"""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


_a					:					List[Any]=			None
_a					:					str=			"<" 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
_a					:					Dict=			[
    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 		UpperCamelCase      :






				"""simple docstring"""




				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=__snake_case				,     repr=__snake_case						)



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



				def  _lowercase					(self    :				Optional[Any]     ,  _A    :				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_):
						__snake_case      :							Optional[int]						=			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  _lowercase					(self    :				List[str]     ,  _A    :				dict     ,  _A    :				Union[str, Any]=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:
						__snake_case      :							Tuple						=			{}

					__snake_case      :							List[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_):
								__snake_case      :							Any						=			PIL.Image.open(lowerCamelCase_)
							else:
								__snake_case      :							int						=			path.split('::')[-1]
								try:
									__snake_case      :							Optional[int]						=			string_to_dict(lowerCamelCase_     ,  config.HUB_DATASETS_URL)["""repo_id"""]
									__snake_case      :							str						=			token_per_repo_id.get(lowerCamelCase_)
								except ValueError:
									__snake_case      :							Optional[int]						=			None
								with xopen(lowerCamelCase_     ,  'rb'     ,  use_auth_token=lowerCamelCase_) as f:
									__snake_case      :							Optional[int]						=			BytesIO(f.read())
								__snake_case      :							List[Any]						=			PIL.Image.open(bytes_)
					else:
						__snake_case      :							Dict						=			PIL.Image.open(BytesIO(bytes_))
					image.load()  # to avoid "Too many open files" errors
					return image



				def  _lowercase					(self    :				List[Any])   ->     Union["FeatureType", Dict[str, "FeatureType"]]:
					from .features import Value

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



				def  _lowercase					(self    :				Tuple     ,  _A    :				Union[pa.StringArray, pa.StructArray, pa.ListArray])   ->     pa.StructArray:
					if pa.types.is_string(storage.type):
						__snake_case      :							Tuple						=			pa.array([None] * len(lowerCamelCase_)     ,  type=pa.binary())
						__snake_case      :							List[Any]						=			pa.StructArray.from_arrays([bytes_array, storage]     ,  ['bytes', 'path']     ,  mask=storage.is_null())
					elif pa.types.is_binary(storage.type):
						__snake_case      :							int						=			pa.array([None] * len(lowerCamelCase_)     ,  type=pa.string())
						__snake_case      :							List[Any]						=			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:
							__snake_case      :							str						=			storage.field('bytes')
						else:
							__snake_case      :							Dict						=			pa.array([None] * len(lowerCamelCase_)     ,  type=pa.binary())
						if storage.type.get_field_index('path') >= 0:
							__snake_case      :							Optional[int]						=			storage.field('path')
						else:
							__snake_case      :							Optional[int]						=			pa.array([None] * len(lowerCamelCase_)     ,  type=pa.string())
						__snake_case      :							Optional[Any]						=			pa.StructArray.from_arrays([bytes_array, path_array]     ,  ['bytes', 'path']     ,  mask=storage.is_null())
					elif pa.types.is_list(storage.type):
						__snake_case      :							str						=			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()     ,  )
						__snake_case      :							Tuple						=			pa.array([None] * len(lowerCamelCase_)     ,  type=pa.string())
						__snake_case      :							int						=			pa.StructArray.from_arrays(
						    [bytes_array, path_array]     ,  ['bytes', 'path']     ,  mask=bytes_array.is_null())
					return array_cast(lowerCamelCase_     ,  self.pa_type)






				def  _lowercase					(self    :				Union[str, Any]     ,  _A    :				pa.StructArray)   ->     pa.StructArray:

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

					__snake_case      :							str						=			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()     ,  )
					__snake_case      :							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()     ,  )
					__snake_case      :							Optional[Any]						=			pa.StructArray.from_arrays([bytes_array, path_array]     ,  ['bytes', 'path']     ,  mask=bytes_array.is_null())
					return array_cast(lowerCamelCase_     ,  self.pa_type)







def   __UpperCAmelCase    (   )							->      Optional[Any]:

	'''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()
		__snake_case      :							int						=			list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
	return _IMAGE_COMPRESSION_FORMATS
def   __UpperCAmelCase    (   UpperCAmelCase_   :    "PIL.Image.Image"  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			BytesIO()
	if image.format in list_image_compression_formats():
		__snake_case      :							Union[str, Any]						=			image.format
	else:
		__snake_case      :							Optional[int]						=			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
	image.save(_a      ,							format=_a  )
	return buffer.getvalue()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    "PIL.Image.Image"  )							->      Union[str, Any]:

	'''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   __UpperCAmelCase    (   UpperCAmelCase_   :    np.ndarray  )							->      Optional[Any]:

	'''simple docstring'''





