
'''simple docstring'''





import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase__	=							logging.getLogger()


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	{}
		_UpperCamelCase						    =	os.path.join(_snake_case						,  """all_results.json""" )
		if os.path.exists(_snake_case ):
				with open(_snake_case						,  """r""" ) as f:
						_UpperCamelCase						    =	json.load(_snake_case )
		else:
				raise ValueError(F'can\'t find {path}' )
		return results


lowerCamelCase__	=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







@require_torch_tpu
class        lowerCAmelCase__      (   __lowercase	):

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


								'''simple docstring'''

								import xla_spawn

								_UpperCamelCase						    =	self.get_auto_remove_tmp_dir()
								_UpperCamelCase						    =	F'\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

								with patch.object(a       ,							"""argv"""       ,							a				):
										_UpperCamelCase						    =	time()
										xla_spawn.main()
										_UpperCamelCase						    =	time()

										_UpperCamelCase						    =	get_results(a				)
										self.assertGreaterEqual(result["""eval_accuracy"""]       ,							0.75				)

										# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
										self.assertLess(end - start       ,							5_00				)







						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								import xla_spawn

								_UpperCamelCase						    =	"""
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            """.split()
								with patch.object(a       ,							"""argv"""       ,							a				):
										xla_spawn.main()





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(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

						def  A_				(			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  A_				(			a				)					->      Dict:


								'''simple docstring'''

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



