




'''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


lowerCAmelCase__										= None
lowerCAmelCase__										= "<" 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__										= [
    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 					lowercase						:
							_lowerCamelCase		:      Tuple=   True
							_lowerCamelCase		:      Optional[Any]=   None
							# Automatically constructed
							_lowerCamelCase		:      Any=   "PIL.Image.Image"
							_lowerCamelCase		:      Optional[Any]=   pa.struct({"bytes": pa.binary(), "path": pa.string()} )
							_lowerCamelCase		:      str=   field(default="Image",					init=_A,					repr=_A )


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


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

								if isinstance(__lowerCamelCase    ,			__lowerCamelCase):
									UpperCAmelCase_		:      Tuple       	=					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    ,			_snake_case    ,			_snake_case=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:
									UpperCAmelCase_		:      List[Any]       	=					{}

								UpperCAmelCase_		:      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):
											UpperCAmelCase_		:      List[Any]       	=					PIL.Image.open(__lowerCamelCase)
										else:
											UpperCAmelCase_		:      Any       	=					path.split('::')[-1]
											try:
												UpperCAmelCase_		:      int       	=					string_to_dict(__lowerCamelCase    ,			config.HUB_DATASETS_URL)["repo_id"]
												UpperCAmelCase_		:      Tuple       	=					token_per_repo_id.get(__lowerCamelCase)
											except ValueError:
												UpperCAmelCase_		:      Any       	=					None
											with xopen(__lowerCamelCase    ,			'rb'    ,			use_auth_token=__lowerCamelCase) as f:
												UpperCAmelCase_		:      Optional[Any]       	=					BytesIO(f.read())
											UpperCAmelCase_		:      List[Any]       	=					PIL.Image.open(bytes_)
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					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    ,			_snake_case)							->				pa.StructArray:
								if pa.types.is_string(storage.type):
									UpperCAmelCase_		:      Dict       	=					pa.array([None] * len(__lowerCamelCase)    ,			type=pa.binary())
									UpperCAmelCase_		:      List[Any]       	=					pa.StructArray.from_arrays([bytes_array, storage]    ,			['bytes', 'path']    ,			mask=storage.is_null())
								elif pa.types.is_binary(storage.type):
									UpperCAmelCase_		:      Optional[int]       	=					pa.array([None] * len(__lowerCamelCase)    ,			type=pa.string())
									UpperCAmelCase_		:      int       	=					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:
										UpperCAmelCase_		:      Optional[int]       	=					storage.field('bytes')
									else:
										UpperCAmelCase_		:      Optional[Any]       	=					pa.array([None] * len(__lowerCamelCase)    ,			type=pa.binary())
									if storage.type.get_field_index('path') >= 0:
										UpperCAmelCase_		:      Optional[Any]       	=					storage.field('path')
									else:
										UpperCAmelCase_		:      Dict       	=					pa.array([None] * len(__lowerCamelCase)    ,			type=pa.string())
									UpperCAmelCase_		:      Optional[Any]       	=					pa.StructArray.from_arrays([bytes_array, path_array]    ,			['bytes', 'path']    ,			mask=storage.is_null())
								elif pa.types.is_list(storage.type):
									UpperCAmelCase_		:      int       	=					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()    ,			)
									UpperCAmelCase_		:      Dict       	=					pa.array([None] * len(__lowerCamelCase)    ,			type=pa.string())
									UpperCAmelCase_		:      List[str]       	=					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    ,			_snake_case)							->				pa.StructArray:

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

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







def 						SCREAMING_SNAKE_CASE(  )    ->    List[Any]:
	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()
		UpperCAmelCase_		:      Any       	=					list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
	return _IMAGE_COMPRESSION_FORMATS







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_		:      int       	=					BytesIO()
	if image.format in list_image_compression_formats():
		UpperCAmelCase_		:      str       	=					image.format
	else:
		UpperCAmelCase_		:      List[Any]       	=					"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
	image.save(_lowerCamelCase   ,format=_lowerCamelCase  )
	return buffer.getvalue()







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Dict:
	if hasattr(_lowerCamelCase   ,'filename'  ) and image.filename != "":
		return {"path": image.filename, "bytes": None}
	else:
		return {"path": None, "bytes": image_to_bytes(_lowerCamelCase  )}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[int]:
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError('To support encoding images, please install \'Pillow\'.'  )

	UpperCAmelCase_		:      Tuple       	=					array.dtype
	UpperCAmelCase_		:      Union[str, Any]       	=					dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
	UpperCAmelCase_		:      Union[str, Any]       	=					dtype.kind
	UpperCAmelCase_		:      List[Any]       	=					dtype.itemsize

	UpperCAmelCase_		:      int       	=					None

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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	if config.PIL_AVAILABLE:
		import PIL.Image
	else:
		raise ImportError('To support encoding images, please install \'Pillow\'.'  )

	if objs:
		UpperCAmelCase_		:      Dict       	=					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  ):
			UpperCAmelCase_		:      int       	=					no_op_if_value_is_null(_lowerCamelCase  )
			return [obj_to_image_dict_func(_lowerCamelCase  ) for obj in objs]
		elif isinstance(_lowerCamelCase   ,PIL.Image.Image  ):
			UpperCAmelCase_		:      Optional[Any]       	=					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 argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

						parser.add_argument(
						    "--txt2img_unclip",
						    default="kakaobrain/karlo-v1-alpha",
						    type=str,
						    required=False,
						    help="The pretrained txt2img unclip.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= UnCLIPImageVariationPipeline(
						    decoder=txtaimg.decoder,
						    text_encoder=txtaimg.text_encoder,
						    tokenizer=txtaimg.tokenizer,
						    text_proj=txtaimg.text_proj,
						    feature_extractor=feature_extractor,
						    image_encoder=image_encoder,
						    super_res_first=txtaimg.super_res_first,
						    super_res_last=txtaimg.super_res_last,
						    decoder_scheduler=txtaimg.decoder_scheduler,
						    super_res_scheduler=txtaimg.super_res_scheduler,
						)

						imgaimg.save_pretrained(args.dump_path)






