





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 			UpperCamelCase_		(  _lowerCamelCase							,  _lowerCamelCase							,  _lowerCamelCase							,  unittest.TestCase					):
					lowerCAmelCase_							=	StableDiffusionInstructPixaPixPipeline
					lowerCAmelCase_							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					lowerCAmelCase_							=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowerCAmelCase_							=	IMAGE_TO_IMAGE_IMAGE_PARAMS
					lowerCAmelCase_							=	IMAGE_TO_IMAGE_IMAGE_PARAMS




					def 	lowerCAmelCase			(		self  )		->							Any:
										torch.manual_seed(0  )
										_snake_case									=				UNetaDConditionModel(
										    block_out_channels=(32, 64)				,						layers_per_block=2				,						sample_size=32				,						in_channels=8				,						out_channels=4				,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,						cross_attention_dim=32				,						)
										_snake_case									=				PNDMScheduler(skip_prk_steps=lowerCAmelCase_  )
										torch.manual_seed(0  )
										_snake_case									=				AutoencoderKL(
										    block_out_channels=[32, 64]				,						in_channels=3				,						out_channels=3				,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,						latent_channels=4				,						)
										torch.manual_seed(0  )
										_snake_case									=				CLIPTextConfig(
										    bos_token_id=0				,						eos_token_id=2				,						hidden_size=32				,						intermediate_size=37				,						layer_norm_eps=1E-05				,						num_attention_heads=4				,						num_hidden_layers=5				,						pad_token_id=1				,						vocab_size=1000				,						)
										_snake_case									=				CLIPTextModel(lowerCAmelCase_  )
										_snake_case									=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

										_snake_case									=				{
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=0  )		->							Tuple:
										_snake_case									=				floats_tensor((1, 3, 32, 32)				,						rng=random.Random(lowerCAmelCase_  )  ).to(lowerCAmelCase_  )
										_snake_case									=				image.cpu().permute(0				,						2				,						3				,						1  )[0]
										_snake_case									=				Image.fromarray(np.uinta(lowerCAmelCase_  )  ).convert('RGB'  )
										if str(lowerCAmelCase_  ).startswith('mps'  ):
															_snake_case									=				torch.manual_seed(lowerCAmelCase_  )
										else:
															_snake_case									=				torch.Generator(device=lowerCAmelCase_  ).manual_seed(lowerCAmelCase_  )
										_snake_case									=				{
										    'prompt': 'A painting of a squirrel eating a burger',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'image_guidance_scale': 1,
										    'output_type': 'numpy',
										}
										return inputs




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case									=				self.get_dummy_components()
										_snake_case									=				StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_  )
										_snake_case									=				sd_pipe.to(lowerCAmelCase_  )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

										_snake_case									=				self.get_dummy_inputs(lowerCAmelCase_  )
										_snake_case									=				sd_pipe(**lowerCAmelCase_  ).images
										_snake_case									=				image[0, -3:, -3:, -1]
										assert image.shape == (1, 32, 32, 3)
										_snake_case									=				np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]  )

										assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case									=				self.get_dummy_components()
										_snake_case									=				StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_  )
										_snake_case									=				sd_pipe.to(lowerCAmelCase_  )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

										_snake_case									=				self.get_dummy_inputs(lowerCAmelCase_  )
										_snake_case									=				'french fries'
										_snake_case									=				sd_pipe(**lowerCAmelCase_				,						negative_prompt=lowerCAmelCase_  )
										_snake_case									=				output.images
										_snake_case									=				image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										_snake_case									=				np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]  )

										assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case									=				self.get_dummy_components()
										_snake_case									=				StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_  )
										_snake_case									=				sd_pipe.to(lowerCAmelCase_  )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

										_snake_case									=				self.get_dummy_inputs(lowerCAmelCase_  )
										_snake_case									=				[inputs['prompt']] * 2

										_snake_case									=				np.array(inputs['image']  ).astype(np.floataa  ) / 255.0
										_snake_case									=				torch.from_numpy(lowerCAmelCase_  ).unsqueeze(0  ).to(lowerCAmelCase_  )
										_snake_case									=				image / 2 + 0.5
										_snake_case									=				image.permute(0				,						3				,						1				,						2  )
										_snake_case									=				image.repeat(2				,						1				,						1				,						1  )

