



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


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




				def __call__(						self					:						Tuple						):
							return self.pa_type




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

							if isinstance(__lowercase,					__lowercase						):
										lowercase__					  =				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					:						Optional[int],					__lowercase					:						dict,					__lowercase					:						Optional[Any]=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:
										lowercase__					  =				{}

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




				def 							A__      (						self					:						str						):
							from .features import Value

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




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

							@no_op_if_value_is_null
							def path_to_bytes(__lowercase					:						List[Any]						):
										with xopen(__lowercase,					"rb"						) as f:
													lowercase__					  =				f.read()
										return bytes_

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



def __lowerCAmelCase (     ):
			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()
						lowercase__					  =				list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
			return _IMAGE_COMPRESSION_FORMATS



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



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



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

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

			lowercase__					  =				None

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




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

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


lowercase_								=     {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RobertaPreLayerNormForCausalLM""",
							    """RobertaPreLayerNormForMaskedLM""",
							    """RobertaPreLayerNormForMultipleChoice""",
							    """RobertaPreLayerNormForQuestionAnswering""",
							    """RobertaPreLayerNormForSequenceClassification""",
							    """RobertaPreLayerNormForTokenClassification""",
							    """RobertaPreLayerNormModel""",
							    """RobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRobertaPreLayerNormForCausalLM""",
							    """TFRobertaPreLayerNormForMaskedLM""",
							    """TFRobertaPreLayerNormForMultipleChoice""",
							    """TFRobertaPreLayerNormForQuestionAnswering""",
							    """TFRobertaPreLayerNormForSequenceClassification""",
							    """TFRobertaPreLayerNormForTokenClassification""",
							    """TFRobertaPreLayerNormMainLayer""",
							    """TFRobertaPreLayerNormModel""",
							    """TFRobertaPreLayerNormPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """FlaxRobertaPreLayerNormForCausalLM""",
							    """FlaxRobertaPreLayerNormForMaskedLM""",
							    """FlaxRobertaPreLayerNormForMultipleChoice""",
							    """FlaxRobertaPreLayerNormForQuestionAnswering""",
							    """FlaxRobertaPreLayerNormForSequenceClassification""",
							    """FlaxRobertaPreLayerNormForTokenClassification""",
							    """FlaxRobertaPreLayerNormModel""",
							    """FlaxRobertaPreLayerNormPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




