

'''simple docstring'''





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(lowercase__      )
class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







				def __init__(       self     :		Optional[int]		,					*UpperCAmelCase__     :		int		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										super().__init__(*UpperCAmelCase__		,					**UpperCAmelCase__   )
										requires_backends(self		,					'''vision'''   )
										self.check_model_type(UpperCAmelCase__   )







				def __call__(       self     :		Union[str, Any]		,					UpperCAmelCase__     :		Union[str, List[str], "Image.Image", List["Image.Image"]]		,					**UpperCAmelCase__     :		List[Any]   ):
										'''simple docstring'''
										return super().__call__(UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					**UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return {}, {}, {}







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=load_image(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=image.size
										lowercase :							Optional[int]    		=self.image_processor(images=UpperCAmelCase__		,					return_tensors=self.framework   )
										return model_inputs







				def  lowerCamelCase_	(       self     :		Any		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.model(**UpperCAmelCase__   )
										return model_outputs







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









'''simple docstring'''





import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__lowerCamelCase												=						logging.get_logger(__name__)


def        a__     (					):
   # Get the sagemaker specific mp parameters from smp_options variable.
   UpperCAmelCase__	:Any      =      os.getenv('''SM_HP_MP_PARAMETERS''',   '''{}'''						)
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      UpperCAmelCase__	:Any      =      json.loads(UpperCamelCase_						)
      if "partitions" not in smp_options:
         return False
   except json.JSONDecodeError:
      return False

   # Get the sagemaker specific framework parameters from mpi_options variable.
   UpperCAmelCase__	:Optional[Any]      =      os.getenv('''SM_FRAMEWORK_PARAMS''',   '''{}'''						)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      UpperCAmelCase__	:str      =      json.loads(UpperCamelCase_						)
      if not mpi_options.get('''sagemaker_mpi_enabled''',   UpperCamelCase_						):
         return False
   except json.JSONDecodeError:
      return False
   # Lastly, check if the `smdistributed` module is present.
   return importlib.util.find_spec('''smdistributed'''						) is not None


if is_sagemaker_model_parallel_available():
    import smdistributed.modelparallel.torch as smp

    smp.init()




@dataclass
class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					field(
     default=""						, metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}						, )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    super().__post_init__()
    warnings.warn(
        '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
        '''`TrainingArguments` instead.'''			,       __lowerCamelCase			,       )


 @cached_property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    logger.info('''PyTorch: setting up devices''' )
    if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
       logger.warning(
           '''torch.distributed process group is initialized, but local_rank == -1. '''
           '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch''' )
    if self.no_cuda:
       UpperCAmelCase__	:Union[str, Any]      =      torch.device('''cpu''' )
       UpperCAmelCase__	:Dict      =      0
    elif is_sagemaker_model_parallel_available():
       UpperCAmelCase__	:Dict      =      smp.local_rank()
       UpperCAmelCase__	:List[str]      =      torch.device('''cuda'''			,       __lowerCamelCase )
       UpperCAmelCase__	:Optional[int]      =      1
    elif is_sagemaker_dp_enabled():
       import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

       torch.distributed.init_process_group(backend='''smddp'''			,       timeout=self.ddp_timeout_delta )
       UpperCAmelCase__	:int      =      int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK''' ) )
       UpperCAmelCase__	:int      =      torch.device('''cuda'''			,       self.local_rank )
       UpperCAmelCase__	:Union[str, Any]      =      1
    elif self.local_rank == -1:
       # if n_gpu is > 1 we'll use nn.DataParallel.
       # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
       # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
       # trigger an error that a device index is missing. Index 0 takes into account the
       # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
       # will use the first GPU in that env, i.e. GPU#1
       UpperCAmelCase__	:str      =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
       # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
       # the default value.
       UpperCAmelCase__	:str      =      torch.cuda.device_count()
    else:
       # Here, we'll use torch.distributed.
       # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
       if not torch.distributed.is_initialized():
          torch.distributed.init_process_group(backend='''nccl'''			,       timeout=self.ddp_timeout_delta )
       UpperCAmelCase__	:str      =      torch.device('''cuda'''			,       self.local_rank )
       UpperCAmelCase__	:Union[str, Any]      =      1

    if device.type == "cuda":
       torch.cuda.set_device(__lowerCamelCase )

    return device


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    if is_sagemaker_model_parallel_available():
       return smp.dp_size()

    return super().world_size


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict ):
    return not is_sagemaker_model_parallel_available()





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return False



