





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							:       str					 =	"""<""" 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							:       Optional[Any]					 =	[
    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 				a			:
				SCREAMING_SNAKE_CASE__     :   bool		      =     True
				SCREAMING_SNAKE_CASE__     :   Optional[str]		      =     None
				# Automatically constructed
				SCREAMING_SNAKE_CASE__     :   ClassVar[str]		      =     "PIL.Image.Image"
				SCREAMING_SNAKE_CASE__     :   ClassVar[Any]		      =     pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}			)
				SCREAMING_SNAKE_CASE__     :   str		      =     field(default='''Image'''   ,init=__lowercase   ,repr=__lowercase			)






				def __call__(  self					):

										"""simple docstring"""




										return self.pa_type






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




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

										if isinstance(_lowerCAmelCase			, _lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				str           =   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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=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:				str           =   {}

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										from .features import Value

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






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										if pa.types.is_string(storage.type					):
																__SCREAMING_SNAKE_CASE:				List[str]           =   pa.array([None] * len(_lowerCAmelCase					)			, type=pa.binary()					)
																__SCREAMING_SNAKE_CASE:				str           =   pa.StructArray.from_arrays([bytes_array, storage]			, ['''bytes''', '''path''']			, mask=storage.is_null()					)
										elif pa.types.is_binary(storage.type					):
																__SCREAMING_SNAKE_CASE:				Dict           =   pa.array([None] * len(_lowerCAmelCase					)			, type=pa.string()					)
																__SCREAMING_SNAKE_CASE:				List[Any]           =   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:				Dict           =   storage.field('''bytes'''					)
																else:
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   pa.array([None] * len(_lowerCAmelCase					)			, type=pa.binary()					)
																if storage.type.get_field_index('''path'''					) >= 0:
																						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   storage.field('''path'''					)
																else:
																						__SCREAMING_SNAKE_CASE:				int           =   pa.array([None] * len(_lowerCAmelCase					)			, type=pa.string()					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   pa.StructArray.from_arrays([bytes_array, path_array]			, ['''bytes''', '''path''']			, mask=storage.is_null()					)
										elif pa.types.is_list(storage.type					):
																__SCREAMING_SNAKE_CASE:				List[str]           =   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()			, )
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pa.array([None] * len(_lowerCAmelCase					)			, type=pa.string()					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   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_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										@no_op_if_value_is_null
										def path_to_bytes(_lowerCAmelCase					):
																with xopen(_lowerCAmelCase			, '''rb'''					) as f:
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   f.read()
																return bytes_

										__SCREAMING_SNAKE_CASE:				List[Any]           =   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:				Tuple           =   pa.array(
										    [os.path.basename(_lowerCAmelCase					) if path is not None else None for path in storage.field('''path'''					).to_pylist()]			, type=pa.string()			, )
										__SCREAMING_SNAKE_CASE:				str           =   pa.StructArray.from_arrays([bytes_array, path_array]			, ['''bytes''', '''path''']			, mask=bytes_array.is_null()					)
										return array_cast(_lowerCAmelCase			, self.pa_type					)






def 		lowerCAmelCase  (	)      ->    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:				int           =   list(set(PIL.Image.OPEN.keys()					) & set(PIL.Image.SAVE.keys()					)					)
						return _IMAGE_COMPRESSION_FORMATS






def 		lowerCAmelCase  (	UpperCamelCase__   :   "PIL.Image.Image"					)      ->    bytes:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   BytesIO()
						if image.format in list_image_compression_formats():
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   image.format
						else:
												__SCREAMING_SNAKE_CASE:				str           =   '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
						image.save(UpperCamelCase__       ,			format=UpperCamelCase__					)
						return buffer.getvalue()






def 		lowerCAmelCase  (	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 		lowerCAmelCase  (	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:				int           =   array.dtype
						__SCREAMING_SNAKE_CASE:				List[Any]           =   dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   dtype.kind
						__SCREAMING_SNAKE_CASE:				Tuple           =   dtype.itemsize

						__SCREAMING_SNAKE_CASE:				Optional[int]           =   None

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






def 		lowerCAmelCase  (	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:				Optional[int]           =   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:				Any           =   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:				List[Any]           =   no_op_if_value_is_null(UpperCamelCase__					)
																		return [obj_to_image_dict_func(UpperCamelCase__					) for obj in objs]
												else:
																		return objs
						else:
												return objs










import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict





@dataclass
class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ComputeEnvironment.AMAZON_SAGEMAKER
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     True
				SCREAMING_SNAKE_CASE__     :   int		      =     '''ml.p3.2xlarge'''
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''accelerate_sagemaker_execution_role'''
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''hf-sm'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     '''us-east-1'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     1
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''accelerate-sagemaker-1'''
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''1.6'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''4.4'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''train.py'''
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''False''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''--do_test''',
				    '''False''',
				    '''--do_predict''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]





class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
										assert isinstance(converted_args['''model_name_or_path''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''do_train''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''epochs''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''learning_rate''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''max_steps''']			, _lowerCAmelCase					)

										with pytest.raises(_lowerCAmelCase					):
																_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)



