]								=       num_proc
					__lowercase			:  Optional[Any]								=       '''utf-8'''
					__lowercase			:  Tuple								=       to_json_kwargs
		def 	_lowerCamelCase							(						self       )						->       int:
					__lowercase			:  str								=       self.to_json_kwargs.pop('''path_or_buf'''				,						UpperCamelCase_       )
					__lowercase			:  str								=       self.to_json_kwargs.pop('''orient'''				,						'''records'''       )
					__lowercase			:  List[Any]								=       self.to_json_kwargs.pop('''lines'''				,						True if orient == '''records''' else False       )
					__lowercase			:  Any								=       self.to_json_kwargs.pop('''index'''				,						False if orient in ['''split''', '''table'''] else True       )
					__lowercase			:  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:
											__lowercase			:  List[str]								=       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.'''       )
								__lowercase			:  Tuple								=       self._write(
								    file_obj=self.path_or_buf				,						orient=UpperCamelCase_				,						lines=UpperCamelCase_				,						index=UpperCamelCase_				,						**self.to_json_kwargs       )
					return written
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       List[str]:
					__lowercase			,__lowercase			,__lowercase			,__lowercase			,__lowercase			:  Dict								=       args

					__lowercase			:  Union[str, Any]								=       query_table(
					    table=self.dataset.data				,						key=slice(UpperCamelCase_				,						offset + self.batch_size       )				,						indices=self.dataset._indices				,						)
					__lowercase			:  Optional[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_				,						)						->       int:
					__lowercase			:  Dict								=       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'''				,						):
											__lowercase			:  str								=       self._batch_json((offset, orient, lines, index, to_json_kwargs)       )
											written += file_obj.write(UpperCamelCase_       )
					else:
								__lowercase			,__lowercase			:  Dict								=       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'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:






 class    __magic_name__				:






        @staticmethod
        def      lowerCAmelCase						(     *snake_case    ,				**snake_case)		->    str:




           '''simple docstring'''
           pass





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =hashlib.mda(image.tobytes()				)
   return m.hexdigest()[:1_0]





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =np.array(__lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =npimg.shape
   return {"hash": hashimage(__lowerCamelCase				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    __magic_name__				(						unittest.TestCase							):
       UpperCAmelCase		 =dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase		 =dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =MaskGenerationPipeline(model=snake_case    ,				image_processor=snake_case)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF')
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          pass






       @slow
       @require_torch
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =pipeline('mask-generation'    ,				model='facebook/sam-vit-huge')

          _UpperCAmelCase  :  Union[str, Any]				       =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  str				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
              ]    ,				)
          # fmt: on






       @require_torch
       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       ='facebook/sam-vit-huge'
          _UpperCAmelCase  :  Optional[int]				       =pipeline('mask-generation'    ,				model=snake_case)

          _UpperCAmelCase  :  str				       =image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'    ,				pred_iou_thresh=1    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  Tuple				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
              ]    ,				)

