





'''simple docstring'''


import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 		__lowercase			(					A__ ,       A__ ,       unittest.TestCase      ):
			lowerCamelCase     :						Dict		 =     IFImgaImgPipeline
			lowerCamelCase     :						Any		 =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
			lowerCamelCase     :						Tuple		 =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			lowerCamelCase     :						str		 =     PipelineTesterMixin.required_optional_params - {"latents"}

			def     UpperCAmelCase__	(self ):
								return self._get_dummy_components()

			def     UpperCAmelCase__	(self     ,				A     ,				A=0 ):






								if str(_A ).startswith('''mps''' ):
													lowerCamelCase_   : List[Any]       =						torch.manual_seed(_A )
								else:
													lowerCamelCase_   : Any       =						torch.Generator(device=_A ).manual_seed(_A )

								lowerCamelCase_   : int       =						floats_tensor((1, 3, 3_2, 3_2)     ,				rng=random.Random(_A ) ).to(_A )

								lowerCamelCase_   : Tuple       =						{
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs

			def     UpperCAmelCase__	(self ):
								self._test_save_load_optional_components()

			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()     ,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,				)
			def     UpperCAmelCase__	(self ):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )

			@unittest.skipIf(torch_device != '''cuda'''     ,				reason='''float16 requires CUDA''' )
			def     UpperCAmelCase__	(self ):
								super().test_save_load_floataa(expected_max_diff=1E-1 )

			@unittest.skipIf(torch_device != '''cuda'''     ,				reason='''float16 requires CUDA''' )
			def     UpperCAmelCase__	(self ):
								super().test_floataa_inference(expected_max_diff=1E-1 )

			def     UpperCAmelCase__	(self ):
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )

			def     UpperCAmelCase__	(self ):
								self._test_save_load_local()






			def     UpperCAmelCase__	(self ):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2     ,				)




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