										_snake_case									=				sd_pipe(**lowerCAmelCase_  ).images
										_snake_case									=				image[-1, -3:, -3:, -1]

										assert image.shape == (2, 32, 32, 3)
										_snake_case									=				np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]  )

										assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				'cpu'  # ensure determinism for the device-dependent torch.Generator
										_snake_case									=				self.get_dummy_components()
										_snake_case									=				EulerAncestralDiscreteScheduler(
										    beta_start=0.0_00_85				,						beta_end=0.0_12				,						beta_schedule='scaled_linear'  )
										_snake_case									=				StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_  )
										_snake_case									=				sd_pipe.to(lowerCAmelCase_  )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

										_snake_case									=				self.get_dummy_inputs(lowerCAmelCase_  )
										_snake_case									=				sd_pipe(**lowerCAmelCase_  ).images
										_snake_case									=				image[0, -3:, -3:, -1]

										_snake_case									=				[round(lowerCAmelCase_				,						4  ) for x in image_slice.flatten().tolist()]
										print(','.join([str(lowerCAmelCase_  ) for x in slice]  )  )

										assert image.shape == (1, 32, 32, 3)
										_snake_case									=				np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]  )

										assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							Any:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				self.get_dummy_components()
										_snake_case									=				StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_  )
										_snake_case									=				VaeImageProcessor(do_resize=lowerCAmelCase_				,						do_normalize=lowerCAmelCase_  )
										_snake_case									=				pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

										_snake_case									=				pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_				,						input_image_type='pt'  )  )[0]

										_snake_case									=				components['vae']
										_snake_case									=				self.get_dummy_inputs_by_type(lowerCAmelCase_				,						input_image_type='pt'  )

										for image_param in self.image_latents_params:
															if image_param in inputs.keys():
																				_snake_case									=				vae.encode(inputs[image_param]  ).latent_dist.mode()

										_snake_case									=				pipe(**lowerCAmelCase_  )[0]

										_snake_case									=				np.abs(out - out_latents_inputs  ).max()
										self.assertLess(lowerCAmelCase_				,						1E-4				,						'passing latents as image input generate different result from passing image'  )





@slow
@require_torch_gpu
class 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self  )		->							str:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=0  )		->							List[str]:
										_snake_case									=				torch.manual_seed(lowerCAmelCase_  )
										_snake_case									=				load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'  )
										_snake_case									=				{
										    'prompt': 'turn him into a cyborg',
										    'image': image,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'image_guidance_scale': 1.0,
										    'output_type': 'numpy',
										}
										return inputs




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'				,						safety_checker=lowerCAmelCase_  )
										pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
										pipe.enable_attention_slicing()

										_snake_case									=				self.get_inputs()
										_snake_case									=				pipe(**lowerCAmelCase_  ).images
										_snake_case									=				image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										_snake_case									=				np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]  )

										assert np.abs(expected_slice - image_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'				,						safety_checker=lowerCAmelCase_  )
										_snake_case									=				LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
										pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
										pipe.enable_attention_slicing()

										_snake_case									=				self.get_inputs()
										_snake_case									=				pipe(**lowerCAmelCase_  ).images
										_snake_case									=				image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										_snake_case									=				np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]  )

										assert np.abs(expected_slice - image_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'				,						safety_checker=lowerCAmelCase_  )
										_snake_case									=				DDIMScheduler.from_config(pipe.scheduler.config  )
										pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
										pipe.enable_attention_slicing()

										_snake_case									=				self.get_inputs()
										_snake_case									=				pipe(**lowerCAmelCase_  ).images
										_snake_case									=				image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										_snake_case									=				np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]  )

										assert np.abs(expected_slice - image_slice  ).max() < 1E-3




					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										_snake_case									=				0

										def callback_fn(lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  ) -> None:
															_snake_case									=				True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																				_snake_case									=				latents.detach().cpu().numpy()
																				assert latents.shape == (1, 4, 64, 64)
																				_snake_case									=				latents[0, -3:, -3:, -1]
																				_snake_case									=				np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]  )

																				assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2
															elif step == 2:
																				_snake_case									=				latents.detach().cpu().numpy()
																				assert latents.shape == (1, 4, 64, 64)
																				_snake_case									=				latents[0, -3:, -3:, -1]
																				_snake_case									=				np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]  )

																				assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2

										_snake_case									=				False

										_snake_case									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'				,						safety_checker=lowerCAmelCase_				,						torch_dtype=torch.floataa  )
										_snake_case									=				pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
										pipe.enable_attention_slicing()

