



'''simple docstring'''


import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
		import jax







@require_flax
class 	UpperCAmelCase	(		__a					,	unittest.TestCase):


	'''simple docstring'''




	__magic_name__					: Optional[Any]  =      FlaxAutoencoderKL




	@property
	def       lowercase_  (   self) ->      Tuple:



						"""simple docstring"""







						a_       		=4
						a_       		=3
						a_       		=(3_2, 3_2)

						a_       		=jax.random.PRNGKey(0)
						a_       		=jax.random.uniform(lowerCAmelCase_							,  ((batch_size, num_channels) + sizes))

						return {"sample": image, "prng_key": prng_key}




	def       lowercase_  (   self) ->      Tuple:



						"""simple docstring"""







						a_       		={
						    "block_out_channels": [3_2, 6_4],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						a_       		=self.dummy_input
						return init_dict, inputs_dict







'''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)

lowercase							       =			logging.getLogger()






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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


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







@require_torch_tpu
class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




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



      """simple docstring"""







      import xla_spawn

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

      with patch.object(lowerCAmelCase_							,  "argv"							,  lowerCAmelCase_):
           a_       		=time()
           xla_spawn.main()
           a_       		=time()

           a_       		=get_results(lowerCAmelCase_)
           self.assertGreaterEqual(result["eval_accuracy"]							,  0.7_5)

           # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
           self.assertLess(end - start							,  5_0_0)




 def       lowercase_  (   self) ->      Tuple:



      """simple docstring"""







      import xla_spawn

      a_       		="\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            ".split()
      with patch.object(lowerCAmelCase_							,  "argv"							,  lowerCAmelCase_):
           xla_spawn.main()


