






"""simple docstring"""
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      SCREAMING_SNAKE_CASE  :





								"""simple docstring"""




								@staticmethod
								def    lowerCamelCase(*lowerCAmelCase_     ,     **lowerCAmelCase_							):
									pass






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Dict	    =   hashlib.mda(image.tobytes()					)
	return m.hexdigest()




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





	"""simple docstring"""




	_A       :				Optional[int]    =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Any	    =   DepthEstimationPipeline(model=lowerCAmelCase_     ,     image_processor=lowerCAmelCase_							)
		return depth_estimator, [
		    "./tests/fixtures/tests_samples/COCO/000000039769.png",
		    "./tests/fixtures/tests_samples/COCO/000000039769.png",
		]





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

		A_		:						List[str]	    =   datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""     ,     """image"""     ,     split="""test"""							)
		A_		:						Optional[Any]	    =   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							)},
		    ]     ,     lowerCAmelCase_     ,     )





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





	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   """Intel/dpt-large"""
		A_		:						Optional[Any]	    =   pipeline("""depth-estimation"""     ,     model=lowerCAmelCase_							)
		A_		:						List[str]	    =   depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""							)
		A_		:						Union[str, Any]	    =   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    lowerCamelCase(self							):
		# This is highly irregular to have no small tests.
		self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""							)









"""simple docstring"""
import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
_lowerCAmelCase         =							logging.getLogger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   git.Repo(search_parent_directories=snake_case__					)
	A_		:						List[str]	    =   {
	    """repo_id""": str(snake_case__					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	}

	with open(os.path.join(snake_case__   ,   """git_log.json"""					)   ,   """w"""					) as f:
		json.dump(snake_case__   ,   snake_case__   ,   indent=4					)






def 			__UpperCamelCase	(		snake_case__					):
	if params.n_gpu <= 0:
		A_		:						Dict	    =   0
		A_		:						str	    =   -1
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   False
		return

	assert torch.cuda.is_available()

	logger.info("""Initializing GPUs"""					)
	if params.n_gpu > 1:
		assert params.local_rank != -1

		A_		:						str	    =   int(os.environ["""WORLD_SIZE"""]					)
		A_		:						int	    =   int(os.environ["""N_GPU_NODE"""]					)
		A_		:						int	    =   int(os.environ["""RANK"""]					)

		# number of nodes / node ID
		A_		:						Optional[int]	    =   params.world_size // params.n_gpu_per_node
		A_		:						Optional[int]	    =   params.global_rank // params.n_gpu_per_node
		A_		:						Tuple	    =   True

		assert params.n_nodes == int(os.environ["""N_NODES"""]					)
		assert params.node_id == int(os.environ["""NODE_RANK"""]					)

	# local job (single GPU)
	else:
		assert params.local_rank == -1

		A_		:						Dict	    =   1
		A_		:						Tuple	    =   0
		A_		:						Dict	    =   0
		A_		:						Optional[int]	    =   0
		A_		:						List[str]	    =   1
		A_		:						List[Any]	    =   1
		A_		:						List[Any]	    =   False

	# sanity checks
	assert params.n_nodes >= 1
	assert 0 <= params.node_id < params.n_nodes
	assert 0 <= params.local_rank <= params.global_rank < params.world_size
	assert params.world_size == params.n_nodes * params.n_gpu_per_node

	# define whether this is the master process / if we are in multi-node distributed mode
	A_		:						Optional[int]	    =   params.node_id == 0 and params.local_rank == 0
	A_		:						Optional[Any]	    =   params.n_nodes > 1

	# summary
	A_		:						str	    =   F"""--- Global rank: {params.global_rank} - """
	logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes					)
	logger.info(PREFIX + """Node ID        : %i""" % params.node_id					)
	logger.info(PREFIX + """Local rank     : %i""" % params.local_rank					)
	logger.info(PREFIX + """World size     : %i""" % params.world_size					)
	logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node					)
	logger.info(PREFIX + """Master         : %s""" % str(params.is_master					)					)
	logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node					)					)
	logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu					)					)
	logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()					)

	# set GPU device
	torch.cuda.set_device(params.local_rank					)

	# initialize multi-GPU
	if params.multi_gpu:
		logger.info("""Initializing PyTorch distributed"""					)
		torch.distributed.init_process_group(
		    init_method="""env://"""   ,   backend="""nccl"""   ,   )






def 			__UpperCamelCase	(		snake_case__					):
	np.random.seed(args.seed					)
	torch.manual_seed(args.seed					)
	if args.n_gpu > 0:
		torch.cuda.manual_seed_all(args.seed					)

