



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=()	,				__UpperCamelCase=None	,				__UpperCamelCase="no"	,				__UpperCamelCase="29500" )	->						Any:
				a__	:							List[str]      						= False
				a__	:							str      						= False
				if any(key.startswith("KAGGLE" ) for key in os.environ.keys() ):
								a__	:							Optional[int]      						= True
				elif "IPython" in sys.modules:
								a__	:							int      						= "google.colab" in str(sys.modules["IPython"].get_ipython() )

				try:
								a__	:							Dict      						= PrecisionType(mixed_precision.lower() )
				except ValueError:
								raise ValueError(
								    F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.' )

				if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"	,				__UpperCamelCase ) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state ) > 0:
												raise ValueError(
												    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
												    "your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`." )
								if num_processes is None:
												a__	:							str      						= 8

								a__	:							List[str]      						= PrepareForLaunch(__UpperCamelCase	,				distributed_type="TPU" )
								print(F'Launching a training on {num_processes} TPU cores.' )
								xmp.spawn(__UpperCamelCase	,				args=__UpperCamelCase	,				nprocs=__UpperCamelCase	,				start_method="fork" )
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print("Launching training on one GPU." )
								else:
												print("Launching training on one CPU." )
								function(*__UpperCamelCase )
				else:
								if num_processes is None:
												raise ValueError(
												    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call." )

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state ) > 0:
																raise ValueError(
																    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
																    "inside your training function. Restart your notebook and make sure no cells initializes an "
																    "`Accelerator`." )

												if torch.cuda.is_initialized():
																raise ValueError(
																    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
																    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
																    "function." )

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=__UpperCamelCase	,				master_addr="127.0.01"	,				master_port=__UpperCamelCase	,				mixed_precision=__UpperCamelCase ):
																a__	:							Union[str, Any]      						= PrepareForLaunch(__UpperCamelCase	,				distributed_type="MULTI_GPU" )
																print(F'Launching training on {num_processes} GPUs.' )
																try:
																				start_processes(__UpperCamelCase	,				args=__UpperCamelCase	,				nprocs=__UpperCamelCase	,				start_method="fork" )
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																								    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																								    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																								    "which one is problematic." ) from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																a__	:							Any      						= "1"
																print("Launching training on MPS." )
												elif torch.cuda.is_available():
																print("Launching training on one GPU." )
												else:
																print("Launching training on CPU." )
												function(*__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=()	,				__UpperCamelCase=2 )	->						Any:
				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=__UpperCamelCase	,				master_addr="127.0.01"	,				master_port="29500"	,				accelerate_mixed_precision="no"	,				accelerate_debug_rdv_file=tmp_file.name	,				accelerate_use_cpu="yes"	,				):
												a__	:							str      						= PrepareForLaunch(__UpperCamelCase	,				debug=__UpperCamelCase )
												start_processes(__UpperCamelCase	,				args=__UpperCamelCase	,				nprocs=__UpperCamelCase	,				start_method="fork" )







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						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       ["image_processor", "tokenizer"]
							A				:Any =       "LayoutLMv3ImageProcessor"
							A				:str =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



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







											"""simple docstring"""



											a__	:							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." ,					__UpperCAmelCase ,					)
															a__	:							List[str]      						= 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__(__UpperCAmelCase ,					__UpperCAmelCase				)



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







											"""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__	:							List[str]      						= self.image_processor(images=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase				)

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

											a__	:							Union[str, 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=__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					pad_to_multiple_of=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase ,					return_attention_mask=__UpperCAmelCase ,					return_overflowing_tokens=__UpperCAmelCase ,					return_special_tokens_mask=__UpperCAmelCase ,					return_offsets_mapping=__UpperCAmelCase ,					return_length=__UpperCAmelCase ,					verbose=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											# add pixel values
											a__	:							Dict      						= features.pop("pixel_values"				)
											if return_overflowing_tokens is True:
															a__	:							str      						= self.get_overflowing_images(__UpperCAmelCase ,					encoded_inputs["overflow_to_sample_mapping"]				)
											a__	:							Tuple      						= images

											return encoded_inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



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

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

											return images_with_overflow



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







											"""simple docstring"""



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



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







											"""simple docstring"""



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



							@property
							def 		_A	(							self				):







											"""simple docstring"""



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



							@property
							def 		_A	(							self				):







											"""simple docstring"""



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




							@property
							def 		_A	(							self				):







											"""simple docstring"""



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


