

import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = False	):

						if device_specific:
												seed += AcceleratorState().process_index
						random.seed(lowercase_	)
						np.random.seed(lowercase_	)
						torch.manual_seed(lowercase_	)
						if is_xpu_available():
												torch.xpu.manual_seed_all(lowercase_	)
						elif is_npu_available():
												torch.npu.manual_seed_all(lowercase_	)
						else:
												torch.cuda.manual_seed_all(lowercase_	)
						# ^^ safe to call this function even if cuda is not available
						if is_tpu_available():
												xm.set_rng_state(lowercase_	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = None	):

						if rng_type == RNGType.TORCH:
												snake_case_       =     torch.get_rng_state()
						elif rng_type == RNGType.CUDA:
												snake_case_       =     torch.cuda.get_rng_state()
						elif rng_type == RNGType.XLA:
												assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
												snake_case_       =     torch.tensor(xm.get_rng_state()	)
						elif rng_type == RNGType.NPU:
												assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
												snake_case_       =     torch.npu.get_rng_state()
						elif rng_type == RNGType.XPU:
												assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
												snake_case_       =     torch.xpu.get_rng_state()
						elif rng_type == RNGType.GENERATOR:
												assert generator is not None, "Need a generator to synchronize its seed."
												snake_case_       =     generator.get_state()

						# Broadcast the rng state from device 0 to other devices
						snake_case_       =     AcceleratorState()
						if state.distributed_type == DistributedType.TPU:
												snake_case_       =     rng_state.to(xm.xla_device()	)
												xm.collective_broadcast([rng_state]	)
												xm.mark_step()
												snake_case_       =     rng_state.cpu()
						elif (
						    state.distributed_type in CUDA_DISTRIBUTED_TYPES
						    or state.distributed_type == DistributedType.MULTI_NPU
						    or state.distributed_type == DistributedType.MULTI_XPU
						):
												snake_case_       =     rng_state.to(state.device	)
												torch.distributed.broadcast(lowercase_  ,		0	)
												snake_case_       =     rng_state.cpu()
						elif state.distributed_type == DistributedType.MULTI_CPU:
												torch.distributed.broadcast(lowercase_  ,		0	)

						# Set the broadcast rng state
						if rng_type == RNGType.TORCH:
												torch.set_rng_state(lowercase_	)
						elif rng_type == RNGType.CUDA:
												torch.cuda.set_rng_state(lowercase_	)
						elif rng_type == RNGType.NPU:
												torch.npu.set_rng_state(lowercase_	)
						elif rng_type == RNGType.XPU:
												torch.xpu.set_rng_state(lowercase_	)
						elif rng_type == RNGType.XLA:
												xm.set_rng_state(rng_state.item()	)
						elif rng_type == RNGType.GENERATOR:
												generator.set_state(lowercase_	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None	):

						for rng_type in rng_types:
												synchronize_rng_state(RNGType(lowercase_	)  ,		generator=lowercase_	)


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     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							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     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							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									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     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor