
import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any)      ->					Optional[int]:






							'''simple docstring'''





							return EnvironmentCommand()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					str:






							'''simple docstring'''





							return EnvironmentCommand(args.accelerate_config_file)




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	@staticmethod
	def      _lowerCamelCase       (      a	:ArgumentParser		)	->				Any:
								__UpperCamelCase			:     Dict         =      parser.add_parser("env"		)
								download_parser.set_defaults(func=a		)
								download_parser.add_argument(
								    "--accelerate-config_file"  ,     default=a  ,     help="The accelerate config file to use for the default values in the launching script."  ,     )
								download_parser.set_defaults(func=a		)







	def __init__(      self	:int  ,     a	:List[str]  ,     *a	:Any		)	->				None:
								__UpperCamelCase			:     Dict         =      accelerate_config_file







	def      _lowerCamelCase       (      self	:Tuple		)	->				Optional[int]:
								__UpperCamelCase			:     Any         =      "not installed"
								if is_safetensors_available():
															import safetensors

															__UpperCamelCase			:     Union[str, Any]         =      safetensors.__version__
								elif importlib.util.find_spec("safetensors"		) is not None:
															import safetensors

															__UpperCamelCase			:     Tuple         =      f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

								__UpperCamelCase			:     Optional[int]         =      "not installed"
								__UpperCamelCase			:     Tuple         =      "not found"
								if is_accelerate_available():
															import accelerate
															from accelerate.commands.config import default_config_file, load_config_from_file

															__UpperCamelCase			:     List[Any]         =      accelerate.__version__
															# Get the default from the config file.
															if self._accelerate_config_file is not None or os.path.isfile(a		):
																						__UpperCamelCase			:     Union[str, Any]         =      load_config_from_file(self._accelerate_config_file		).to_dict()

															__UpperCamelCase			:     Any         =      (
															    "\n".join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]		)
															    if isinstance(a  ,     a		)
															    else f'\t{accelerate_config}'
															)

								__UpperCamelCase			:     Optional[int]         =      "not installed"
								__UpperCamelCase			:     Any         =      "NA"
								if is_torch_available():
															import torch

															__UpperCamelCase			:     List[Any]         =      torch.__version__
															__UpperCamelCase			:     List[Any]         =      torch.cuda.is_available()

								__UpperCamelCase			:     Any         =      "not installed"
								__UpperCamelCase			:     Union[str, Any]         =      "NA"
								if is_tf_available():
															import tensorflow as tf

															__UpperCamelCase			:     Union[str, Any]         =      tf.__version__
															try:
																						# deprecated in v2.1
																						__UpperCamelCase			:     Tuple         =      tf.test.is_gpu_available()
															except AttributeError:
																						# returns list of devices, convert to bool
																						__UpperCamelCase			:     str         =      bool(tf.config.list_physical_devices("GPU"		)		)

								__UpperCamelCase			:     Any         =      "not installed"
								__UpperCamelCase			:     Optional[int]         =      "not installed"
								__UpperCamelCase			:     Union[str, Any]         =      "not installed"
								__UpperCamelCase			:     List[Any]         =      "NA"
								if is_flax_available():
															import flax
															import jax
															import jaxlib

															__UpperCamelCase			:     int         =      flax.__version__
															__UpperCamelCase			:     Any         =      jax.__version__
															__UpperCamelCase			:     int         =      jaxlib.__version__
															__UpperCamelCase			:     int         =      jax.lib.xla_bridge.get_backend().platform

								__UpperCamelCase			:     Dict         =      {
								    "`transformers` version": version,
								    "Platform": platform.platform(),
								    "Python version": platform.python_version(),
								    "Huggingface_hub version": huggingface_hub.__version__,
								    "Safetensors version": f'{safetensors_version}',
								    "Accelerate version": f'{accelerate_version}',
								    "Accelerate config": f'{accelerate_config_str}',
								    "PyTorch version (GPU?)": f'{pt_version} ({pt_cuda_available})',
								    "Tensorflow version (GPU?)": f'{tf_version} ({tf_cuda_available})',
								    "Flax version (CPU?/GPU?/TPU?)": f'{flax_version} ({jax_backend})',
								    "Jax version": f'{jax_version}',
								    "JaxLib version": f'{jaxlib_version}',
								    "Using GPU in script?": "<fill in>",
								    "Using distributed or parallel set-up in script?": "<fill in>",
								}

								print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"		)
								print(self.format_dict(a		)		)

								return info







	@staticmethod
	def      _lowerCamelCase       (      a	:Optional[int]		)	->				List[Any]:
								return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

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 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




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




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      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		)},
								    ]  ,     a  ,     )







	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF"		)
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







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