






import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class 				UpperCamelCase__		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			@property
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  1
									_SCREAMING_SNAKE_CASE     				=  3
									_SCREAMING_SNAKE_CASE     				=  (32, 32)

									_SCREAMING_SNAKE_CASE     				=  floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0					)					).to(UpperCAmelCase_					)
									return image







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  UNetaDConditionModel(
									    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,     cross_attention_dim=32				,     )
									return model







			@property
			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_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				,     )
									return model







			@property
			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_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=1_000				,     )
									return CLIPTextModel(UpperCAmelCase_					)







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									def extract(*UpperCAmelCase_:  Optional[Any]				,     **UpperCAmelCase_:  Optional[Any]					):



															class 				UpperCamelCase__		:







																		def __init__(     self:  str					):







																								'''simple docstring'''




																								_SCREAMING_SNAKE_CASE     				=  torch.ones([0]					)







																		def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  int					):







																								'''simple docstring'''




																								self.pixel_values.to(UpperCAmelCase_					)
																								return self
															return Out()

									return extract







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.dummy_cond_unet
									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler(
									    beta_start=0.0_00_85				,     beta_end=0.0_12				,     beta_schedule="""scaled_linear"""				,     clip_sample=UpperCAmelCase_				,     set_alpha_to_one=UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  self.dummy_vae
									_SCREAMING_SNAKE_CASE     				=  self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									# make sure here that pndm scheduler skips prk
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline(
									    unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     safety_checker=UpperCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """A painting of a squirrel eating a burger"""

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe([prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""					)
									_SCREAMING_SNAKE_CASE     				=  output.images

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=UpperCAmelCase_				,     )[0]

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]					)

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







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.dummy_cond_unet
									_SCREAMING_SNAKE_CASE     				=  PNDMScheduler(skip_prk_steps=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.dummy_vae
									_SCREAMING_SNAKE_CASE     				=  self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									# make sure here that pndm scheduler skips prk
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline(
									    unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     safety_checker=UpperCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """A painting of a squirrel eating a burger"""
									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe([prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""					)

									_SCREAMING_SNAKE_CASE     				=  output.images

									_SCREAMING_SNAKE_CASE     				=  torch.Generator(device=UpperCAmelCase_					).manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type="""np"""				,     return_dict=UpperCAmelCase_				,     )[0]

									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_SCREAMING_SNAKE_CASE     				=  np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]					)

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







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,     safety_checker=UpperCAmelCase_					)
									assert isinstance(UpperCAmelCase_				,     UpperCAmelCase_					)
									assert isinstance(pipe.scheduler				,     UpperCAmelCase_					)
									assert pipe.safety_checker is None

									_SCREAMING_SNAKE_CASE     				=  pipe("""example prompt"""				,     num_inference_steps=2					).images[0]
									assert image is not None

									# check that there's no error when saving a pipeline with one of the models being None
									with tempfile.TemporaryDirectory() as tmpdirname:
															pipe.save_pretrained(UpperCAmelCase_					)
															_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained(UpperCAmelCase_					)

									# sanity check that the pipeline still works
									assert pipe.safety_checker is None
									_SCREAMING_SNAKE_CASE     				=  pipe("""example prompt"""				,     num_inference_steps=2					).images[0]
									assert image is not None







			@unittest.skipIf(torch_device != """cuda"""				,     """This test requires a GPU"""					)
			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  self.dummy_cond_unet
									_SCREAMING_SNAKE_CASE     				=  PNDMScheduler(skip_prk_steps=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  self.dummy_vae
									_SCREAMING_SNAKE_CASE     				=  self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									# put models in fp16
									_SCREAMING_SNAKE_CASE     				=  unet.half()
									_SCREAMING_SNAKE_CASE     				=  vae.half()
									_SCREAMING_SNAKE_CASE     				=  bert.half()

									# make sure here that pndm scheduler skips prk
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline(
									    unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_				,     vae=UpperCAmelCase_				,     text_encoder=UpperCAmelCase_				,     tokenizer=UpperCAmelCase_				,     safety_checker=UpperCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """A painting of a squirrel eating a burger"""
									_SCREAMING_SNAKE_CASE     				=  sd_pipe([prompt]				,     num_inference_steps=2				,     output_type="""np"""					).images

									assert image.shape == (1, 64, 64, 3)



