

'''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

		__snake_case					:      List[Any] 		=     "0.12"  # assumed parallelism: 8



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



			'''simple docstring'''






			@classmethod
			def lowercase__		( cls			:		List[str]					)     ->						Dict:





									'''simple docstring'''
									A__	:  Optional[int]           =TOKEN
									HfFolder.save_token(a_					)


			@classmethod
			def lowercase__		( cls			:		Optional[int]					)     ->						Tuple:





									'''simple docstring'''
									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 lowercase__		( self			:		str					)     ->						List[str]:





									'''simple docstring'''
									A__	:  Optional[Any]           =BertConfig(
									    vocab_size=99						,     hidden_size=32						,     num_hidden_layers=5						,     num_attention_heads=4						,     intermediate_size=37					)
									A__	:  Any           =FlaxBertModel(a_					)
									model.push_to_hub("""test-model-flax"""						,     use_auth_token=self._token					)

									A__	:  List[Any]           =FlaxBertModel.from_pretrained(f"{USER}/test-model-flax"					)

									A__	:  Dict           =flatten_dict(unfreeze(model.params					)					)
									A__	:  Dict           =flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															A__	:  Dict           =(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(a_						,     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(a_						,     repo_id="""test-model-flax"""						,     push_to_hub=a_						,     use_auth_token=self._token					)

									A__	:  int           =FlaxBertModel.from_pretrained(f"{USER}/test-model-flax"					)

									A__	:  Union[str, Any]           =flatten_dict(unfreeze(model.params					)					)
									A__	:  Any           =flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															A__	:  Union[str, Any]           =(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(a_						,     1e-3						,     msg=f"{key} not identical"					)



			def lowercase__		( self			:		Tuple					)     ->						Tuple:





									'''simple docstring'''
									A__	:  Union[str, Any]           =BertConfig(
									    vocab_size=99						,     hidden_size=32						,     num_hidden_layers=5						,     num_attention_heads=4						,     intermediate_size=37					)
									A__	:  Any           =FlaxBertModel(a_					)
									model.push_to_hub("""valid_org/test-model-flax-org"""						,     use_auth_token=self._token					)

									A__	:  str           =FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									A__	:  int           =flatten_dict(unfreeze(model.params					)					)
									A__	:  List[str]           =flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															A__	:  List[str]           =(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(a_						,     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(
															    a_						,     repo_id="""valid_org/test-model-flax-org"""						,     push_to_hub=a_						,     use_auth_token=self._token					)

									A__	:  List[str]           =FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									A__	:  Tuple           =flatten_dict(unfreeze(model.params					)					)
									A__	:  int           =flatten_dict(unfreeze(new_model.params					)					)

									for key in base_params.keys():
															A__	:  Union[str, Any]           =(base_params[key] - new_params[key]).sum().item()
															self.assertLessEqual(a_						,     1e-3						,     msg=f"{key} not identical"					)


def 		__lowerCamelCase			(  __snake_case	:						List[Any],						__snake_case	:						Any			)			->				Union[str, Any]:






						"""simple docstring"""





						A__	:  Dict           =True
						A__	:  Union[str, Any]           =flatten_dict(modela.params			)
						A__	:  str           =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:
																		A__	:  Dict           =False

						return models_are_equal



@require_flax
class lowerCamelCase					(    unittest.TestCase		):



			'''simple docstring'''






			def lowercase__		( self			:		Optional[Any]					)     ->						Dict:





									'''simple docstring'''
									A__	:  Optional[int]           =BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									A__	:  str           =FlaxBertModel(a_					)

									A__	:  Optional[Any]           ="""bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(a_						,     a_					)					)

															with self.assertRaises(a_					):
																					A__	:  Tuple           =FlaxBertModel.from_pretrained(a_					)

															A__	:  Any           =FlaxBertModel.from_pretrained(a_						,     subfolder=a_					)

									self.assertTrue(check_models_equal(a_						,     a_					)					)


			def lowercase__		( self			:		List[Any]					)     ->						Tuple:





									'''simple docstring'''
									A__	:  Optional[Any]           =BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									A__	:  List[Any]           =FlaxBertModel(a_					)

									A__	:  List[Any]           ="""bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(a_						,     a_					)						,     max_shard_size="""10KB"""					)

															with self.assertRaises(a_					):
																					A__	:  str           =FlaxBertModel.from_pretrained(a_					)

															A__	:  str           =FlaxBertModel.from_pretrained(a_						,     subfolder=a_					)

									self.assertTrue(check_models_equal(a_						,     a_					)					)


			def lowercase__		( self			:		int					)     ->						int:





									'''simple docstring'''
									A__	:  Optional[int]           ="""bert"""
									A__	:  Tuple           ="""hf-internal-testing/tiny-random-bert-subfolder"""

									with self.assertRaises(a_					):
															A__	:  Optional[Any]           =FlaxBertModel.from_pretrained(a_					)

									A__	:  Optional[int]           =FlaxBertModel.from_pretrained(a_						,     subfolder=a_					)

									self.assertIsNotNone(a_					)



			def lowercase__		( self			:		List[Any]					)     ->						Union[str, Any]:





									'''simple docstring'''
									A__	:  Optional[Any]           ="""bert"""
									A__	:  Any           ="""hf-internal-testing/tiny-random-bert-sharded-subfolder"""
									with self.assertRaises(a_					):
															A__	:  str           =FlaxBertModel.from_pretrained(a_					)

									A__	:  List[str]           =FlaxBertModel.from_pretrained(a_						,     subfolder=a_					)

									self.assertIsNotNone(a_					)






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase	:   Any    =   False


class 	__snake_case   ( unittest.TestCase ):
		pass



@nightly
@require_torch_gpu
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a_						)
										lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained(a_    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  generator.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  'cyberpunk 2077'
						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt=a_    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'    ,).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]						)

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

						lowerCAmelCase__		  =  'A painting of a squirrel eating a burger '
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.text_to_image(
						    prompt=a_    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'						).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]						)

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

						lowerCAmelCase__		  =  pipe.image_variation(a_    ,generator=a_    ,output_type='numpy'						).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]						)

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