



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 							lowerCAmelCase_		(		UpperCAmelCase__     ):
		SCREAMING_SNAKE_CASE_       :			Dict					       =	["""image_processor""", """tokenizer"""]
		SCREAMING_SNAKE_CASE_       :			Any					       =	"""LayoutLMv2ImageProcessor"""
		SCREAMING_SNAKE_CASE_       :			List[Any]					       =	("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







		def __init__(    self  :   List[Any]       ,		UpperCAmelCase_  :   Union[str, Any]=None       ,		UpperCAmelCase_  :   Optional[Any]=None       ,		**UpperCAmelCase_  :   Tuple					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
										    ''' instead.'''       ,		__lowercase       ,		)
										_UpperCAmelCase						:  str     					=	kwargs.pop('''feature_extractor'''					)

						_UpperCAmelCase						:  int     					=	image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
										raise ValueError('''You need to specify an `image_processor`.'''					)
						if tokenizer is None:
										raise ValueError('''You need to specify a `tokenizer`.'''					)

						super().__init__(__lowercase       ,		__lowercase					)







		def __call__(    self  :   Any       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   int = None       ,		UpperCAmelCase_  :   Optional[int] = None       ,		UpperCAmelCase_  :   List[Any] = None       ,		UpperCAmelCase_  :   Optional[int] = None       ,		UpperCAmelCase_  :   List[str] = True       ,		UpperCAmelCase_  :   int = False       ,		UpperCAmelCase_  :   Dict = False       ,		UpperCAmelCase_  :   Union[str, Any] = None       ,		UpperCAmelCase_  :   List[str] = 0       ,		UpperCAmelCase_  :   str = None       ,		UpperCAmelCase_  :   List[str] = None       ,		UpperCAmelCase_  :   Optional[int] = None       ,		UpperCAmelCase_  :   List[str] = False       ,		UpperCAmelCase_  :   List[str] = False       ,		UpperCAmelCase_  :   Any = False       ,		UpperCAmelCase_  :   Dict = False       ,		UpperCAmelCase_  :   Dict = True       ,		UpperCAmelCase_  :   Optional[int] = None       ,		**UpperCAmelCase_  :   Union[str, Any]       ,		)       ->		BatchEncoding:


						'''simple docstring'''







						if self.image_processor.apply_ocr and (boxes is not None):
										raise ValueError(
										    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''					)

						if self.image_processor.apply_ocr and (word_labels is not None):
										raise ValueError(
										    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''					)

						if return_overflowing_tokens is True and return_offsets_mapping is False:
										raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''					)

						# first, apply the image processor
						_UpperCAmelCase						:  Optional[Any]     					=	self.image_processor(images=__lowercase       ,		return_tensors=__lowercase					)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
										if isinstance(__lowercase       ,		__lowercase					):
														_UpperCAmelCase						:  List[Any]     					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
										_UpperCAmelCase						:  str     					=	features['''words''']

						_UpperCAmelCase						:  Tuple     					=	self.tokenizer(
						    text=text if text is not None else features['''words''']       ,		text_pair=text_pair if text_pair is not None else None       ,		boxes=boxes if boxes is not None else features['''boxes''']       ,		word_labels=__lowercase       ,		add_special_tokens=__lowercase       ,		padding=__lowercase       ,		truncation=__lowercase       ,		max_length=__lowercase       ,		stride=__lowercase       ,		pad_to_multiple_of=__lowercase       ,		return_token_type_ids=__lowercase       ,		return_attention_mask=__lowercase       ,		return_overflowing_tokens=__lowercase       ,		return_special_tokens_mask=__lowercase       ,		return_offsets_mapping=__lowercase       ,		return_length=__lowercase       ,		verbose=__lowercase       ,		return_tensors=__lowercase       ,		**__lowercase       ,		)

						# add pixel values
						_UpperCAmelCase						:  Any     					=	features.pop('''pixel_values'''					)
						if return_overflowing_tokens is True:
										_UpperCAmelCase						:  Optional[Any]     					=	self.get_overflowing_images(__lowercase       ,		encoded_inputs['''overflow_to_sample_mapping''']					)
						_UpperCAmelCase						:  Dict     					=	images

						return encoded_inputs







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   int       ,		UpperCAmelCase_  :   List[str]					)       ->		int:


						'''simple docstring'''







						_UpperCAmelCase						:  str     					=	[]
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]					)

						if len(__lowercase					) != len(__lowercase					):
										raise ValueError(
										    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
										    F''' {len(__lowercase					)} and {len(__lowercase					)}'''					)

						return images_with_overflow







		def 					a_  (    self  :   Dict       ,		*UpperCAmelCase_  :   int       ,		**UpperCAmelCase_  :   List[Any]					)       ->		Union[str, Any]:


