






"""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_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''LayoutLMv3ImageProcessor'''
				UpperCAmelCase__        =				('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')







				def __init__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						List[Any]=None						,    __lowerCamelCase							:						Optional[Any]=None						,    **__lowerCamelCase							:						Dict	)				->					Tuple:
										__magic_name__            =     None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																    " instead."						,    __lowerCamelCase						,    )
																__magic_name__            =     kwargs.pop("feature_extractor"	)

										__magic_name__            =     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__(__lowerCamelCase						,    __lowerCamelCase	)







				def __call__(							self							:						Any						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						,    __lowerCamelCase							:						Union[List[List[int]], List[List[List[int]]]] = None						,    __lowerCamelCase							:						Optional[Union[List[int], List[List[int]]]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						List[str]						,    )				->					BatchEncoding:
										# 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."	)

										# first, apply the image processor
										__magic_name__            =     self.image_processor(images=__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

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

										__magic_name__            =     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=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )

										# add pixel values
										__magic_name__            =     features.pop("pixel_values"	)
										if return_overflowing_tokens is True:
																__magic_name__            =     self.get_overflowing_images(__lowerCamelCase						,    encoded_inputs["overflow_to_sample_mapping"]	)
										__magic_name__            =     images

										return encoded_inputs







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						int	)				->					Union[str, Any]:
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										__magic_name__            =     []
										for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]	)

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

										return images_with_overflow







				def     _snake_case					(							self							:						Optional[Any]						,    *__lowerCamelCase							:						Any						,    **__lowerCamelCase							:						Optional[Any]	)				->					Union[str, Any]:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Any						,    **__lowerCamelCase							:						List[str]	)				->					Dict:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				def     _snake_case					(							self							:						Any	)				->					str:
										return ["input_ids", "bbox", "attention_mask", "pixel_values"]







				@property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."						,    __lowerCamelCase						,    )
										return self.image_processor_class







				@property
				def     _snake_case					(							self							:						Any	)				->					Tuple:
										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."						,    __lowerCamelCase						,    )
										return self.image_processor














"""simple docstring"""

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
			# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
			# setup instructions, if using on-demand hardware

			# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
			# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
			# Throw an error if user passes both BYO and on-demand cluster args
			# Otherwise, use default values
			lowercase    			=       argparse.ArgumentParser()
			parser.add_argument('''--user''', type=str, default='''ubuntu''')
			parser.add_argument('''--host''', type=str, default='''localhost''')
			parser.add_argument('''--key_path''', type=str, default=None)
			parser.add_argument('''--instance''', type=str, default='''V100:1''')
			parser.add_argument('''--provider''', type=str, default='''cheapest''')
			parser.add_argument('''--use_spot''', type=bool, default=False)
			parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
			lowercase			,      lowercase    			=       parser.parse_known_args()
			if args.host != "localhost":
						if args.instance != "V100:1" or args.provider != "cheapest":
									raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
						)
			else:
						lowercase    			=       rh.cluster(
						    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
						)
			lowercase    			=       args.example.rsplit('''/''', 1)[0]

			# Set up remote environment
			cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
			# Note transformers is copied into the home directory on the remote machine, so we can install from there
			cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
			cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

			# Run example. You can bypass the CLI wrapper and paste your own code here.
			cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

			# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
			# from my_script... import train
			# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
			# launch_train_gpu = rh.function(fn=train,
			#                                system=gpu,
			#                                reqs=reqs,
			#                                name='train_bert_glue')
			#
			# We can pass in arguments just like we would to a function:
			# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
			#                  stream_logs=True)



