
import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler







class        lowerCAmelCase__      :

						def __init__(			self       ,							a				)					->      Tuple:


								'''simple docstring'''

								if isinstance(a       ,							a				):
										# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
										# modified it, it will not be accepted here again, since `auto` values would have been overridden
										_UpperCamelCase						    =	deepcopy(a				)
								elif os.path.exists(a				):
										with io.open(a       ,							"""r"""       ,							encoding="""utf-8"""				) as f:
												_UpperCamelCase						    =	json.load(a				)
								else:
										try:
												_UpperCamelCase						    =	baseaa.urlsafe_baadecode(a				).decode("""utf-8"""				)
												_UpperCamelCase						    =	json.loads(a				)
										except (UnicodeDecodeError, AttributeError, ValueError):
												raise ValueError(
												    F'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'				)

								_UpperCamelCase						    =	config

								self.set_stage_and_offload()

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_value("""zero_optimization.stage"""       ,							-1				)

								# offload
								_UpperCamelCase						    =	False
								if self.is_zeroa() or self.is_zeroa():
										_UpperCamelCase						    =	set(["""cpu""", """nvme"""]				)
										_UpperCamelCase						    =	set(
										    [
										        self.get_value("""zero_optimization.offload_optimizer.device"""				),
										        self.get_value("""zero_optimization.offload_param.device"""				),
										    ]				)
										if len(offload_devices & offload_devices_valid				) > 0:
												_UpperCamelCase						    =	True

						def  A_				(			self       ,							a				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	self.config

								# find the config node of interest if it exists
								_UpperCamelCase						    =	ds_key_long.split("""."""				)
								_UpperCamelCase						    =	nodes.pop()
								for node in nodes:
										_UpperCamelCase						    =	config.get(a				)
										if config is None:
												return None, ds_key

								return config, ds_key

						def  A_				(			self       ,							a       ,							a=None				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase						    =	self.find_config_node(a				)
								if config is None:
										return default
								return config.get(a       ,							a				)

						def  A_				(			self       ,							a       ,							a=False				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.config

								# find the config node of interest if it exists
								_UpperCamelCase						    =	ds_key_long.split("""."""				)
								for node in nodes:
										_UpperCamelCase						    =	config
										_UpperCamelCase						    =	config.get(a				)
										if config is None:
												if must_exist:
														raise ValueError(F'Can\'t find {ds_key_long} entry in the config: {self.config}'				)
												else:
														return

        # if found remove it
								if parent_config is not None:
										parent_config.pop(a				)

						def  A_				(			self       ,							a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_value(a				)
								return False if value is None else bool(a				)

						def  A_				(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_value(a				)
								return False if value is None else not bool(a				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								return self._stage == 2

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								return self._stage == 3







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								return self._offload







class        lowerCAmelCase__      :

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	engine







						def  A_				(			self       ,							a       ,							**a				)					->      str:


								'''simple docstring'''

								self.engine.backward(a       ,							**a				)

								# Deepspeed's `engine.step` performs the following operations:
								# - gradient accumulation check
								# - gradient clipping
								# - optimizer step
								# - zero grad
								# - checking overflow
								# - lr_scheduler step (only if engine.lr_scheduler is not None)
								self.engine.step()
								# and this plugin overrides the above calls with no-ops when Accelerate runs under
								# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
								# training loop that works transparently under many training regimes.







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a				)					->      Dict:


								'''simple docstring'''

								super().__init__(a       ,							device_placement=a       ,							scaler=a				)
								_UpperCamelCase						    =	hasattr(self.optimizer       ,							"""overflow"""				)

						def  A_				(			self       ,							a=None				)					->      Tuple:


								'''simple docstring'''

								pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







						@property
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								if self.__has_overflow__:
										return self.optimizer.overflow
								return False







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      int:


								'''simple docstring'''

								super().__init__(a       ,							a				)







						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class        lowerCAmelCase__      :

						def __init__(			self       ,							a       ,							a=0.001       ,							a=0       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	params
								_UpperCamelCase						    =	lr
								_UpperCamelCase						    =	weight_decay
								_UpperCamelCase						    =	kwargs







class        lowerCAmelCase__      :

						def __init__(			self       ,							a       ,							a=None       ,							a=0       ,							**a				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	optimizer
								_UpperCamelCase						    =	total_num_steps
								_UpperCamelCase						    =	warmup_num_steps
								_UpperCamelCase						    =	kwargs





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        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							a       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	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__(a       ,							a				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							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       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

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

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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



