




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		:				Dict       			=		'<' 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		:				List[str]       			=		[
    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__							:
				"""simple docstring"""



				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=_UpperCamelCase    ,       repr=_UpperCamelCase   )





				def __call__(      self				:     Any							)					->     List[str]:
							return self.pa_type





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]							)					->     dict:
							if config.PIL_AVAILABLE:
										import PIL.Image
							else:
										raise ImportError("To support encoding images, please install 'Pillow'."							)

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
										a    =			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				:     Any					,      __lowerCamelCase				:     dict					,      __lowerCamelCase				:     Tuple=None							)					->     "PIL.Image.Image":
							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:
										a    =			{}

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





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Union["FeatureType", Dict[str, "FeatureType"]]:
							from .features import Value

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





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Union[pa.StringArray, pa.StructArray, pa.ListArray]							)					->     pa.StructArray:
							if pa.types.is_string(storage.type							):
										a    =			pa.array([None] * len(__lowerCamelCase							)					,      type=pa.binary()							)
										a    =			pa.StructArray.from_arrays([bytes_array, storage]					,      ["bytes", "path"]					,      mask=storage.is_null()							)
							elif pa.types.is_binary(storage.type							):
										a    =			pa.array([None] * len(__lowerCamelCase							)					,      type=pa.string()							)
										a    =			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:
													a    =			storage.field("bytes"							)
										else:
													a    =			pa.array([None] * len(__lowerCamelCase							)					,      type=pa.binary()							)
										if storage.type.get_field_index("path"							) >= 0:
													a    =			storage.field("path"							)
										else:
													a    =			pa.array([None] * len(__lowerCamelCase							)					,      type=pa.string()							)
										a    =			pa.StructArray.from_arrays([bytes_array, path_array]					,      ["bytes", "path"]					,      mask=storage.is_null()							)
							elif pa.types.is_list(storage.type							):
										a    =			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()					,      )
										a    =			pa.array([None] * len(__lowerCamelCase							)					,      type=pa.string()							)
										a    =			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				:     int					,      __lowerCamelCase				:     pa.StructArray							)					->     pa.StructArray:

							@no_op_if_value_is_null
							def path_to_bytes(__lowerCamelCase				:     str							):
										with xopen(__lowerCamelCase					,      "rb"							) as f:
													a    =			f.read()
										return bytes_

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




def 			__magic_name__				( ):






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




def 			__magic_name__				( A		:					"PIL.Image.Image"   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					"PIL.Image.Image"   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					np.ndarray   ):






			'''simple docstring'''







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

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

			a    =			None

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






def 			__magic_name__				( A		:					Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]   ):






			'''simple docstring'''







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

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






