


'''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__       ):
    __UpperCAmelCase             =     ['image_processor', 'tokenizer']
    __UpperCAmelCase             =     'OneFormerImageProcessor'
    __UpperCAmelCase             =     ('CLIPTokenizer', 'CLIPTokenizerFast')


    def __init__(					self  , a=None  , a=None  , a = 77  , a = 77  , **a   ):
          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        __a							(					self  , a=None  , a=77   ):
          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   ):

          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        __a							(					self  , a=None  , a=None  , a=None  , **a   ):

          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        __a							(					self  , *a  , **a   ):
          return self.image_processor.post_process_semantic_segmentation(*a  , **a   )


    def        __a							(					self  , *a  , **a   ):
          return self.image_processor.post_process_instance_segmentation(*a  , **a   )


    def        __a							(					self  , *a  , **a   ):
          return self.image_processor.post_process_panoptic_segmentation(*a  , **a   )









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__  : Any				=	{
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Tuple				=	['CodeGenTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Dict				=	[
      'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
      'CodeGenForCausalLM',
      'CodeGenModel',
      'CodeGenPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  from .tokenization_codegen import CodeGenTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_codegen_fast import CodeGenTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_codegen import (
        CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
        CodeGenForCausalLM,
        CodeGenModel,
        CodeGenPreTrainedModel,
    )

else:
  import sys

  a__  : Optional[int]				=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





