



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  UpperCamelCase				( UpperCamelCase_ ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				Optional[int]:






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



     def __call__(					self				,				A_				,				**A_						) ->				Dict:






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



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Dict:






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



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				load_image(__A						)
           _lowerCamelCase			    =				image.size
           _lowerCamelCase			    =				self.image_processor(images=__A				,				return_tensors=self.framework						)
           return model_inputs



     def 		UpperCamelCase_						(					self				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.model(**__A						)
           return model_outputs





     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				model_outputs.predicted_depth
           _lowerCamelCase			    =				torch.nn.functional.interpolate(
               predicted_depth.unsqueeze(1						)				,				size=self.image_size[::-1]				,				mode='''bicubic'''				,				align_corners=__A						)
           _lowerCamelCase			    =				prediction.squeeze().cpu().numpy()
           _lowerCamelCase			    =				(output * 2_55 / np.max(__A						)).astype('''uint8'''						)
           _lowerCamelCase			    =				Image.fromarray(__A						)
           _lowerCamelCase			    =				{}
           _lowerCamelCase			    =				predicted_depth
           _lowerCamelCase			    =				depth
           return output_dict
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)