										_snake_case									=				self.get_inputs()
										pipe(**lowerCAmelCase_				,						callback=lowerCAmelCase_				,						callback_steps=1  )
										assert callback_fn.has_been_called
										assert number_of_steps == 3




					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										_snake_case									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    'timbrooks/instruct-pix2pix'				,						safety_checker=lowerCAmelCase_				,						torch_dtype=torch.floataa  )
										_snake_case									=				pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
										pipe.enable_attention_slicing(1  )
										pipe.enable_sequential_cpu_offload()

										_snake_case									=				self.get_inputs()
										_snake_case									=				pipe(**lowerCAmelCase_  )

										_snake_case									=				torch.cuda.max_memory_allocated()
										# make sure that less than 2.2 GB is allocated
										assert mem_bytes < 2.2 * 10**9






					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				self.get_inputs()
										# resize to resolution that is divisible by 8 but not 16 or 32
										_snake_case									=				inputs['image'].resize((504, 504)  )

										_snake_case									=				'timbrooks/instruct-pix2pix'
										_snake_case									=				StableDiffusionInstructPixaPixPipeline.from_pretrained(
										    lowerCAmelCase_				,						safety_checker=lowerCAmelCase_				,						)
										pipe.to(lowerCAmelCase_  )
										pipe.set_progress_bar_config(disable=lowerCAmelCase_  )
										pipe.enable_attention_slicing()

										_snake_case									=				pipe(**lowerCAmelCase_  )
										_snake_case									=				output.images[0]

										_snake_case									=				image[255:258, 383:386, -1]

										assert image.shape == (504, 504, 3)
										_snake_case									=				np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]  )

										assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3










import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class 			UpperCamelCase_		(  unittest.TestCase					):




					@parameterized.expand([(None,), ('foo.json',)]  )
					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										_snake_case									=				GenerationConfig(
										    do_sample=lowerCAmelCase_				,						temperature=0.7				,						length_penalty=1.0				,						bad_words_ids=[[1, 2, 3], [4, 5]]				,						)
										with tempfile.TemporaryDirectory() as tmp_dir:
															config.save_pretrained(lowerCAmelCase_				,						config_name=lowerCAmelCase_  )
															_snake_case									=				GenerationConfig.from_pretrained(lowerCAmelCase_				,						config_name=lowerCAmelCase_  )

										# Checks parameters that were specified
										self.assertEqual(loaded_config.do_sample				,						lowerCAmelCase_  )
										self.assertEqual(loaded_config.temperature				,						0.7  )
										self.assertEqual(loaded_config.length_penalty				,						1.0  )
										self.assertEqual(loaded_config.bad_words_ids				,						[[1, 2, 3], [4, 5]]  )

										# Checks parameters that were not specified (defaults)
										self.assertEqual(loaded_config.top_k				,						50  )
										self.assertEqual(loaded_config.max_length				,						20  )
										self.assertEqual(loaded_config.max_time				,						lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				AutoConfig.from_pretrained('gpt2'  )
										_snake_case									=				GenerationConfig.from_model_config(lowerCAmelCase_  )
										_snake_case									=				GenerationConfig()

										# The generation config has loaded a few non-default parameters from the model config
										self.assertNotEqual(lowerCAmelCase_				,						lowerCAmelCase_  )

										# One of those parameters is eos_token_id -- check if it matches
										self.assertNotEqual(generation_config_from_model.eos_token_id				,						default_generation_config.eos_token_id  )
										self.assertEqual(generation_config_from_model.eos_token_id				,						model_config.eos_token_id  )




					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				GenerationConfig()
										_snake_case									=				{
										    'max_new_tokens': 1024,
										    'foo': 'bar',
										}
										_snake_case									=				copy.deepcopy(lowerCAmelCase_  )
										_snake_case									=				generation_config.update(**lowerCAmelCase_  )

										# update_kwargs was not modified (no side effects)
										self.assertEqual(lowerCAmelCase_				,						lowerCAmelCase_  )

										# update_kwargs was used to update the config on valid attributes
										self.assertEqual(generation_config.max_new_tokens				,						1024  )

										# `.update()` returns a dictionary of unused kwargs
										self.assertEqual(lowerCAmelCase_				,						{'foo': 'bar'}  )




					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				GenerationConfig()
										_snake_case									=				'bar'

										with tempfile.TemporaryDirectory('test-generation-config'  ) as tmp_dir:
															generation_config.save_pretrained(lowerCAmelCase_  )

															_snake_case									=				GenerationConfig.from_pretrained(lowerCAmelCase_  )
										# update_kwargs was used to update the config on valid attributes
										self.assertEqual(new_config.foo				,						'bar'  )

										_snake_case									=				GenerationConfig.from_model_config(lowerCAmelCase_  )
										assert not hasattr(lowerCAmelCase_				,						'foo'  )  # no new kwargs should be initialized if from config






					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				GenerationConfig()
										self.assertEqual(default_config.temperature				,						1.0  )
										self.assertEqual(default_config.do_sample				,						lowerCAmelCase_  )
										self.assertEqual(default_config.num_beams				,						1  )

										_snake_case									=				GenerationConfig(
										    do_sample=lowerCAmelCase_				,						temperature=0.7				,						length_penalty=1.0				,						bad_words_ids=[[1, 2, 3], [4, 5]]				,						)
										self.assertEqual(config.temperature				,						0.7  )
										self.assertEqual(config.do_sample				,						lowerCAmelCase_  )
										self.assertEqual(config.num_beams				,						1  )

										with tempfile.TemporaryDirectory() as tmp_dir:
															config.save_pretrained(lowerCAmelCase_  )
															_snake_case									=				GenerationConfig.from_pretrained(lowerCAmelCase_				,						temperature=1.0  )

										self.assertEqual(loaded_config.temperature				,						1.0  )
										self.assertEqual(loaded_config.do_sample				,						lowerCAmelCase_  )
										self.assertEqual(loaded_config.num_beams				,						1  )  # default value





@is_staging_test
class 			UpperCamelCase_		(  unittest.TestCase					):




					@classmethod
					def 	lowerCAmelCase			(		cls  )		->							List[str]:
										_snake_case									=				TOKEN
										HfFolder.save_token(lowerCAmelCase_  )




					@classmethod
					def 	lowerCAmelCase			(		cls  )		->							Union[str, Any]:
										try:
															delete_repo(token=cls._token				,						repo_id='test-generation-config'  )
										except HTTPError:
															pass

										try:
															delete_repo(token=cls._token				,						repo_id='valid_org/test-generation-config-org'  )
										except HTTPError:
															pass




					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				GenerationConfig(
										    do_sample=lowerCAmelCase_				,						temperature=0.7				,						length_penalty=1.0				,						)
										config.push_to_hub('test-generation-config'				,						use_auth_token=self._token  )

										_snake_case									=				GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''  )
										for k, v in config.to_dict().items():
															if k != "transformers_version":
																				self.assertEqual(lowerCAmelCase_				,						getattr(lowerCAmelCase_				,						lowerCAmelCase_  )  )

        # Reset repo
										delete_repo(token=self._token				,						repo_id='test-generation-config'  )

										# Push to hub via save_pretrained
										with tempfile.TemporaryDirectory() as tmp_dir:
															config.save_pretrained(
															    lowerCAmelCase_				,						repo_id='test-generation-config'				,						push_to_hub=lowerCAmelCase_				,						use_auth_token=self._token  )

										_snake_case									=				GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''  )
										for k, v in config.to_dict().items():
															if k != "transformers_version":
																				self.assertEqual(lowerCAmelCase_				,						getattr(lowerCAmelCase_				,						lowerCAmelCase_  )  )






					def 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				GenerationConfig(
										    do_sample=lowerCAmelCase_				,						temperature=0.7				,						length_penalty=1.0				,						)
										config.push_to_hub('valid_org/test-generation-config-org'				,						use_auth_token=self._token  )

										_snake_case									=				GenerationConfig.from_pretrained('valid_org/test-generation-config-org'  )
										for k, v in config.to_dict().items():
															if k != "transformers_version":
																				self.assertEqual(lowerCAmelCase_				,						getattr(lowerCAmelCase_				,						lowerCAmelCase_  )  )

        # Reset repo
										delete_repo(token=self._token				,						repo_id='valid_org/test-generation-config-org'  )

										# Push to hub via save_pretrained
										with tempfile.TemporaryDirectory() as tmp_dir:
															config.save_pretrained(
															    lowerCAmelCase_				,						repo_id='valid_org/test-generation-config-org'				,						push_to_hub=lowerCAmelCase_				,						use_auth_token=self._token  )

										_snake_case									=				GenerationConfig.from_pretrained('valid_org/test-generation-config-org'  )
										for k, v in config.to_dict().items():
															if k != "transformers_version":
																				self.assertEqual(lowerCAmelCase_				,						getattr(lowerCAmelCase_				,						lowerCAmelCase_  )  )



