





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'pegasus'
      A_						:				Union[str, Any]							=    ['past_key_values']
      A_						:				Optional[Any]							=    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


      def __init__(self       :      Any				,			a__       :      Optional[int]=5_0265				,			a__       :      int=1024				,			a__       :      List[str]=12				,			a__       :      List[Any]=4096				,			a__       :      List[str]=16				,			a__       :      Any=12				,			a__       :      Union[str, Any]=4096				,			a__       :      Tuple=16				,			a__       :      Optional[Any]=0.0				,			a__       :      Any=0.0				,			a__       :      List[str]=True				,			a__       :      Optional[int]=True				,			a__       :      Union[str, Any]="gelu"				,			a__       :      List[str]=1024				,			a__       :      int=0.1				,			a__       :      List[str]=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      Optional[int]=0				,			a__       :      Tuple=False				,			a__       :      List[Any]=0				,			a__       :      Any=1				,			a__       :      List[Any]=1				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         __snake_case							       =   vocab_size
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   d_model
         __snake_case							       =   encoder_ffn_dim
         __snake_case							       =   encoder_layers
         __snake_case							       =   encoder_attention_heads
         __snake_case							       =   decoder_ffn_dim
         __snake_case							       =   decoder_layers
         __snake_case							       =   decoder_attention_heads
         __snake_case							       =   dropout
         __snake_case							       =   attention_dropout
         __snake_case							       =   activation_dropout
         __snake_case							       =   activation_function
         __snake_case							       =   init_std
         __snake_case							       =   encoder_layerdrop
         __snake_case							       =   decoder_layerdrop
         __snake_case							       =   use_cache
         __snake_case							       =   encoder_layers
         __snake_case							       =   scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=a__				,			eos_token_id=a__				,			is_encoder_decoder=a__				,			decoder_start_token_id=a__				,			forced_eos_token_id=a__				,			**a__				,			)


      @property
      def    a (self       :      Any      ):





         """simple docstring"""



         return self.encoder_attention_heads


      @property
      def    a (self       :      Tuple      ):





         """simple docstring"""



         return self.d_model








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

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



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


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



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

