
"""simple docstring"""

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		random.seed(a_      )
		np.random.seed(a_      )
		torch.manual_seed(a_      )
		torch.cuda.manual_seed_all(a_      )
		# ^^ safe to call this function even if cuda is not available






class 	_lowercase       :




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = 0.9999		,			UpperCAmelCase_ = 0.0		,			UpperCAmelCase_ = 0		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = 1.0		,			UpperCAmelCase_ = 2 / 3		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Any:

			if isinstance(UpperCAmelCase_		,			torch.nn.Module ):
					lowerCamelCase					:      str          =   (
					    'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
					    'Please pass the parameters of the module instead.'
					)
					deprecate(
					    'passing a `torch.nn.Module` to `ExponentialMovingAverage`'		,			'1.0.0'		,			UpperCAmelCase_		,			standard_warn=UpperCAmelCase_		,			)
					lowerCamelCase					:      int          =   parameters.parameters()

					# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
					lowerCamelCase					:      Optional[int]          =   True

			if kwargs.get('max_value'		,			UpperCAmelCase_ ) is not None:
					lowerCamelCase					:      Union[str, Any]          =   'The `max_value` argument is deprecated. Please use `decay` instead.'
					deprecate('max_value'		,			'1.0.0'		,			UpperCAmelCase_		,			standard_warn=UpperCAmelCase_ )
					lowerCamelCase					:      Any          =   kwargs['max_value']

			if kwargs.get('min_value'		,			UpperCAmelCase_ ) is not None:
					lowerCamelCase					:      str          =   'The `min_value` argument is deprecated. Please use `min_decay` instead.'
					deprecate('min_value'		,			'1.0.0'		,			UpperCAmelCase_		,			standard_warn=UpperCAmelCase_ )
					lowerCamelCase					:      Tuple          =   kwargs['min_value']

			lowerCamelCase					:      Dict          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      Dict          =   [p.clone().detach() for p in parameters]

			if kwargs.get('device'		,			UpperCAmelCase_ ) is not None:
					lowerCamelCase					:      str          =   'The `device` argument is deprecated. Please use `to` instead.'
					deprecate('device'		,			'1.0.0'		,			UpperCAmelCase_		,			standard_warn=UpperCAmelCase_ )
					self.to(device=kwargs['device'] )

			lowerCamelCase					:      Optional[int]          =   None

			lowerCamelCase					:      List[Any]          =   decay
			lowerCamelCase					:      Tuple          =   min_decay
			lowerCamelCase					:      Dict          =   update_after_step
			lowerCamelCase					:      List[str]          =   use_ema_warmup
			lowerCamelCase					:      List[str]          =   inv_gamma
			lowerCamelCase					:      Optional[int]          =   power
			lowerCamelCase					:      Optional[Any]          =   0
			lowerCamelCase					:      Any          =   None  # set in `step()`

			lowerCamelCase					:      Union[str, Any]          =   model_cls
			lowerCamelCase					:      Union[str, Any]          =   model_config




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							"EMAModel":
			lowerCamelCase     ,   lowerCamelCase					:      Dict          =   model_cls.load_config(UpperCAmelCase_		,			return_unused_kwargs=UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   model_cls.from_pretrained(UpperCAmelCase_ )

			lowerCamelCase					:      Union[str, Any]          =   cls(model.parameters()		,			model_cls=UpperCAmelCase_		,			model_config=model.config )

			ema_model.load_state_dict(UpperCAmelCase_ )
			return ema_model




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Any:
			if self.model_cls is None:
					raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.' )

			if self.model_config is None:
					raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.' )

			lowerCamelCase					:      Any          =   self.model_cls.from_config(self.model_config )
			lowerCamelCase					:      List[Any]          =   self.state_dict()
			state_dict.pop('shadow_params'		,			UpperCAmelCase_ )

			model.register_to_config(**UpperCAmelCase_ )
			self.copy_to(model.parameters() )
			model.save_pretrained(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							float:
			lowerCamelCase					:      List[str]          =   max(0		,			optimization_step - self.update_after_step - 1 )

			if step <= 0:
					return 0.0

			if self.use_ema_warmup:
					lowerCamelCase					:      Tuple          =   1 - (1 + step / self.inv_gamma) ** -self.power
			else:
					lowerCamelCase					:      Union[str, Any]          =   (1 + step) / (10 + step)

			lowerCamelCase					:      Optional[int]          =   min(UpperCAmelCase_		,			self.decay )
			# make sure decay is not smaller than min_decay
			lowerCamelCase					:      Optional[Any]          =   max(UpperCAmelCase_		,			self.min_decay )
			return cur_decay_value




