



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


SCREAMING_SNAKE_CASE__       =      None
SCREAMING_SNAKE_CASE__       =      '''<''' 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
SCREAMING_SNAKE_CASE__       =      [
    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      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		List[Any]			       =    True
					A__				:		Optional[Any]			       =    None
					# Automatically constructed
					A__				:		Any			       =    "PIL.Image.Image"
					A__				:		str			       =    pa.struct({"bytes": pa.binary(), "path": pa.string()}      )
					A__				:		Tuple			       =    field(default="Image"							,     init=snake_case__							,     repr=snake_case__      )



					def __call__(     self					:       Tuple					):






								"""simple docstring"""


								return self.pa_type



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


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

								if isinstance(lowercase_				,					lowercase_					):
											A__       =    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				,					_snake_case					:       str				,					_snake_case					:       Dict=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:
											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(lowercase_					):
																	A__       =    PIL.Image.open(lowercase_					)
														else:
																	A__       =    path.split('::'					)[-1]
																	try:
																				A__       =    string_to_dict(lowercase_				,					config.HUB_DATASETS_URL					)["repo_id"]
																				A__       =    token_per_repo_id.get(lowercase_					)
																	except ValueError:
																				A__       =    None
																	with xopen(lowercase_				,					'rb'				,					use_auth_token=lowercase_					) 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     _a							(     self					:       int					):






								"""simple docstring"""


								from .features import Value

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



					def     _a							(     self					:       List[str]				,					_snake_case					:       Tuple					):






								"""simple docstring"""


								if pa.types.is_string(storage.type					):
											A__       =    pa.array([None] * len(lowercase_					)				,					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(lowercase_					)				,					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(lowercase_					)				,					type=pa.binary()					)
											if storage.type.get_field_index('path'					) >= 0:
														A__       =    storage.field('path'					)
											else:
														A__       =    pa.array([None] * len(lowercase_					)				,					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(lowercase_					)					)['bytes'] if arr is not None else None for arr in storage.to_pylist()]				,					type=pa.binary()				,					)
											A__       =    pa.array([None] * len(lowercase_					)				,					type=pa.string()					)
											A__       =    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					:       List[str]				,					_snake_case					:       Tuple					):






								"""simple docstring"""


								@no_op_if_value_is_null
								def path_to_bytes(_snake_case					:       Optional[Any]					):
											with xopen(lowercase_				,					'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(lowercase_					) 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(lowercase_				,					self.pa_type					)







def A						(       )						->      Optional[int]:






			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 A						(       __UpperCamelCase					)						->      Tuple:
			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(__SCREAMING_SNAKE_CASE ,	format=__SCREAMING_SNAKE_CASE					)
			return buffer.getvalue()


def A						(       __UpperCamelCase					)						->      Optional[int]:






			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 A						(       __UpperCamelCase					)						->      Optional[int]:






			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(__SCREAMING_SNAKE_CASE					)
									A__       =    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}'''					)
			A__       =    PIL.Image.fromarray(array.astype(__SCREAMING_SNAKE_CASE					)					)
			return {"path": None, "bytes": image_to_bytes(__SCREAMING_SNAKE_CASE					)}






def A						(       __UpperCamelCase					)						->      int:






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

			if objs:
						A__       =    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					):
									A__       =    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					):
									A__       =    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









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       =      {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




