
'''simple docstring'''





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     a (					__magic_name__     ):
				_snake_case		     =			['''image_processor''', '''tokenizer''']
				_snake_case		     =			'''LayoutLMv2ImageProcessor'''
				_snake_case		     =			('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')




				def __init__(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     **SCREAMING_SNAKE_CASE_						:					Any   ):
										snake_case							:  List[str]			=     None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.''',     SCREAMING_SNAKE_CASE_,     )
																snake_case							:  Union[str, Any]			=     kwargs.pop('''feature_extractor'''   )

										snake_case							:  List[str]			=     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__(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def __call__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,     SCREAMING_SNAKE_CASE_						:					Union[List[List[int]], List[List[List[int]]]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[List[int], List[List[int]]]] = None,     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					Union[bool, str, PaddingStrategy] = False,     SCREAMING_SNAKE_CASE_						:					Union[bool, str, TruncationStrategy] = False,     SCREAMING_SNAKE_CASE_						:					Optional[int] = None,     SCREAMING_SNAKE_CASE_						:					int = 0,     SCREAMING_SNAKE_CASE_						:					Optional[int] = None,     SCREAMING_SNAKE_CASE_						:					Optional[bool] = None,     SCREAMING_SNAKE_CASE_						:					Optional[bool] = None,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, TensorType]] = None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										# verify input
										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
										snake_case							:  List[str]			=     self.image_processor(images=SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_   )

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

										snake_case							:  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=SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_,     padding=SCREAMING_SNAKE_CASE_,     truncation=SCREAMING_SNAKE_CASE_,     max_length=SCREAMING_SNAKE_CASE_,     stride=SCREAMING_SNAKE_CASE_,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_,     return_token_type_ids=SCREAMING_SNAKE_CASE_,     return_attention_mask=SCREAMING_SNAKE_CASE_,     return_overflowing_tokens=SCREAMING_SNAKE_CASE_,     return_special_tokens_mask=SCREAMING_SNAKE_CASE_,     return_offsets_mapping=SCREAMING_SNAKE_CASE_,     return_length=SCREAMING_SNAKE_CASE_,     verbose=SCREAMING_SNAKE_CASE_,     return_tensors=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )

										# add pixel values
										snake_case							:  Tuple			=     features.pop('''pixel_values'''   )
										if return_overflowing_tokens is True:
																snake_case							:  Dict			=     self.get_overflowing_images(SCREAMING_SNAKE_CASE_,     encoded_inputs['''overflow_to_sample_mapping''']   )
										snake_case							:  int			=     images

										return encoded_inputs




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict   ):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										snake_case							:  Optional[Any]			=     []
										for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]   )

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

										return images_with_overflow




				def 						__snake_case				(				self						:					Union[str, Any],     *SCREAMING_SNAKE_CASE_						:					int,     **SCREAMING_SNAKE_CASE_						:					Tuple   ):
										return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Optional[Any],     *SCREAMING_SNAKE_CASE_						:					List[Any],     **SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				@property
				def 						__snake_case				(				self						:					Union[str, Any]   ):
										return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




				@property
				def 						__snake_case				(				self						:					Optional[Any]   ):
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',     SCREAMING_SNAKE_CASE_,     )
										return self.image_processor_class




				@property
				def 						__snake_case				(				self						:					Optional[int]   ):
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',     SCREAMING_SNAKE_CASE_,     )
										return self.image_processor



'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )