
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def 				A	( lowercase			)       ->  List[Any]:
		'''simple docstring'''

		return EnvironmentCommand()


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					parser.add_parser('env'	)
				download_parser.set_defaults(func=A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					huggingface_hub.__version__

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

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

				UpperCamelCase          =					'not installed'
				if is_transformers_available():
						import transformers

						UpperCamelCase          =					transformers.__version__

				UpperCamelCase          =					'not installed'
				if is_accelerate_available():
						import accelerate

						UpperCamelCase          =					accelerate.__version__

				UpperCamelCase          =					'not installed'
				if is_xformers_available():
						import xformers

						UpperCamelCase          =					xformers.__version__

				UpperCamelCase          =					{
				    '`diffusers` version': version,
				    'Platform': platform.platform(),
				    'Python version': platform.python_version(),
				    'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
				    'Huggingface_hub version': hub_version,
				    'Transformers version': transformers_version,
				    'Accelerate version': accelerate_version,
				    'xFormers version': xformers_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 __UpperCamelCase			(			A_	)  -> Optional[int]:
				"""simple docstring"""
				return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"


import os
import sys
import unittest


_UpperCAmelCase :			str              =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_UpperCAmelCase :			Tuple              =	os.path.join(git_repo_path, "src", "diffusers")


class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					find_backend('    if not is_torch_available():'	)
				self.assertEqual(A_    ,				'torch'	)

				# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
				# self.assertEqual(backend_with_underscore, "tensorflow_text")

				UpperCamelCase          =					find_backend('    if not (is_torch_available() and is_transformers_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers'	)

				# double_backend_with_underscore = find_backend(
				#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
				# )
				# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

				UpperCamelCase          =					find_backend(
				    '    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'	)
				self.assertEqual(A_    ,				'torch_and_transformers_and_onnx'	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					read_init()
				# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
				self.assertIn('torch'    ,				A_	)
				self.assertIn('torch_and_transformers'    ,				A_	)
				self.assertIn('flax_and_transformers'    ,				A_	)
				self.assertIn('torch_and_transformers_and_onnx'    ,				A_	)

				# Likewise, we can't assert on the exact content of a key
				self.assertIn('UNet2DModel'    ,				objects['torch']	)
				self.assertIn('FlaxUNet2DConditionModel'    ,				objects['flax']	)
				self.assertIn('StableDiffusionPipeline'    ,				objects['torch_and_transformers']	)
				self.assertIn('FlaxStableDiffusionPipeline'    ,				objects['flax_and_transformers']	)
				self.assertIn('LMSDiscreteScheduler'    ,				objects['torch_and_scipy']	)
				self.assertIn('OnnxStableDiffusionPipeline'    ,				objects['torch_and_transformers_and_onnx']	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					create_dummy_object('CONSTANT'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				'\nCONSTANT = None\n'	)

				UpperCamelCase          =					create_dummy_object('function'    ,				'\'torch\''	)
				self.assertEqual(
				    A_    ,				'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'	)

				UpperCamelCase          =					'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, \'torch\')\n'
				UpperCamelCase          =					create_dummy_object('FakeClass'    ,				'\'torch\''	)
				self.assertEqual(A_    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, ["torch"])\n'
				UpperCamelCase          =					create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}	)
				self.assertEqual(dummy_files['torch']    ,				A_	)
