


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(_UpperCAmelCase      )
class        lowerCamelCase__	(							_UpperCAmelCase      ):






   """simple docstring"""






   def __init__(							self      ,						*snake_case      ,						**snake_case     ):

      '''simple docstring'''


      super().__init__(*lowercase__      ,						**lowercase__     )
      requires_backends(self      ,						"vision"     )
      self.check_model_type(lowercase__     )




   def __call__(							self      ,						snake_case      ,						**snake_case     ):

      '''simple docstring'''


      return super().__call__(lowercase__      ,						**lowercase__     )




   def 				snake_case__				(							self      ,						**snake_case     ):

      '''simple docstring'''


      return {}, {}, {}




   def 				snake_case__				(							self      ,						snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       load_image(lowercase__     )
      UpperCamelCase__					=       image.size
      UpperCamelCase__					=       self.image_processor(images=lowercase__      ,						return_tensors=self.framework     )
      return model_inputs




   def 				snake_case__				(							self      ,						snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       self.model(**lowercase__     )
      return model_outputs







   def 				snake_case__				(							self      ,						snake_case     ):

      '''simple docstring'''


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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		'ibert'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=False      ,						snake_case="none"      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case      ,						bos_token_id=snake_case      ,						eos_token_id=snake_case      ,						**snake_case     )

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       quant_mode
						UpperCamelCase__					=       force_dequant







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__					=       {0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__					=       {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]     )


