
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:




    class 	__A:

      """simple docstring"""







      @staticmethod
      def        UpperCAmelCase_					(*SCREAMING_SNAKE_CASE_       ,			**SCREAMING_SNAKE_CASE_						):
           pass






def 						__magic_name__ (    __a			:       Image						):



     '''simple docstring'''




     UpperCamelCase__				=				hashlib.mda(image.tobytes()						)
     return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	__A(				unittest.TestCase ):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__			    =			MODEL_FOR_DEPTH_ESTIMATION_MAPPING







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				DepthEstimationPipeline(model=UpperCamelCase__       ,			image_processor=UpperCamelCase__						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=				depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
       self.assertEqual({"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)}       ,			UpperCamelCase__						)
       import datasets

       UpperCamelCase__				=				datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,			"""image"""       ,			split="""test"""						)
       UpperCamelCase__				=				depth_estimator(
           [
               Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
               """http://images.cocodataset.org/val2017/000000039769.jpg""",
               # RGBA
               dataset[0]["""file"""],
               # LA
               dataset[1]["""file"""],
               # L
               dataset[2]["""file"""],
           ]						)
       self.assertEqual(
           [
               {"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)},
               {"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)},
               {"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)},
               {"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)},
               {"""predicted_depth""": ANY(torch.Tensor						), """depth""": ANY(Image.Image						)},
           ]       ,			UpperCamelCase__       ,			)







  @require_tf
  @unittest.skip("""Depth estimation is not implemented in TF"""						)
  def        UpperCAmelCase_					(self						):
       pass







  @slow
  @require_torch
  def        UpperCAmelCase_					(self						):
       UpperCamelCase__				=				"Intel/dpt-large"
       UpperCamelCase__				=				pipeline("""depth-estimation"""       ,			model=UpperCamelCase__						)
       UpperCamelCase__				=				depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
       UpperCamelCase__				=				hashimage(outputs["""depth"""]						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()						)       ,			29.304						)
       self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()						)       ,			2.662						)







  @require_torch
  def        UpperCAmelCase_					(self						):
       self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""						)









"""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


__snake_case   			=    logging.get_logger(__name__)







def   __lowerCAmelCase						(						)	->						str:


    """simple docstring"""





    snake_case    :      Dict      					=    os.getenv("SM_HP_MP_PARAMETERS"						,							"{}" )
    try:
        # Parse it and check the field "partitions" is included, it is required for model parallel.
        snake_case    :      Optional[int]      					=    json.loads(lowercase )
        if "partitions" not in smp_options:
            return False
    except json.JSONDecodeError:
        return False

    # Get the sagemaker specific framework parameters from mpi_options variable.
    snake_case    :      Optional[int]      					=    os.getenv("SM_FRAMEWORK_PARAMS"						,							"{}" )
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        snake_case    :      Any      					=    json.loads(lowercase )
        if not mpi_options.get("sagemaker_mpi_enabled"						,							lowercase ):
            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 _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       str							  =  field(
       default=''''''    ,							metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''}    ,							)







   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       super().__post_init__()
       warnings.warn(
           "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
           "`TrainingArguments` instead."						,	UpperCamelCase__						,	)







   @cached_property
   def  lowerCamelCase		(	self						)	->		"torch.device":

       '''simple docstring'''




       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:
           snake_case    :      Optional[Any]      					=    torch.device("cpu"						)
           snake_case    :      List[Any]      					=    0
       elif is_sagemaker_model_parallel_available():
           snake_case    :      Tuple      					=    smp.local_rank()
           snake_case    :      int      					=    torch.device("cuda"						,	UpperCamelCase__						)
           snake_case    :      Dict      					=    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						)
           snake_case    :      Any      					=    int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"						)						)
           snake_case    :      Optional[Any]      					=    torch.device("cuda"						,	self.local_rank						)
           snake_case    :      str      					=    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
           snake_case    :      List[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.
           snake_case    :      Optional[Any]      					=    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						)
           snake_case    :      Any      					=    torch.device("cuda"						,	self.local_rank						)
           snake_case    :      Dict      					=    1

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

       return device







   @property
   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       if is_sagemaker_model_parallel_available():
           return smp.dp_size()

       return super().world_size







   @property
   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       return not is_sagemaker_model_parallel_available()







   @property
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       return False