						'''simple docstring'''







						return self.tokenizer.batch_decode(*__lowercase       ,		**__lowercase					)







		def 					a_  (    self  :   List[str]       ,		*UpperCAmelCase_  :   Dict       ,		**UpperCAmelCase_  :   str					)       ->		Any:


						'''simple docstring'''







						return self.tokenizer.decode(*__lowercase       ,		**__lowercase					)







		@property
		def 					a_  (    self  :   List[Any]					)       ->		int:


						'''simple docstring'''







						return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







		@property
		def 					a_  (    self  :   Tuple					)       ->		Dict:


						'''simple docstring'''







						warnings.warn(
						    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,		__lowercase       ,		)
						return self.image_processor_class







		@property
		def 					a_  (    self  :   Optional[int]					)       ->		Any:


						'''simple docstring'''







						warnings.warn(
						    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,		__lowercase       ,		)
						return self.image_processor




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Union[str, Any]       =				"""config.json"""




						@register_to_config
						def __init__(					self ,    __lowercase=2 ,    __lowercase=5 ,    __lowercase=(2, 5) ,    __lowercase="for diffusion" ,    __lowercase=[1, 3] ,    )       ->     int:
										pass







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""config.json"""




						@register_to_config
						def __init__(					self ,    __lowercase=2 ,    __lowercase=5 ,    __lowercase=(2, 5) ,    __lowercase="for diffusion" ,    __lowercase=[1, 3] ,    )       ->     Optional[Any]:
										pass







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""config.json"""




						@register_to_config
						def __init__(					self ,    __lowercase=2 ,    __lowercase=5 ,    __lowercase=(2, 5) ,    __lowercase="for diffusion" ,    __lowercase=[1, 3] ,    __lowercase=[1, 3] ,    )       ->     List[str]:
										pass







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[Any]       =				"""config.json"""




						@register_to_config
						def __init__(					self ,    __lowercase=2 ,    __lowercase=5 ,    __lowercase=(2, 5) ,    __lowercase="for diffusion" ,    __lowercase=[1, 5] ,    __lowercase=[5, 4] ,    )       ->     List[str]:
										pass







class 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     str:
										with self.assertRaises(__lowercase				):
														ConfigMixin.load_config('''dummy_path'''				)




						def     lowercase_			(					self				)       ->     Optional[int]:
										lowerCAmelCase_			:   List[str]     					=					SampleObject()
										lowerCAmelCase_			:   Dict     					=					obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# init ignore private arguments
										lowerCAmelCase_			:   List[str]     					=					SampleObject(_name_or_path='''lalala'''				)
										lowerCAmelCase_			:   Dict     					=					obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# can override default
										lowerCAmelCase_			:   List[Any]     					=					SampleObject(c=6				)
										lowerCAmelCase_			:   Optional[int]     					=					obj.config
										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == 6
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										# can use positional arguments.
										lowerCAmelCase_			:   Optional[Any]     					=					SampleObject(1 ,    c=6				)
										lowerCAmelCase_			:   Dict     					=					obj.config
										assert config["a"] == 1
										assert config["b"] == 5
										assert config["c"] == 6
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   List[Any]     					=					SampleObject()
										lowerCAmelCase_			:   str     					=					obj.config

										assert config["a"] == 2
										assert config["b"] == 5
										assert config["c"] == (2, 5)
										assert config["d"] == "for diffusion"
										assert config["e"] == [1, 3]

										with tempfile.TemporaryDirectory() as tmpdirname:
														obj.save_config(__lowercase				)
														lowerCAmelCase_			:   int     					=					SampleObject.from_config(SampleObject.load_config(__lowercase				)				)
														lowerCAmelCase_			:   Optional[Any]     					=					new_obj.config

										# unfreeze configs
										lowerCAmelCase_			:   str     					=					dict(__lowercase				)
										lowerCAmelCase_			:   Any     					=					dict(__lowercase				)

										assert config.pop('''c'''				) == (2, 5)  # instantiated as tuple
										assert new_config.pop('''c'''				) == [2, 5]  # saved & loaded as list because of json
										config.pop('''_use_default_values'''				)
										assert config == new_config




						def     lowercase_			(					self				)       ->     List[str]:
										lowerCAmelCase_			:   Optional[Any]     					=					logging.get_logger('''diffusers.configuration_utils'''				)
										# 30 for warning
										logger.setLevel(3_0				)

										with CaptureLogger(__lowercase				) as cap_logger:
														lowerCAmelCase_			:   int     					=					DDIMScheduler.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-torch''' ,    subfolder='''scheduler'''				)

										assert ddim.__class__ == DDIMScheduler
										# no warning should be thrown
										assert cap_logger.out == ""




