'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
 import PIL.Image

 from .features import FeatureType


A_     :						Optional[List[str]]    							=None
A_     :						List[Any]    							='''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
A_     :						int    							=[
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]


@dataclass
class     __UpperCAmelCase       :
  __A :				bool											= True
  __A :				Optional[str]											= None
  # Automatically constructed
  __A :				ClassVar[str]											= "PIL.Image.Image"
  __A :				ClassVar[Any]											= pa.struct({'bytes': pa.binary(), 'path': pa.string()}		)
  __A :				str											= field(default='Image'						,       init=__a						,       repr=__a		)





  def __call__(	self ):
     return self.pa_type





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     if config.PIL_AVAILABLE:
        import PIL.Image
     else:
        raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      np.array(_lowerCamelCase )

     if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
        return {"path": value, "bytes": None}
     elif isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
        return {"path": None, "bytes": value}
     elif isinstance(_lowerCamelCase    ,     np.ndarray ):
        # convert the image array to PNG/TIFF bytes
        return encode_np_array(_lowerCamelCase )
     elif isinstance(_lowerCamelCase    ,     PIL.Image.Image ):
        # convert the PIL image to bytes (default format is PNG/TIFF)
        return encode_pil_image(_lowerCamelCase )
     elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
        # we set "bytes": None to not duplicate the data if they're already available locally
        return {"bytes": None, "path": value.get('''path''' )}
     elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
        # store the image bytes, and path is used to infer the image format using the file extension
        return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
     else:
        raise ValueError(
            F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=None ):
     if not self.decode:
        raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.''' )

     if config.PIL_AVAILABLE:
        import PIL.Image
     else:
        raise ImportError('''To support decoding images, please install \'Pillow\'.''' )

     if token_per_repo_id is None:
        lowerCAmelCase_			=      {}

     lowerCAmelCase_	,lowerCAmelCase_			=      value['''path'''], value['''bytes''']
     if bytes_ is None:
        if path is None:
           raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
        else:
           if is_local_path(_lowerCamelCase ):
              lowerCAmelCase_			=      PIL.Image.open(_lowerCamelCase )
           else:
              lowerCAmelCase_			=      path.split('''::''' )[-1]
              try:
                 lowerCAmelCase_			=      string_to_dict(_lowerCamelCase    ,     config.HUB_DATASETS_URL )['''repo_id''']
                 lowerCAmelCase_			=      token_per_repo_id.get(_lowerCamelCase )
              except ValueError:
                 lowerCAmelCase_			=      None
              with xopen(_lowerCamelCase    ,     '''rb'''    ,     use_auth_token=_lowerCamelCase ) as f:
                 lowerCAmelCase_			=      BytesIO(f.read() )
              lowerCAmelCase_			=      PIL.Image.open(bytes_ )
     else:
        lowerCAmelCase_			=      PIL.Image.open(BytesIO(bytes_ ) )
     image.load()  # to avoid "Too many open files" errors
     return image





  def 						UpperCAmelCase_	(	self ):
     from .features import Value

     return (
         self
         if self.decode
         else {
             "bytes": Value('''binary''' ),
             "path": Value('''string''' ),
         }
     )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     if pa.types.is_string(storage.type ):
        lowerCAmelCase_			=      pa.array([None] * len(_lowerCamelCase )    ,     type=pa.binary() )
        lowerCAmelCase_			=      pa.StructArray.from_arrays([bytes_array, storage]    ,     ['''bytes''', '''path''']    ,     mask=storage.is_null() )
     elif pa.types.is_binary(storage.type ):
        lowerCAmelCase_			=      pa.array([None] * len(_lowerCamelCase )    ,     type=pa.string() )
        lowerCAmelCase_			=      pa.StructArray.from_arrays([storage, path_array]    ,     ['''bytes''', '''path''']    ,     mask=storage.is_null() )
     elif pa.types.is_struct(storage.type ):
        if storage.type.get_field_index('''bytes''' ) >= 0:
           lowerCAmelCase_			=      storage.field('''bytes''' )
        else:
           lowerCAmelCase_			=      pa.array([None] * len(_lowerCamelCase )    ,     type=pa.binary() )
        if storage.type.get_field_index('''path''' ) >= 0:
           lowerCAmelCase_			=      storage.field('''path''' )
        else:
           lowerCAmelCase_			=      pa.array([None] * len(_lowerCamelCase )    ,     type=pa.string() )
        lowerCAmelCase_			=      pa.StructArray.from_arrays([bytes_array, path_array]    ,     ['''bytes''', '''path''']    ,     mask=storage.is_null() )
     elif pa.types.is_list(storage.type ):
        lowerCAmelCase_			=      pa.array(
            [encode_np_array(np.array(_lowerCamelCase ) )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]    ,     type=pa.binary()    ,     )
        lowerCAmelCase_			=      pa.array([None] * len(_lowerCamelCase )    ,     type=pa.string() )
        lowerCAmelCase_			=      pa.StructArray.from_arrays(
            [bytes_array, path_array]    ,     ['''bytes''', '''path''']    ,     mask=bytes_array.is_null() )
     return array_cast(_lowerCamelCase    ,     self.pa_type )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):