	@torch.no_grad()
	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if isinstance(UpperCAmelCase_		,			torch.nn.Module ):
					lowerCamelCase					:      Optional[Any]          =   (
					    'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
					    'Please pass the parameters of the module instead.'
					)
					deprecate(
					    'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'		,			'1.0.0'		,			UpperCAmelCase_		,			standard_warn=UpperCAmelCase_		,			)
					lowerCamelCase					:      List[str]          =   parameters.parameters()

			lowerCamelCase					:      List[Any]          =   list(UpperCAmelCase_ )

			self.optimization_step += 1

			# Compute the decay factor for the exponential moving average.
			lowerCamelCase					:      List[str]          =   self.get_decay(self.optimization_step )
			lowerCamelCase					:      Optional[Any]          =   decay
			lowerCamelCase					:      List[str]          =   1 - decay

			lowerCamelCase					:      Tuple          =   contextlib.nullcontext
			if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
					import deepspeed

			for s_param, param in zip(self.shadow_params		,			UpperCAmelCase_ ):
					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							lowerCamelCase					:      str          =   deepspeed.zero.GatheredParameters(UpperCAmelCase_		,			modifier_rank=UpperCAmelCase_ )

					with context_manager():
							if param.requires_grad:
									s_param.sub_(one_minus_decay * (s_param - param) )
							else:
									s_param.copy_(UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							None:
			lowerCamelCase					:      int          =   list(UpperCAmelCase_ )
			for s_param, param in zip(self.shadow_params		,			UpperCAmelCase_ ):
					param.data.copy_(s_param.to(param.device ).data )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None )		->							None:
			lowerCamelCase					:      List[Any]          =   [
			    p.to(device=UpperCAmelCase_		,			dtype=UpperCAmelCase_ ) if p.is_floating_point() else p.to(device=UpperCAmelCase_ )
			    for p in self.shadow_params
			]




	def       _UpperCamelCase   (			self )		->							dict:
			return {
			    "decay": self.decay,
			    "min_decay": self.min_decay,
			    "optimization_step": self.optimization_step,
			    "update_after_step": self.update_after_step,
			    "use_ema_warmup": self.use_ema_warmup,
			    "inv_gamma": self.inv_gamma,
			    "power": self.power,
			    "shadow_params": self.shadow_params,
			}




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							None:
			lowerCamelCase					:      Dict          =   [param.detach().cpu().clone() for param in parameters]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							None:
			if self.temp_stored_params is None:
					raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`' )
			for c_param, param in zip(self.temp_stored_params		,			UpperCAmelCase_ ):
					param.data.copy_(c_param.data )

			# Better memory-wise.
			lowerCamelCase					:      Optional[int]          =   None




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							None:
			lowerCamelCase					:      Optional[int]          =   copy.deepcopy(UpperCAmelCase_ )

			lowerCamelCase					:      Dict          =   state_dict.get('decay'		,			self.decay )
			if self.decay < 0.0 or self.decay > 1.0:
					raise ValueError('Decay must be between 0 and 1' )

			lowerCamelCase					:      List[Any]          =   state_dict.get('min_decay'		,			self.min_decay )
			if not isinstance(self.min_decay		,			UpperCAmelCase_ ):
					raise ValueError('Invalid min_decay' )

			lowerCamelCase					:      Tuple          =   state_dict.get('optimization_step'		,			self.optimization_step )
			if not isinstance(self.optimization_step		,			UpperCAmelCase_ ):
					raise ValueError('Invalid optimization_step' )

			lowerCamelCase					:      Dict          =   state_dict.get('update_after_step'		,			self.update_after_step )
			if not isinstance(self.update_after_step		,			UpperCAmelCase_ ):
					raise ValueError('Invalid update_after_step' )

			lowerCamelCase					:      Any          =   state_dict.get('use_ema_warmup'		,			self.use_ema_warmup )
			if not isinstance(self.use_ema_warmup		,			UpperCAmelCase_ ):
					raise ValueError('Invalid use_ema_warmup' )

			lowerCamelCase					:      List[str]          =   state_dict.get('inv_gamma'		,			self.inv_gamma )
			if not isinstance(self.inv_gamma		,			(float, int) ):
					raise ValueError('Invalid inv_gamma' )

			lowerCamelCase					:      int          =   state_dict.get('power'		,			self.power )
			if not isinstance(self.power		,			(float, int) ):
					raise ValueError('Invalid power' )

			lowerCamelCase					:      Dict          =   state_dict.get('shadow_params'		,			UpperCAmelCase_ )
			if shadow_params is not None:
					lowerCamelCase					:      int          =   shadow_params
					if not isinstance(self.shadow_params		,			UpperCAmelCase_ ):
							raise ValueError('shadow_params must be a list' )
					if not all(isinstance(UpperCAmelCase_		,			torch.Tensor ) for p in self.shadow_params ):
							raise ValueError('shadow_params must all be Tensors' )








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   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_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      Any          =   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_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






