

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 a(lowercase__	):





	'''simple docstring'''
	return EnvironmentCommand()





def a(lowercase__	):





	'''simple docstring'''
	return EnvironmentCommand(args.accelerate_config_file	)






class SCREAMING_SNAKE_CASE  (		__lowerCAmelCase							):






			"""simple docstring"""




			@staticmethod
			def 			__lowerCAmelCase (    __UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= parser.add_parser('env'							)
				download_parser.set_defaults(func=lowerCAmelCase_							)
				download_parser.add_argument(
				    '--accelerate-config_file' ,				default=lowerCAmelCase_ ,				help='The accelerate config file to use for the default values in the launching script.' ,				)
				download_parser.set_defaults(func=lowerCAmelCase_							)


			def __init__(    self ,				__UpperCamelCase ,				*__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= accelerate_config_file


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'not installed'
				if is_safetensors_available():
					import safetensors

					snake_case_      		= safetensors.__version__
				elif importlib.util.find_spec('safetensors'							) is not None:
					import safetensors

					snake_case_      		= f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

				snake_case_      		= 'not installed'
				snake_case_      		= snake_case_      		= 'not found'
				if is_accelerate_available():
					import accelerate
					from accelerate.commands.config import default_config_file, load_config_from_file

					snake_case_      		= accelerate.__version__
					# Get the default from the config file.
					if self._accelerate_config_file is not None or os.path.isfile(lowerCAmelCase_							):
						snake_case_      		= load_config_from_file(self._accelerate_config_file							).to_dict()

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

				snake_case_      		= 'not installed'
				snake_case_      		= 'NA'
				if is_torch_available():
					import torch

					snake_case_      		= torch.__version__
					snake_case_      		= torch.cuda.is_available()

				snake_case_      		= 'not installed'
				snake_case_      		= 'NA'
				if is_tf_available():
					import tensorflow as tf

					snake_case_      		= tf.__version__
					try:
						# deprecated in v2.1
						snake_case_      		= tf.test.is_gpu_available()
					except AttributeError:
						# returns list of devices, convert to bool
						snake_case_      		= bool(tf.config.list_physical_devices('GPU'							)							)

				snake_case_      		= 'not installed'
				snake_case_      		= 'not installed'
				snake_case_      		= 'not installed'
				snake_case_      		= 'NA'
				if is_flax_available():
					import flax
					import jax
					import jaxlib

					snake_case_      		= flax.__version__
					snake_case_      		= jax.__version__
					snake_case_      		= jaxlib.__version__
					snake_case_      		= jax.lib.xla_bridge.get_backend().platform

				snake_case_      		= {
				    '`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(lowerCAmelCase_							)							)

				return info







			@staticmethod
			def 			__lowerCAmelCase (    __UpperCamelCase							):

				"""simple docstring"""


				return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
