





'''simple docstring'''

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 					a_     :

    '''simple docstring'''
    UpperCamelCase     = True
    UpperCamelCase     = None
    # Automatically constructed
    UpperCamelCase     = "PIL.Image.Image"
    UpperCamelCase     = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}       )
    UpperCamelCase     = field(default='''Image'''      ,    init=snake_case_      ,    repr=snake_case_       )


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


    def 			snake_case_(		self ,		A       )		->   dict:
        if config.PIL_AVAILABLE:
            import PIL.Image
        else:
            raise ImportError("""To support encoding images, please install 'Pillow'."""       )

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if isinstance(A ,		A       ):
            return {"path": value, "bytes": None}
        elif isinstance(A ,		A       ):
            return {"path": None, "bytes": value}
        elif isinstance(A ,		np.ndarray       ):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(A       )
        elif isinstance(A ,		PIL.Image.Image       ):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(A       )
        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 ,		A ,		A=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:
            _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(A       ):
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(A       )
                else:
                    _SCREAMING_SNAKE_CASE									=			path.split("""::"""       )[-1]
                    try:
                        _SCREAMING_SNAKE_CASE									=			string_to_dict(A ,		config.HUB_DATASETS_URL       )["""repo_id"""]
                        _SCREAMING_SNAKE_CASE									=			token_per_repo_id.get(A       )
                    except ValueError:
                        _SCREAMING_SNAKE_CASE									=			None
                    with xopen(A ,		"""rb""" ,		use_auth_token=A       ) 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 			snake_case_(		self       )		->   Union["FeatureType", Dict[str, "FeatureType"]]:
        from .features import Value

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


    def 			snake_case_(		self ,		A       )		->   pa.StructArray:
        if pa.types.is_string(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		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(A       ) ,		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(A       ) ,		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(A       ) ,		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(A       )       )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] ,		type=pa.binary() ,		)
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays(
                [bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=bytes_array.is_null()       )
        return array_cast(A ,		self.pa_type       )





    def 			snake_case_(		self ,		A       )		->   pa.StructArray:

        @no_op_if_value_is_null
        def path_to_bytes(A       ):
            with xopen(A ,		"""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(A       ) 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(A ,		self.pa_type       )





def lowerCamelCase						(     )						->List[str]:
    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 lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->bytes:
    _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(__lowerCamelCase					,       format=__lowerCamelCase    )
    return buffer.getvalue()





def lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->dict:
    if hasattr(__lowerCamelCase					,       """filename"""    ) and image.filename != "":
        return {"path": image.filename, "bytes": None}
    else:
        return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}





def lowerCamelCase						(     __lowerCamelCase :    np.ndarray    )						->dict:
    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(__lowerCamelCase    )
            _SCREAMING_SNAKE_CASE									=			np.dtype(__lowerCamelCase    )
            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(__lowerCamelCase    )    )
    return {"path": None, "bytes": image_to_bytes(__lowerCamelCase    )}






def lowerCamelCase						(     __lowerCamelCase :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )						->List[dict]:
    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(__lowerCamelCase    )
        if isinstance(__lowerCamelCase					,       __lowerCamelCase    ):
            return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
        if isinstance(__lowerCamelCase					,       np.ndarray    ):
            _SCREAMING_SNAKE_CASE									=			no_op_if_value_is_null(__lowerCamelCase    )
            return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
        elif isinstance(__lowerCamelCase					,       PIL.Image.Image    ):
            _SCREAMING_SNAKE_CASE									=			no_op_if_value_is_null(__lowerCamelCase    )
            return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
        else:
            return objs
    else:
        return objs










'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