@nightly
@require_torch_gpu
class 				UpperCamelCase__		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  (
									    """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
									    """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
									    """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
									    """ children from bahnhof zoo, detailed """
									)
									_SCREAMING_SNAKE_CASE     				=  4_003_660_346
									_SCREAMING_SNAKE_CASE     				=  7

									# without safety guidance (sld_guidance_scale = 0)
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

									assert image.shape == (1, 512, 512, 3)

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

									# without safety guidance (strong configuration)
									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2_000				,     sld_warmup_steps=7				,     sld_threshold=0.0_25				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

									assert image.shape == (1, 512, 512, 3)

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







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,     safety_checker=UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """padme amidala taking a bath artwork, safe for work, no nudity"""
									_SCREAMING_SNAKE_CASE     				=  2_734_971_755
									_SCREAMING_SNAKE_CASE     				=  7

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

									assert image.shape == (1, 512, 512, 3)

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

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2_000				,     sld_warmup_steps=7				,     sld_threshold=0.0_25				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

									assert image.shape == (1, 512, 512, 3)

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







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(UpperCAmelCase_					)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  (
									    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
									    """ leyendecker"""
									)
									_SCREAMING_SNAKE_CASE     				=  1_044_355_234
									_SCREAMING_SNAKE_CASE     				=  12

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=0				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]					)

									assert image.shape == (1, 512, 512, 3)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-7

									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(UpperCAmelCase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(
									    [prompt]				,     generator=UpperCAmelCase_				,     guidance_scale=UpperCAmelCase_				,     num_inference_steps=50				,     output_type="""np"""				,     width=512				,     height=512				,     sld_guidance_scale=2_000				,     sld_warmup_steps=7				,     sld_threshold=0.0_25				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									_SCREAMING_SNAKE_CASE     				=  np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]					)
									assert image.shape == (1, 512, 512, 3)

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









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			    =     '''0.12'''  # assumed parallelism: 8



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







			@classmethod
			def 							UpperCamelCase				(     cls:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  TOKEN
									HfFolder.save_token(UpperCAmelCase_					)







			@classmethod
			def 							UpperCamelCase				(     cls:  Union[str, Any]					):







									'''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 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    vocab_size=99				,     hidden_size=32				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=37					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)
									model.push_to_hub("""test-model-flax"""				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

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

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(F'{USER}/test-model-flax'					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

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







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig(
									    vocab_size=99				,     hidden_size=32				,     num_hidden_layers=5				,     num_attention_heads=4				,     intermediate_size=37					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)
									model.push_to_hub("""valid_org/test-model-flax-org"""				,     use_auth_token=self._token					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

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

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""					)

									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(model.params					)					)
									_SCREAMING_SNAKE_CASE     				=  flatten_dict(unfreeze(new_model.params					)					)

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  True
						_SCREAMING_SNAKE_CASE     				=  flatten_dict(modela.params		)
						_SCREAMING_SNAKE_CASE     				=  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:
																		_SCREAMING_SNAKE_CASE     				=  False

						return models_are_equal




@require_flax
class 				__UpperCAmelCase		(unittest.TestCase   ):







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)					)

															with self.assertRaises(UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertTrue(check_models_equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""					)
									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  """bert"""
									with tempfile.TemporaryDirectory() as tmp_dir:
															model.save_pretrained(os.path.join(UpperCAmelCase_				,     UpperCAmelCase_					)				,     max_shard_size="""10KB"""					)

															with self.assertRaises(UpperCAmelCase_					):
																					_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertTrue(check_models_equal(UpperCAmelCase_				,     UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """bert"""
									_SCREAMING_SNAKE_CASE     				=  """hf-internal-testing/tiny-random-bert-subfolder"""

									with self.assertRaises(UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertIsNotNone(UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """bert"""
									_SCREAMING_SNAKE_CASE     				=  """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
									with self.assertRaises(UpperCAmelCase_					):
															_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  FlaxBertModel.from_pretrained(UpperCAmelCase_				,     subfolder=UpperCAmelCase_					)

									self.assertIsNotNone(UpperCAmelCase_					)