     @no_op_if_value_is_null
     def path_to_bytes(_lowerCamelCase ):
        with xopen(_lowerCamelCase    ,     '''rb''' ) as f:
           lowerCAmelCase_			=      f.read()
        return bytes_

     lowerCAmelCase_			=      pa.array(
         [
             (path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
             for x in storage.to_pylist()
         ]    ,     type=pa.binary()    ,     )
     lowerCAmelCase_			=      pa.array(
         [os.path.basename(_lowerCamelCase ) if path is not None else None for path in storage.field('''path''' ).to_pylist()]    ,     type=pa.string()    ,     )
     lowerCAmelCase_			=      pa.StructArray.from_arrays([bytes_array, path_array]    ,     ['''bytes''', '''path''']    ,     mask=bytes_array.is_null() )
     return array_cast(_lowerCamelCase    ,     self.pa_type )






def snake_case_      (					)			->					List[str]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('''To support encoding images, please install \'Pillow\'.''')

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      lowerCAmelCase_			=      list(set(PIL.Image.OPEN.keys()) & set(PIL.Image.SAVE.keys()))
   return _IMAGE_COMPRESSION_FORMATS



def snake_case_      (					__snake_case  :							"PIL.Image.Image")			->					bytes:
   lowerCAmelCase_			=      BytesIO()
   if image.format in list_image_compression_formats():
      lowerCAmelCase_			=      image.format
   else:
      lowerCAmelCase_			=      '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
   image.save(__snake_case	,   format=__snake_case)
   return buffer.getvalue()



def snake_case_      (					__snake_case  :							"PIL.Image.Image")			->					dict:
   if hasattr(__snake_case	,   '''filename''') and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(__snake_case)}



def snake_case_      (					__snake_case  :							np.ndarray)			->					dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('''To support encoding images, please install \'Pillow\'.''')

   lowerCAmelCase_			=      array.dtype
   lowerCAmelCase_			=      dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
   lowerCAmelCase_			=      dtype.kind
   lowerCAmelCase_			=      dtype.itemsize

   lowerCAmelCase_			=      None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowerCAmelCase_			=      np.dtype('''|u1''')
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''')
      if dtype is not dest_dtype:
         warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''')
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      lowerCAmelCase_			=      dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         lowerCAmelCase_			=      dtype_byteorder + dtype_kind + str(__snake_case)
         lowerCAmelCase_			=      np.dtype(__snake_case)
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''')
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''')
   lowerCAmelCase_			=      PIL.Image.fromarray(array.astype(__snake_case))
   return {"path": None, "bytes": image_to_bytes(__snake_case)}






def snake_case_      (					__snake_case  :							Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]])			->					List[dict]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('''To support encoding images, please install \'Pillow\'.''')

   if objs:
      lowerCAmelCase_	,lowerCAmelCase_			=      first_non_null_value(__snake_case)
      if isinstance(__snake_case	,   __snake_case):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(__snake_case	,   np.ndarray):
         lowerCAmelCase_			=      no_op_if_value_is_null(__snake_case)
         return [obj_to_image_dict_func(__snake_case) for obj in objs]
      elif isinstance(__snake_case	,   PIL.Image.Image):
         lowerCAmelCase_			=      no_op_if_value_is_null(__snake_case)
         return [obj_to_image_dict_func(__snake_case) for obj in objs]
      else:
         return objs
   else:
      return objs






'''simple docstring'''

import gc
import threading
import time

import psutil
import torch


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      psutil.Process()
     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      -1

     while True:
        lowerCAmelCase_			=      max(self.process.memory_info().rss    ,     self.cpu_memory_peak )

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        if not self.peak_monitoring:
           break





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      threading.Thread(target=self.peak_monitor )
     lowerCAmelCase_			=      True
     self.thread.start()







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      False
     self.thread.join()
     return self.cpu_memory_peak






A_     :						List[str]    							=PeakCPUMemory()



def snake_case_      (					)			->					Tuple:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      torch.cuda.memory_allocated(__snake_case)
   torch.cuda.reset_peak_memory_stats()

   return measures



def snake_case_      (					__snake_case  :							Any)			->					List[str]:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time() - start_measures['''time''']}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
   lowerCAmelCase_			=      (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
      lowerCAmelCase_			=      (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20

   return measures






def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Optional[int])			->					Dict:
   print(F'''{description}:''')
   print(F'''- Time: {measures['time']:.2f}s''')
   for i in range(torch.cuda.device_count()):
      print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
      lowerCAmelCase_			=      measures[F'''{i}-peak''']
      print(F'''- GPU {i} peak: {peak:.2f}MiB''')
   print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
   print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')