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

	__snake_case      :							Optional[Any]						=			array.dtype
	__snake_case      :							Optional[Any]						=			dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
	__snake_case      :							int						=			dtype.kind
	__snake_case      :							Tuple						=			dtype.itemsize

	__snake_case      :							List[str]						=			None

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

def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]  )							->      List[str]:

	'''simple docstring'''





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

	if objs:
		__snake_case      :							List[str]						=			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  ):
			__snake_case      :							Dict						=			no_op_if_value_is_null(_a  )
			return [obj_to_image_dict_func(_a  ) for obj in objs]
		elif isinstance(_a      ,							PIL.Image.Image  ):
			__snake_case      :							Optional[int]						=			no_op_if_value_is_null(_a  )
			return [obj_to_image_dict_func(_a  ) for obj in objs]
		else:
			return objs
	else:
		return objs






"""simple docstring"""







import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			0



				def  _lowercase					(self    :				Tuple)   ->     int:
					__snake_case      :							Optional[Any]						=			AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				str)   ->     List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							List[str]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							Any						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							List[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							str						=			CLIPConfig()

						# Create a dummy config file with image_proceesor_type
						__snake_case      :							List[Any]						=			Path(_A) / 'preprocessor_config.json'
						__snake_case      :							Optional[Any]						=			Path(_A) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
						json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

						# remove image_processor_type to make sure config.json alone is enough to load image processor locally
						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A).to_dict()

						config_dict.pop('image_processor_type')
						__snake_case      :							Optional[int]						=			CLIPImageProcessor(**_A)

						# save in new folder
						model_config.save_pretrained(_A)
						config.save_pretrained(_A)

						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A)

						# make sure private variable is not incorrectly saved
						__snake_case      :							int						=			json.loads(config.to_json_string())
						self.assertTrue('_processor_class' not in dict_as_saved)

					self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case      :							int						=			Path(_A) / 'preprocessor_config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )

						__snake_case      :							List[str]						=			AutoImageProcessor.from_pretrained(_A)
						self.assertIsInstance(_A     ,  _A)



				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					with self.assertRaisesRegex(
					    _A     ,  'clip-base is not a local folder and is not a valid model identifier'):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('clip-base')



				def  _lowercase					(self    :				str)   ->     int:
					with self.assertRaisesRegex(
					    _A     ,  r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case      :							str						=			AutoImageProcessor.from_pretrained(_A     ,  revision='aaaaaa')



				def  _lowercase					(self    :				List[Any])   ->     str:
					with self.assertRaisesRegex(
					    _A     ,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'     ,  ):
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')



				def  _lowercase					(self    :				Optional[int])   ->     List[str]:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_A):
						__snake_case      :							Any						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_A):
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)

					__snake_case      :							Union[str, Any]						=			AutoImageProcessor.from_pretrained(
					    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
					self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')

					# Test image processor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(_A)
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(_A     ,  trust_remote_code=_A)
					self.assertEqual(reloaded_image_processor.__class__.__name__     ,  'NewImageProcessor')



				def  _lowercase					(self    :				int)   ->     Optional[int]:
					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(_A):
							AutoImageProcessor.register(_A     ,  _A)

						with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case      :							Tuple						=			Path(_A) / 'preprocessor_config.json'
							__snake_case      :							Dict						=			Path(_A) / 'config.json'
							json.dump(
							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}     ,  open(_A     ,  'w')     ,  )
							json.dump({'model_type': 'clip'}     ,  open(_A     ,  'w'))

							__snake_case      :							Tuple						=			CustomImageProcessor.from_pretrained(_A)

						# Now that the config is registered, it can be used as any other config with the auto-API
						with tempfile.TemporaryDirectory() as tmp_dir:
							image_processor.save_pretrained(_A)
							__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained(_A)
							self.assertIsInstance(_A     ,  _A)

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






				def  _lowercase					(self    :				List[Any])   ->     Tuple:

					class 		UpperCamelCase      (   lowercase						):
									UpperCAmelCase    :				str   =		True

					try:
						AutoConfig.register('custom'     ,  _A)
						AutoImageProcessor.register(_A     ,  _A)
						# If remote code is not set, the default is to use local
						__snake_case      :							Tuple						=			AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote code is disabled, we load the local one.
						__snake_case      :							Optional[int]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

						# If remote is enabled, we load from the Hub
						__snake_case      :							List[Any]						=			AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'     ,  trust_remote_code=_A)
						self.assertEqual(image_processor.__class__.__name__     ,  'NewImageProcessor')
						self.assertTrue(not hasattr(_A     ,  'is_local'))

					finally:
						if "custom" in CONFIG_MAPPING._extra_content:
							del CONFIG_MAPPING._extra_content["custom"]
						if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
							del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





