'''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     _a				(      lowercase__  :			int ):







     '''simple docstring'''


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







class       snake_case					:




  def __init__(      self     :	Any     , a_     :	Iterable[torch.nn.Parameter]     , a_     :	float = 0.9999     , a_     :	float = 0.0     , a_     :	int = 0     , a_     :	bool = False     , a_     :	Union[float, int] = 1.0     , a_     :	Union[float, int] = 2 / 3     , a_     :	Optional[Any] = None     , a_     :	Dict[str, Any] = None     , **a_     :	Optional[int]     , )->       Dict:




       """simple docstring"""

       if isinstance(a_     , torch.nn.Module				):
            SCREAMING_SNAKE_CASE__				:				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'     , a_     , standard_warn=a_     , )
            SCREAMING_SNAKE_CASE__				:				Dict			=		parameters.parameters()

            # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
            SCREAMING_SNAKE_CASE__				:				Any			=		True

       if kwargs.get('max_value'     , a_				) is not None:
            SCREAMING_SNAKE_CASE__				:				Any			=		'The `max_value` argument is deprecated. Please use `decay` instead.'
            deprecate('max_value'     , '1.0.0'     , a_     , standard_warn=a_				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		kwargs['max_value']

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

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		list(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		[p.clone().detach() for p in parameters]

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

       SCREAMING_SNAKE_CASE__				:				Tuple			=		None

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

       SCREAMING_SNAKE_CASE__				:				Tuple			=		model_cls
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model_config




  @classmethod
  def 	__lowercase(      cls     :	Any     , a_     :	List[Any]     , a_     :	str				)->       "EMAModel":




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model_cls.load_config(a_     , return_unused_kwargs=a_				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model_cls.from_pretrained(a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		cls(model.parameters()     , model_cls=a_     , model_config=model.config				)

       ema_model.load_state_dict(a_				)
       return ema_model




  def 	__lowercase(      self     :	List[str]     , a_     :	List[Any]				)->       List[Any]:




       """simple docstring"""
       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__.'				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_cls.from_config(self.model_config				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.state_dict()
       state_dict.pop('shadow_params'     , a_				)

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




  def 	__lowercase(      self     :	Optional[int]     , a_     :	int				)->       float:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		max(0     , optimization_step - self.update_after_step - 1				)

       if step <= 0:
            return 0.0

       if self.use_ema_warmup:
            SCREAMING_SNAKE_CASE__				:				int			=		1 - (1 + step / self.inv_gamma) ** -self.power
       else:
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		(1 + step) / (10 + step)

       SCREAMING_SNAKE_CASE__				:				str			=		min(a_     , self.decay				)
       # make sure decay is not smaller than min_decay
       SCREAMING_SNAKE_CASE__				:				int			=		max(a_     , self.min_decay				)
       return cur_decay_value




  @torch.no_grad()
  def 	__lowercase(      self     :	int     , a_     :	Iterable[torch.nn.Parameter]				)->       Optional[Any]:




       """simple docstring"""
       if isinstance(a_     , torch.nn.Module				):
            SCREAMING_SNAKE_CASE__				:				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'     , a_     , standard_warn=a_     , )
            SCREAMING_SNAKE_CASE__				:				Tuple			=		parameters.parameters()

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		list(a_				)

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.get_decay(self.optimization_step				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		decay
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		1 - decay

       SCREAMING_SNAKE_CASE__				:				str			=		contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
            import deepspeed

       for s_param, param in zip(self.shadow_params     , a_				):
            if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		deepspeed.zero.GatheredParameters(a_     , modifier_rank=a_				)

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




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	Iterable[torch.nn.Parameter]				)->       None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		list(a_				)
       for s_param, param in zip(self.shadow_params     , a_				):
            param.data.copy_(s_param.to(param.device				).data				)




  def 	__lowercase(      self     :	Optional[int]     , a_     :	Any=None     , a_     :	Dict=None				)->       None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		[
           p.to(device=a_     , dtype=a_				) if p.is_floating_point() else p.to(device=a_				)
           for p in self.shadow_params
       ]




  def 	__lowercase(      self     :	Union[str, Any]				)->       dict:




       """simple docstring"""
       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 	__lowercase(      self     :	Optional[Any]     , a_     :	Iterable[torch.nn.Parameter]				)->       None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[param.detach().cpu().clone() for param in parameters]




  def 	__lowercase(      self     :	Dict     , a_     :	Iterable[torch.nn.Parameter]				)->       None:




       """simple docstring"""
       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     , a_				):
            param.data.copy_(c_param.data				)

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




  def 	__lowercase(      self     :	Any     , a_     :	dict				)->       None:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		copy.deepcopy(a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		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'				)

       SCREAMING_SNAKE_CASE__				:				Any			=		state_dict.get('min_decay'     , self.min_decay				)
       if not isinstance(self.min_decay     , a_				):
            raise ValueError('Invalid min_decay'				)

       SCREAMING_SNAKE_CASE__				:				int			=		state_dict.get('optimization_step'     , self.optimization_step				)
       if not isinstance(self.optimization_step     , a_				):
            raise ValueError('Invalid optimization_step'				)

       SCREAMING_SNAKE_CASE__				:				Tuple			=		state_dict.get('update_after_step'     , self.update_after_step				)
       if not isinstance(self.update_after_step     , a_				):
            raise ValueError('Invalid update_after_step'				)

       SCREAMING_SNAKE_CASE__				:				int			=		state_dict.get('use_ema_warmup'     , self.use_ema_warmup				)
       if not isinstance(self.use_ema_warmup     , a_				):
            raise ValueError('Invalid use_ema_warmup'				)

       SCREAMING_SNAKE_CASE__				:				Any			=		state_dict.get('inv_gamma'     , self.inv_gamma				)
       if not isinstance(self.inv_gamma     , (float, int)				):
            raise ValueError('Invalid inv_gamma'				)

