





from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase_     							= logging.get_logger(__name__)






@add_end_docstrings(__lowerCamelCase				)
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def __init__(    self,		*__magic_name__,		**__magic_name__							) ->      Tuple:






       """simple docstring"""
       super().__init__(*__magic_name__,		**__magic_name__							)
       requires_backends(self,		'''vision'''							)
       self.check_model_type(__magic_name__							)




   def __call__(    self,		__magic_name__,		**__magic_name__							) ->      List[Any]:






       """simple docstring"""
       return super().__call__(__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Dict:






       """simple docstring"""
       return {}, {}, {}




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      load_image(__magic_name__							)
       UpperCamelCase__							:				Any          =      image.size
       UpperCamelCase__							:				Union[str, Any]          =      self.image_processor(images=__magic_name__,		return_tensors=self.framework							)
       return model_inputs




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.model(**__magic_name__							)
       return model_outputs




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      model_outputs.predicted_depth
       UpperCamelCase__							:				List[str]          =      torch.nn.functional.interpolate(
           predicted_depth.unsqueeze(1							),		size=self.image_size[::-1],		mode='''bicubic''',		align_corners=__magic_name__							)
       UpperCamelCase__							:				str          =      prediction.squeeze().cpu().numpy()
       UpperCamelCase__							:				List[Any]          =      (output * 255 / np.max(__magic_name__							)).astype('''uint8'''							)
       UpperCamelCase__							:				str          =      Image.fromarray(__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      {}
       UpperCamelCase__							:				Optional[Any]          =      predicted_depth
       UpperCamelCase__							:				str          =      depth
       return output_dict








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_     							= {
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= ['ConvBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ConvBertForMaskedLM',
      'ConvBertForMultipleChoice',
      'ConvBertForQuestionAnswering',
      'ConvBertForSequenceClassification',
      'ConvBertForTokenClassification',
      'ConvBertLayer',
      'ConvBertModel',
      'ConvBertPreTrainedModel',
      'load_tf_weights_in_convbert',
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_     							= [
      'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFConvBertForMaskedLM',
      'TFConvBertForMultipleChoice',
      'TFConvBertForQuestionAnswering',
      'TFConvBertForSequenceClassification',
      'TFConvBertForTokenClassification',
      'TFConvBertLayer',
      'TFConvBertModel',
      'TFConvBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
  from .tokenization_convbert import ConvBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_convbert_fast import ConvBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_convbert import (
        CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConvBertForMaskedLM,
        ConvBertForMultipleChoice,
        ConvBertForQuestionAnswering,
        ConvBertForSequenceClassification,
        ConvBertForTokenClassification,
        ConvBertLayer,
        ConvBertModel,
        ConvBertPreTrainedModel,
        load_tf_weights_in_convbert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_convbert import (
        TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertLayer,
        TFConvBertModel,
        TFConvBertPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

