



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@property
	def      A	(						self			:     Dict		)			->		List[Any]:
								torch.manual_seed(0		)
								UpperCAmelCase :       Optional[Any] 							=		UNetaDModel(
								    block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
								return model







	def      A	(						self			:     Optional[int]		)			->		Optional[int]:
								UpperCAmelCase :       Any 							=		self.dummy_uncond_unet
								UpperCAmelCase :       Optional[Any] 							=		ScoreSdeVeScheduler()

								UpperCAmelCase :       Tuple 							=		ScoreSdeVePipeline(unet=__snake_case		,			scheduler=__snake_case		)
								sde_ve.to(__snake_case		)
								sde_ve.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		torch.manual_seed(0		)
								UpperCAmelCase :       str 							=		sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=__snake_case		).images

								UpperCAmelCase :       Tuple 							=		torch.manual_seed(0		)
								UpperCAmelCase :       Dict 							=		sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=__snake_case		,			return_dict=__snake_case		)[
								    0
								]

								UpperCAmelCase :       List[str] 							=		image[0, -3:, -3:, -1]
								UpperCAmelCase :       str 							=		image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase :       Union[str, Any] 							=		np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2

@slow
@require_torch
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       List[Any] 							=		'''google/ncsnpp-church-256'''
								UpperCAmelCase :       Union[str, Any] 							=		UNetaDModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       List[str] 							=		ScoreSdeVeScheduler.from_pretrained(__snake_case		)

								UpperCAmelCase :       Tuple 							=		ScoreSdeVePipeline(unet=__snake_case		,			scheduler=__snake_case		)
								sde_ve.to(__snake_case		)
								sde_ve.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       Tuple 							=		torch.manual_seed(0		)
								UpperCAmelCase :       str 							=		sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=__snake_case		).images

								UpperCAmelCase :       Optional[Any] 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)

								UpperCAmelCase :       Optional[int] 							=		np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]		)

								assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2










'''simple docstring'''






import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
						import os

						from flax.core.frozen_dict import unfreeze
						from flax.traverse_util import flatten_dict

						from transformers import FlaxBertModel

						UpperCamelCase__:   Tuple								=			"0.12"  # assumed parallelism: 8

@require_flax
@is_staging_test
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@classmethod
	def      A	(						cls			:     Union[str, Any]		)			->		int:
								UpperCAmelCase :       Optional[Any] 							=		TOKEN
								HfFolder.save_token(__snake_case		)







	@classmethod
	def      A	(						cls			:     List[str]		)			->		Tuple:
								try:
															delete_repo(token=cls._token		,			repo_id='''test-model-flax'''		)
								except HTTPError:
															pass

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







	def      A	(						self			:     int		)			->		Tuple:
								UpperCAmelCase :       List[Any] 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)
								model.push_to_hub('''test-model-flax'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       List[Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Tuple 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Union[str, Any] 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)

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

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

								UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""		)

								UpperCAmelCase :       Any 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       str 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Optional[Any] 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)







	def      A	(						self			:     Optional[Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Dict 							=		BertConfig(
								    vocab_size=99		,			hidden_size=32		,			num_hidden_layers=5		,			num_attention_heads=4		,			intermediate_size=37		)
								UpperCAmelCase :       Optional[Any] 							=		FlaxBertModel(__snake_case		)
								model.push_to_hub('''valid_org/test-model-flax-org'''		,			use_auth_token=self._token		)

								UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''		)

								UpperCAmelCase :       List[Any] 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       int 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       Any 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)

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

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

								UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org'''		)

								UpperCAmelCase :       Any 							=		flatten_dict(unfreeze(model.params		)		)
								UpperCAmelCase :       Optional[Any] 							=		flatten_dict(unfreeze(new_model.params		)		)

								for key in base_params.keys():
															UpperCAmelCase :       int 							=		(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(__snake_case		,			1E-3		,			msg=F"""{key} not identical"""		)


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Any  )      ->   Union[str, Any]:
							UpperCAmelCase :       str 							=		True
							UpperCAmelCase :       int 							=		flatten_dict(modela.params  )
							UpperCAmelCase :       Dict 							=		flatten_dict(modela.params  )
							for key in flat_params_a.keys():
														if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]  )  ) > 1e-4:
																					UpperCAmelCase :       Dict 							=		False

							return models_are_equal




@require_flax
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)

								UpperCAmelCase :       int 							=		'''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case		,			__snake_case		)		)

															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(__snake_case		)

															UpperCAmelCase :       str 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertTrue(check_models_equal(__snake_case		,			__snake_case		)		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Dict 							=		BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''		)
								UpperCAmelCase :       Dict 							=		FlaxBertModel(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		'''bert'''
								with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(__snake_case		,			__snake_case		)		,			max_shard_size='''10KB'''		)

															with self.assertRaises(__snake_case		):
																						UpperCAmelCase :       Any 							=		FlaxBertModel.from_pretrained(__snake_case		)

															UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertTrue(check_models_equal(__snake_case		,			__snake_case		)		)







	def      A	(						self			:     Optional[int]		)			->		str:
								UpperCAmelCase :       Dict 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-subfolder'''

								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Optional[Any] 							=		FlaxBertModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       Tuple 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertIsNotNone(__snake_case		)







	def      A	(						self			:     Dict		)			->		List[Any]:
								UpperCAmelCase :       Optional[int] 							=		'''bert'''
								UpperCAmelCase :       int 							=		'''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
								with self.assertRaises(__snake_case		):
															UpperCAmelCase :       Dict 							=		FlaxBertModel.from_pretrained(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		FlaxBertModel.from_pretrained(__snake_case		,			subfolder=__snake_case		)

								self.assertIsNotNone(__snake_case		)





