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


__lowerCamelCase			: Optional[List[str]]							   =		None
__lowerCamelCase			: Tuple							   =		'''<''' 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
__lowerCamelCase			: Tuple							   =		[
    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 __snake_case				:
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							None
   # Automatically constructed
   lowerCAmelCase_														=							"PIL.Image.Image"
   lowerCAmelCase_														=							pa.struct({"bytes": pa.binary(), "path": pa.string()}		)
   lowerCAmelCase_														=							field(default="Image"   ,		init=lowerCamelCase_   ,		repr=lowerCamelCase_		)




   def __call__(    self			:				Tuple      ):



     """simple docstring"""






     return self.pa_type




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]      ):



     """simple docstring"""






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

     if isinstance(_lowercase       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      np.array(_lowercase      )

     if isinstance(_lowercase       ,	_lowercase      ):
       return {"path": value, "bytes": None}
     elif isinstance(_lowercase       ,	_lowercase      ):
       return {"path": None, "bytes": value}
     elif isinstance(_lowercase       ,	np.ndarray      ):
       # convert the image array to PNG/TIFF bytes
       return encode_np_array(_lowercase      )
     elif isinstance(_lowercase       ,	PIL.Image.Image      ):
       # convert the PIL image to bytes (default format is PNG/TIFF)
       return encode_pil_image(_lowercase      )
     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 	__a						(    self			:				str       ,	_lowercase			:				dict       ,	_lowercase			:				Optional[Any]=None      ):



     """simple docstring"""






     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:
       SCREAMING_SNAKE_CASE__										=      {}

     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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(_lowercase      ):
           SCREAMING_SNAKE_CASE__										=      PIL.Image.open(_lowercase      )
         else:
           SCREAMING_SNAKE_CASE__										=      path.split("""::"""      )[-1]
           try:
             SCREAMING_SNAKE_CASE__										=      string_to_dict(_lowercase       ,	config.HUB_DATASETS_URL      )["""repo_id"""]
             SCREAMING_SNAKE_CASE__										=      token_per_repo_id.get(_lowercase      )
           except ValueError:
             SCREAMING_SNAKE_CASE__										=      None
           with xopen(_lowercase       ,	"""rb"""       ,	use_auth_token=_lowercase      ) as f:
             SCREAMING_SNAKE_CASE__										=      BytesIO(f.read()      )
           SCREAMING_SNAKE_CASE__										=      PIL.Image.open(bytes_      )
     else:
       SCREAMING_SNAKE_CASE__										=      PIL.Image.open(BytesIO(bytes_      )      )
     image.load()  # to avoid "Too many open files" errors
     return image




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     from .features import Value

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




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Union[pa.StringArray, pa.StructArray, pa.ListArray]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				pa.StructArray      ):



     """simple docstring"""






     @no_op_if_value_is_null
     def path_to_bytes(_lowercase			:				Tuple      ):
       with xopen(_lowercase       ,	"""rb"""      ) as f:
         SCREAMING_SNAKE_CASE__										=      f.read()
       return bytes_

     SCREAMING_SNAKE_CASE__										=      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()       ,	)
     SCREAMING_SNAKE_CASE__										=      pa.array(
         [os.path.basename(_lowercase      ) if path is not None else None for path in storage.field("""path"""      ).to_pylist()]       ,	type=pa.string()       ,	)
     SCREAMING_SNAKE_CASE__										=      pa.StructArray.from_arrays([bytes_array, path_array]       ,	["""bytes""", """path"""]       ,	mask=bytes_array.is_null()      )
     return array_cast(_lowercase       ,	self.pa_type      )





def 	__SCREAMING_SNAKE_CASE  (					)							->					List[str]:







  """simple docstring"""





  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()
    SCREAMING_SNAKE_CASE__										=      list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
  return _IMAGE_COMPRESSION_FORMATS



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       "PIL.Image.Image"	)							->					bytes:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      BytesIO()
  if image.format in list_image_compression_formats():
    SCREAMING_SNAKE_CASE__										=      image.format
  else:
    SCREAMING_SNAKE_CASE__										=      """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
  image.save(__UpperCamelCase ,     format=__UpperCamelCase	)
  return buffer.getvalue()



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       "PIL.Image.Image"	)							->					dict:







  """simple docstring"""





  if hasattr(__UpperCamelCase ,     """filename"""	) and image.filename != "":
    return {"path": image.filename, "bytes": None}
  else:
    return {"path": None, "bytes": image_to_bytes(__UpperCamelCase	)}



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       np.ndarray	)							->					dict:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__										=      array.dtype
  SCREAMING_SNAKE_CASE__										=      dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
  SCREAMING_SNAKE_CASE__										=      dtype.kind
  SCREAMING_SNAKE_CASE__										=      dtype.itemsize

  SCREAMING_SNAKE_CASE__										=      None

  # Multi-channel array case (only np.dtype("|u1") is allowed)
  if array.shape[2:]:
    SCREAMING_SNAKE_CASE__										=      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:
    SCREAMING_SNAKE_CASE__										=      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:
      SCREAMING_SNAKE_CASE__										=      dtype_byteorder + dtype_kind + str(__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      np.dtype(__UpperCamelCase	)
      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}"""	)
  SCREAMING_SNAKE_CASE__										=      PIL.Image.fromarray(array.astype(__UpperCamelCase	)	)
  return {"path": None, "bytes": image_to_bytes(__UpperCamelCase	)}






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]	)							->					List[dict]:







  """simple docstring"""





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

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

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


__lowerCamelCase			: Union[str, Any]							   =		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
   print('''Torch version:''', None)

try:
   import transformers

   print('''transformers version:''', transformers.__version__)
except ImportError:
   print('''transformers version:''', None)
