'''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 							snake_case__				(     _A:   Tuple					)       ->      Union[str, Any]:

		'''simple docstring'''


		if is_torch_version("""<""" ,  """2.0.0"""					) or not hasattr(_A ,  """_dynamo"""					):
				return False
		return isinstance(_A ,  torch._dynamo.eval_frame.OptimizedModule					)






def 							snake_case__				(     _A:   List[str] ,  _A:   bool = True					)       ->      Tuple:

		'''simple docstring'''


		lowerCAmelCase     =    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

		lowerCAmelCase     =    is_compiled_module(_A					)
		if is_compiled:
				lowerCAmelCase     =    model
				lowerCAmelCase     =    model._orig_mod

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

		while isinstance(_A ,  _A					):
				lowerCAmelCase     =    model.module

		if not keep_fpaa_wrapper:
				lowerCAmelCase     =    getattr(_A ,  """forward"""					)
				lowerCAmelCase     =    model.__dict__.pop("""_original_forward""" ,  _A					)
				if original_forward is not None:
						while hasattr(_A ,  """__wrapped__"""					):
								lowerCAmelCase     =    forward.__wrapped__
								if forward == original_forward:
										break
						lowerCAmelCase     =    forward
				if getattr(_A ,  """_converted_to_transformer_engine""" ,  _A					):
						convert_model(_A ,  to_transformer_engine=_A					)

		if is_compiled:
				lowerCAmelCase     =    model
				lowerCAmelCase     =    compiled_model

		return model






def 							snake_case__				(     )       ->      int:

		'''simple docstring'''


		PartialState().wait_for_everyone()






def 							snake_case__				(     _A:   Any ,  _A:   str					)       ->      str:

		'''simple docstring'''


		if PartialState().distributed_type == DistributedType.TPU:
				xm.save(_A ,  _A					)
		elif PartialState().local_process_index == 0:
				torch.save(_A ,  _A					)






@contextmanager
def 							snake_case__				(     **_A:   List[str]					)       ->      Dict:

		'''simple docstring'''


		for key, value in kwargs.items():
				lowerCAmelCase     =    str(_A					)

		yield

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






def 							snake_case__				(     _A:   List[Any]					)       ->      Tuple:

		'''simple docstring'''


		if not hasattr(_A ,  """__qualname__"""					) and not hasattr(_A ,  """__name__"""					):
				lowerCAmelCase     =    getattr(_A ,  """__class__""" ,  _A					)
		if hasattr(_A ,  """__qualname__"""					):
				return obj.__qualname__
		if hasattr(_A ,  """__name__"""					):
				return obj.__name__
		return str(_A					)






def 							snake_case__				(     _A:   List[str] ,  _A:   Optional[int]					)       ->      List[str]:

		'''simple docstring'''


		for key, value in source.items():
				if isinstance(_A ,  _A					):
						lowerCAmelCase     =    destination.setdefault(_A ,  {}					)
						merge_dicts(_A ,  _A					)
				else:
						lowerCAmelCase     =    value

		return destination






def 							snake_case__				(     _A:   int = None					)       ->      bool:

		'''simple docstring'''


		if port is None:
				lowerCAmelCase     =    29500
		with socket.socket(socket.AF_INET ,  socket.SOCK_STREAM					) as s:
				return s.connect_ex(("""localhost""", port)					) == 0





'''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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			['''image_processor''', '''tokenizer''']
			UpperCAmelCase_     :	str						=			'''ViltImageProcessor'''
			UpperCAmelCase_     :	Any						=			('''BertTokenizer''', '''BertTokenizerFast''')



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




					"""simple docstring"""


					lowerCAmelCase     =    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			,       )
							lowerCAmelCase     =    kwargs.pop("""feature_extractor""")

					lowerCAmelCase     =    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)
					lowerCAmelCase     =    self.image_processor



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




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer(
					    text=__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 + pixel_mask
					lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors=__lowerCAmelCase)
					encoding.update(__lowerCAmelCase)

					return encoding



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


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



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.tokenizer.model_input_names
					lowerCAmelCase     =    self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def 				a_			(  self):




					"""simple docstring"""


					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 				a_			(  self):




					"""simple docstring"""


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