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

       SCREAMING_SNAKE_CASE__				:				str			=		state_dict.get('shadow_params'     , a_				)
       if shadow_params is not None:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		shadow_params
            if not isinstance(self.shadow_params     , a_				):
                 raise ValueError('shadow_params must be a list'				)
            if not all(isinstance(a_     , torch.Tensor				) for p in self.shadow_params				):
                 raise ValueError('shadow_params must all be Tensors'				)




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     ['image_processor', 'tokenizer']
  lowercase_			=     'OwlViTImageProcessor'
  lowercase_			=     ('CLIPTokenizer', 'CLIPTokenizerFast')




  def __init__(      self     :	List[str]     , a_     :	List[Any]=None     , a_     :	str=None     , **a_     :	Any				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		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_     , )
            SCREAMING_SNAKE_CASE__				:				Tuple			=		kwargs.pop('feature_extractor'				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		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_				)




  def __call__(      self     :	Any     , a_     :	Optional[int]=None     , a_     :	Tuple=None     , a_     :	List[Any]=None     , a_     :	Tuple="max_length"     , a_     :	str="np"     , **a_     :	Any				)->       int:




       """simple docstring"""

       if text is None and query_images is None and images is None:
            raise ValueError(
                'You have to specify at least one text or query image or image. All three cannot be none.'				)

       if text is not None:
            if isinstance(a_     , a_				) or (isinstance(a_     , a_				) and not isinstance(text[0]     , a_				)):
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		[self.tokenizer(a_     , padding=a_     , return_tensors=a_     , **a_				)]

            elif isinstance(a_     , a_				) and isinstance(text[0]     , a_				):
                 SCREAMING_SNAKE_CASE__				:				Any			=		[]

                 # Maximum number of queries across batch
                 SCREAMING_SNAKE_CASE__				:				str			=		max([len(a_				) for t in text]				)

                 # Pad all batch samples to max number of text queries
                 for t in text:
                      if len(a_				) != max_num_queries:
                           SCREAMING_SNAKE_CASE__				:				Tuple			=		t + [' '] * (max_num_queries - len(a_				))

                      SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.tokenizer(a_     , padding=a_     , return_tensors=a_     , **a_				)
                      encodings.append(a_				)
            else:
                 raise TypeError('Input text should be a string, a list of strings or a nested list of strings'				)

            if return_tensors == "np":
                 SCREAMING_SNAKE_CASE__				:				Dict			=		np.concatenate([encoding['input_ids'] for encoding in encodings]     , axis=0				)
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		np.concatenate([encoding['attention_mask'] for encoding in encodings]     , axis=0				)

            elif return_tensors == "jax" and is_flax_available():
                 import jax.numpy as jnp

                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		jnp.concatenate([encoding['input_ids'] for encoding in encodings]     , axis=0				)
                 SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		jnp.concatenate([encoding['attention_mask'] for encoding in encodings]     , axis=0				)

            elif return_tensors == "pt" and is_torch_available():
                 import torch

                 SCREAMING_SNAKE_CASE__				:				int			=		torch.cat([encoding['input_ids'] for encoding in encodings]     , dim=0				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat([encoding['attention_mask'] for encoding in encodings]     , dim=0				)

            elif return_tensors == "tf" and is_tf_available():
                 import tensorflow as tf

                 SCREAMING_SNAKE_CASE__				:				str			=		tf.stack([encoding['input_ids'] for encoding in encodings]     , axis=0				)
                 SCREAMING_SNAKE_CASE__				:				Dict			=		tf.stack([encoding['attention_mask'] for encoding in encodings]     , axis=0				)

            else:
                 raise ValueError('Target return tensor type could not be returned'				)

            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		BatchEncoding()
            SCREAMING_SNAKE_CASE__				:				List[str]			=		input_ids
            SCREAMING_SNAKE_CASE__				:				Tuple			=		attention_mask

       if query_images is not None:
            SCREAMING_SNAKE_CASE__				:				Any			=		BatchEncoding()
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor(
                a_     , return_tensors=a_     , **a_				).pixel_values
            SCREAMING_SNAKE_CASE__				:				Dict			=		query_pixel_values

       if images is not None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processor(a_     , return_tensors=a_     , **a_				)

       if text is not None and images is not None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		image_features.pixel_values
            return encoding
       elif query_images is not None and images is not None:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_features.pixel_values
            return encoding
       elif text is not None or query_images is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**a_				)     , tensor_type=a_				)




  def 	__lowercase(      self     :	str     , *a_     :	List[str]     , **a_     :	int				)->       List[Any]:




       """simple docstring"""
       return self.image_processor.post_process(*a_     , **a_				)




  def 	__lowercase(      self     :	Tuple     , *a_     :	List[str]     , **a_     :	str				)->       Union[str, Any]:




       """simple docstring"""
       return self.image_processor.post_process_object_detection(*a_     , **a_				)




  def 	__lowercase(      self     :	Optional[Any]     , *a_     :	str     , **a_     :	Dict				)->       Optional[int]:




       """simple docstring"""
       return self.image_processor.post_process_image_guided_detection(*a_     , **a_				)




  def 	__lowercase(      self     :	Optional[int]     , *a_     :	Tuple     , **a_     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       return self.tokenizer.batch_decode(*a_     , **a_				)




  def 	__lowercase(      self     :	Tuple     , *a_     :	Tuple     , **a_     :	Tuple				)->       List[str]:




       """simple docstring"""
       return self.tokenizer.decode(*a_     , **a_				)




  @property
  def 	__lowercase(      self     :	Tuple				)->       Any:




       """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 	__lowercase(      self     :	List[Any]				)->       List[str]:




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



