


'''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 							__UpperCamelCase    (		lowerCamelCase__ ):
	A_           =   ["""image_processor""", """tokenizer"""]
	A_           =   """LayoutLMv3ImageProcessor"""
	A_           =   ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")




	def __init__(       self						,    __a=None						,    __a=None						,    **__a							):

						'''simple docstring'''





						__a      :	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.'						,    snake_case__						,    )
											__a      :	int    =  kwargs.pop('feature_extractor'							)

						__a      :	Optional[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__(snake_case__						,    snake_case__							)




	def __call__(       self						,    __a						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = True						,    __a = False						,    __a = None						,    __a = None						,    __a = 0						,    __a = None						,    __a = None						,    __a = None						,    __a = False						,    __a = False						,    __a = False						,    __a = False						,    __a = True						,    __a = None						,    **__a						,    ):

						'''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.'							)

						# first, apply the image processor
						__a      :	Optional[int]    =  self.image_processor(images=snake_case__						,    return_tensors=snake_case__							)

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

						__a      :	Any    =  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=snake_case__						,    add_special_tokens=snake_case__						,    padding=snake_case__						,    truncation=snake_case__						,    max_length=snake_case__						,    stride=snake_case__						,    pad_to_multiple_of=snake_case__						,    return_token_type_ids=snake_case__						,    return_attention_mask=snake_case__						,    return_overflowing_tokens=snake_case__						,    return_special_tokens_mask=snake_case__						,    return_offsets_mapping=snake_case__						,    return_length=snake_case__						,    verbose=snake_case__						,    return_tensors=snake_case__						,    **snake_case__						,    )

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

						return encoded_inputs




	def 				__UpperCAmelCase							(       self						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Any    =  []
						for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]							)

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

						return images_with_overflow




	def 				__UpperCAmelCase							(       self						,    *__a						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    *__a						,    **__a							):

						'''simple docstring'''





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




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return ["input_ids", "bbox", "attention_mask", "pixel_values"]




	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




'''simple docstring'''



import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
	from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					if is_torch_version('<'						,				'2.0.0'       ) or not hasattr(_SCREAMING_SNAKE_CASE						,				'_dynamo'       ):
										return False
					return isinstance(_SCREAMING_SNAKE_CASE						,				torch._dynamo.eval_frame.OptimizedModule       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       bool = True       ):
					__a      :	int    =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

					__a      :	Any    =  is_compiled_module(_SCREAMING_SNAKE_CASE       )
					if is_compiled:
										__a      :	List[Any]    =  model
										__a      :	Union[str, Any]    =  model._orig_mod

					if is_deepspeed_available():
										options += (DeepSpeedEngine,)

					while isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	Union[str, Any]    =  model.module

					if not keep_fpaa_wrapper:
										__a      :	Optional[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'forward'       )
										__a      :	str    =  model.__dict__.pop('_original_forward'						,				_SCREAMING_SNAKE_CASE       )
										if original_forward is not None:
															while hasattr(_SCREAMING_SNAKE_CASE						,				'__wrapped__'       ):
																				__a      :	Any    =  forward.__wrapped__
																				if forward == original_forward:
																									break
															__a      :	str    =  forward
										if getattr(_SCREAMING_SNAKE_CASE						,				'_converted_to_transformer_engine'						,				_SCREAMING_SNAKE_CASE       ):
															convert_model(_SCREAMING_SNAKE_CASE						,				to_transformer_engine=_SCREAMING_SNAKE_CASE       )

					if is_compiled:
										__a      :	List[str]    =  model
										__a      :	Optional[int]    =  compiled_model

					return model






def   lowerCamelCase							():
					PartialState().wait_for_everyone()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					if PartialState().distributed_type == DistributedType.TPU:
										xm.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					elif PartialState().local_process_index == 0:
										torch.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )






@contextmanager
def   lowerCamelCase							(**_SCREAMING_SNAKE_CASE				:       Tuple       ):
					for key, value in kwargs.items():
										__a      :	Optional[int]    =  str(_SCREAMING_SNAKE_CASE       )

					yield

					for key in kwargs:
										if key.upper() in os.environ:
															del os.environ[key.upper()]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict       ):
					if not hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ) and not hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										__a      :	List[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'__class__'						,				_SCREAMING_SNAKE_CASE       )
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ):
										return obj.__qualname__
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										return obj.__name__
					return str(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					for key, value in source.items():
										if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															__a      :	int    =  destination.setdefault(_SCREAMING_SNAKE_CASE						,				{}       )
															merge_dicts(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	Tuple    =  value

					return destination






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int = None       ):
					if port is None:
										__a      :	List[str]    =  29_500
					with socket.socket(socket.AF_INET						,				socket.SOCK_STREAM       ) as s:
										return s.connect_ex(('localhost', port)       ) == 0



