

'''simple docstring'''




from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch



class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: int      =   ["image_processor", "tokenizer"]
  __lowerCAmelCase					: str      =   "OneFormerImageProcessor"
  __lowerCAmelCase					: Tuple      =   ("CLIPTokenizer", "CLIPTokenizerFast")
  def __init__(     self   , a_=None   , a_=None   , a_ = 7_7   , a_ = 7_7   , **a_     )							->				Optional[int]:


    """simple docstring"""
    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`.'     )

    UpperCAmelCase													=	max_seq_length
    UpperCAmelCase													=	task_seq_length

    super().__init__(a_   , a_     )
  def 						snake_case_			(     self   , a_=None   , a_=7_7     )							->				Optional[int]:


    """simple docstring"""
    if text_list is None:
      raise ValueError('tokens cannot be None.'     )

    UpperCAmelCase													=	self.tokenizer(a_   , padding='max_length'   , max_length=a_   , truncation=a_     )

    UpperCAmelCase							,      UpperCAmelCase													=	tokens['attention_mask'], tokens['input_ids']

    UpperCAmelCase													=	[]
    for attn_mask, input_id in zip(a_   , a_     ):
      UpperCAmelCase													=	torch.tensor(a_     ) * torch.tensor(a_     )
      token_inputs.append(token.unsqueeze(0     )     )

    UpperCAmelCase													=	torch.cat(a_   , dim=0     )
    return token_inputs
  def __call__(     self   , a_=None   , a_=None   , a_=None   , **a_     )							->				Dict:


    """simple docstring"""

    if task_inputs is None:
      raise ValueError('You have to specify the task_input. Found None.'     )
    elif images is None:
      raise ValueError('You have to specify the image. Found None.'     )

    if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
      raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

    UpperCAmelCase													=	self.image_processor(a_   , a_   , a_   , **a_     )

    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	[task_inputs]

    if isinstance(a_   , a_     ) and all(isinstance(a_   , a_     ) for task_input in task_inputs     ):
      UpperCAmelCase													=	[]
      for task in task_inputs:
        UpperCAmelCase													=	F'''the task is {task}'''
        task_token_inputs.append(a_     )
      UpperCAmelCase													=	self._preprocess_text(a_   , max_length=self.task_seq_length     )
    else:
      raise TypeError('Task Inputs should be a string or a list of strings.'     )

    if hasattr(a_   , 'text_inputs'     ):
      UpperCAmelCase													=	encoded_inputs.text_inputs

      UpperCAmelCase													=	[]
      for texts in texts_list:
        UpperCAmelCase													=	self._preprocess_text(a_   , max_length=self.max_seq_length     )
        text_inputs.append(text_input_list.unsqueeze(0     )     )

      UpperCAmelCase													=	torch.cat(a_   , dim=0     )

    return encoded_inputs
  def 						snake_case_			(     self   , a_=None   , a_=None   , a_=None   , **a_     )							->				List[str]:


    """simple docstring"""

    if task_inputs is None:
      raise ValueError('You have to specify the task_input. Found None.'     )
    elif images is None:
      raise ValueError('You have to specify the image. Found None.'     )

    if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
      raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

    UpperCAmelCase													=	self.image_processor.encode_inputs(a_   , a_   , a_   , **a_     )

    if isinstance(a_   , a_     ):
      UpperCAmelCase													=	[task_inputs]

    if isinstance(a_   , a_     ) and all(isinstance(a_   , a_     ) for task_input in task_inputs     ):
      UpperCAmelCase													=	[]
      for task in task_inputs:
        UpperCAmelCase													=	F'''the task is {task}'''
        task_token_inputs.append(a_     )
      UpperCAmelCase													=	self._preprocess_text(a_   , max_length=self.task_seq_length     )
    else:
      raise TypeError('Task Inputs should be a string or a list of strings.'     )

    if hasattr(a_   , 'text_inputs'     ):
      UpperCAmelCase													=	encoded_inputs.text_inputs

      UpperCAmelCase													=	[]
      for texts in texts_list:
        UpperCAmelCase													=	self._preprocess_text(a_   , max_length=self.max_seq_length     )
        text_inputs.append(text_input_list.unsqueeze(0     )     )

      UpperCAmelCase													=	torch.cat(a_   , dim=0     )

    return encoded_inputs
  def 						snake_case_			(     self   , *a_   , **a_     )							->				List[str]:


    """simple docstring"""
    return self.image_processor.post_process_semantic_segmentation(*a_   , **a_     )
  def 						snake_case_			(     self   , *a_   , **a_     )							->				Optional[Any]:


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





  def 						snake_case_			(     self   , *a_   , **a_     )							->				Dict:


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





'''simple docstring'''




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

_a				: List[Any] 							=						logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


  def       lowerCamelCase__		(						cls      :   Dict						):
    return cls


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Union[Tuple, List]       ,   SCREAMING_SNAKE_CASE      :   Optional[Union[List["torch.Generator"], "torch.Generator"]] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.device"] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.dtype"] = None       ,   SCREAMING_SNAKE_CASE      :   Optional["torch.layout"] = None       ,   ):
  UpperCAmelCase													=	device
  UpperCAmelCase													=	shape[0]

  UpperCAmelCase													=	layout or torch.strided
  UpperCAmelCase													=	device or torch.device('cpu'						)

  if generator is not None:
    UpperCAmelCase													=	generator.device.type if not isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) else generator[0].device.type
    if gen_device_type != device.type and gen_device_type == "cpu":
      UpperCAmelCase													=	'cpu'
      if device != "mps":
        logger.info(
            f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
            f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
            f''' slighly speed up this function by passing a generator that was created on the {device} device.'''						)
    elif gen_device_type != device.type and gen_device_type == "cuda":
      raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''						)

  if isinstance(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						):
    UpperCAmelCase													=	(1,) + shape[1:]
    UpperCAmelCase													=	[
        torch.randn(SCREAMING_SNAKE_CASE       ,   generator=generator[i]       ,   device=SCREAMING_SNAKE_CASE       ,   dtype=SCREAMING_SNAKE_CASE       ,   layout=SCREAMING_SNAKE_CASE						)
        for i in range(SCREAMING_SNAKE_CASE						)
    ]
    UpperCAmelCase													=	torch.cat(SCREAMING_SNAKE_CASE       ,   dim=0						).to(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	torch.randn(SCREAMING_SNAKE_CASE       ,   generator=SCREAMING_SNAKE_CASE       ,   device=SCREAMING_SNAKE_CASE       ,   dtype=SCREAMING_SNAKE_CASE       ,   layout=SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)

  return latents



def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Tuple						):
  if is_torch_version('<'       ,   '2.0.0'						) or not hasattr(SCREAMING_SNAKE_CASE       ,   '_dynamo'						):
    return False
  return isinstance(SCREAMING_SNAKE_CASE       ,   torch._dynamo.eval_frame.OptimizedModule						)