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_			:   List[Any]     					=					logging.get_logger('''diffusers.configuration_utils'''				)
										# 30 for warning
										logger.setLevel(3_0				)

										with CaptureLogger(__lowercase				) as cap_logger:
														lowerCAmelCase_			:   Any     					=					EulerDiscreteScheduler.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-torch''' ,    subfolder='''scheduler'''				)

										assert euler.__class__ == EulerDiscreteScheduler
										# no warning should be thrown
										assert cap_logger.out == ""




						def     lowercase_			(					self				)       ->     Any:
										lowerCAmelCase_			:   Tuple     					=					logging.get_logger('''diffusers.configuration_utils'''				)
										# 30 for warning
										logger.setLevel(3_0				)

										with CaptureLogger(__lowercase				) as cap_logger:
														lowerCAmelCase_			:   Optional[int]     					=					EulerAncestralDiscreteScheduler.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-torch''' ,    subfolder='''scheduler'''				)

										assert euler.__class__ == EulerAncestralDiscreteScheduler
										# no warning should be thrown
										assert cap_logger.out == ""




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_			:   Any     					=					logging.get_logger('''diffusers.configuration_utils'''				)
										# 30 for warning
										logger.setLevel(3_0				)

										with CaptureLogger(__lowercase				) as cap_logger:
														lowerCAmelCase_			:   Dict     					=					PNDMScheduler.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-torch''' ,    subfolder='''scheduler'''				)

										assert pndm.__class__ == PNDMScheduler
										# no warning should be thrown
										assert cap_logger.out == ""




						def     lowercase_			(					self				)       ->     Tuple:
										lowerCAmelCase_			:   Optional[int]     					=					logging.get_logger('''diffusers.configuration_utils'''				)
										# 30 for warning
										logger.setLevel(3_0				)

										with CaptureLogger(__lowercase				) as cap_logger:
														lowerCAmelCase_			:   int     					=					DDPMScheduler.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-torch''' ,    subfolder='''scheduler''' ,    prediction_type='''sample''' ,    beta_end=8 ,    )

										with CaptureLogger(__lowercase				) as cap_logger_a:
														lowerCAmelCase_			:   Tuple     					=					DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256''' ,    beta_start=8_8				)

										assert ddpm.__class__ == DDPMScheduler
										assert ddpm.config.prediction_type == "sample"
										assert ddpm.config.beta_end == 8
										assert ddpm_a.config.beta_start == 8_8

										# no warning should be thrown
										assert cap_logger.out == ""
										assert cap_logger_a.out == ""




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Tuple     					=					logging.get_logger('''diffusers.configuration_utils'''				)
										# 30 for warning
										logger.setLevel(3_0				)

										with CaptureLogger(__lowercase				) as cap_logger:
														lowerCAmelCase_			:   List[str]     					=					DPMSolverMultistepScheduler.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-torch''' ,    subfolder='''scheduler'''				)

										assert dpm.__class__ == DPMSolverMultistepScheduler
										# no warning should be thrown
										assert cap_logger.out == ""




						def     lowercase_			(					self				)       ->     Optional[Any]:
										# let's first save a config that should be in the form
										#    a=2,
										#    b=5,
										#    c=(2, 5),
										#    d="for diffusion",
										#    e=[1, 3],

										lowerCAmelCase_			:   Dict     					=					SampleObject()

										lowerCAmelCase_			:   Optional[int]     					=					{k: v for k, v in config.config.items() if not k.startswith('''_'''				)}

										# make sure that default config has all keys in `_use_default_values`
										assert set(config_dict.keys()				) == set(config.config._use_default_values				)

										with tempfile.TemporaryDirectory() as tmpdirname:
														config.save_config(__lowercase				)

														# now loading it with SampleObject2 should put f into `_use_default_values`
														lowerCAmelCase_			:   List[Any]     					=					SampleObjecta.from_config(__lowercase				)

														assert "f" in config._use_default_values
														assert config.f == [1, 3]

										# now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
										# **BECAUSE** it is part of `config._use_default_values`
										lowerCAmelCase_			:   List[Any]     					=					SampleObjecta.from_config(config.config				)
										assert new_config.f == [5, 4]

										config.config._use_default_values.pop()
										lowerCAmelCase_			:   int     					=					SampleObjecta.from_config(config.config				)
										assert new_config_a.f == [1, 3]

										# Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
										assert new_config_a.e == [1, 3]