






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


UpperCamelCase			    =     8

def 	__lowerCamelCase      (		snake_case__	,snake_case__=BITS		)							->  Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  x.device

						_SCREAMING_SNAKE_CASE     				=  (x * 2_55).int().clamp(0	,2_55		)

						_SCREAMING_SNAKE_CASE     				=  2 ** torch.arange(bits - 1	,-1	,-1	,device=UpperCAmelCase_		)
						_SCREAMING_SNAKE_CASE     				=  rearrange(UpperCAmelCase_	,"""d -> d 1 1"""		)
						_SCREAMING_SNAKE_CASE     				=  rearrange(UpperCAmelCase_	,"""b c h w -> b c 1 h w"""		)

						_SCREAMING_SNAKE_CASE     				=  ((x & mask) != 0).float()
						_SCREAMING_SNAKE_CASE     				=  rearrange(UpperCAmelCase_	,"""b c d h w -> b (c d) h w"""		)
						_SCREAMING_SNAKE_CASE     				=  bits * 2 - 1
						return bits

def 	__lowerCamelCase      (		snake_case__	,snake_case__=BITS		)							->  Optional[int]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  x.device

						_SCREAMING_SNAKE_CASE     				=  (x > 0).int()
						_SCREAMING_SNAKE_CASE     				=  2 ** torch.arange(bits - 1	,-1	,-1	,device=UpperCAmelCase_	,dtype=torch.intaa		)

						_SCREAMING_SNAKE_CASE     				=  rearrange(UpperCAmelCase_	,"""d -> d 1 1"""		)
						_SCREAMING_SNAKE_CASE     				=  rearrange(UpperCAmelCase_	,"""b (c d) h w -> b c d h w"""	,d=8		)
						_SCREAMING_SNAKE_CASE     				=  reduce(x * mask	,"""b c d h w -> b c h w"""	,"""sum"""		)
						return (dec / 2_55).clamp(0.0	,1.0		)

def 	__lowerCamelCase      (		self	,snake_case__	,snake_case__	,snake_case__	,snake_case__ = 0.0	,snake_case__ = True	,snake_case__=None	,snake_case__ = True	,)							->  int:






						"""simple docstring"""





						if self.num_inference_steps is None:
												raise ValueError(
												    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""		)

						# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
						# Ideally, read DDIM paper in-detail understanding

						# Notation (<variable name> -> <name in paper>
						# - pred_noise_t -> e_theta(x_t, t)
						# - pred_original_sample -> f_theta(x_t, t) or x_0
						# - std_dev_t -> sigma_t
						# - eta -> η
						# - pred_sample_direction -> "direction pointing to x_t"
						# - pred_prev_sample -> "x_t-1"

						# 1. get previous step value (=t-1)
						_SCREAMING_SNAKE_CASE     				=  timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						_SCREAMING_SNAKE_CASE     				=  self.alphas_cumprod[timestep]
						_SCREAMING_SNAKE_CASE     				=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						_SCREAMING_SNAKE_CASE     				=  1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_SCREAMING_SNAKE_CASE     				=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						_SCREAMING_SNAKE_CASE     				=  self.bit_scale
						if self.config.clip_sample:
												_SCREAMING_SNAKE_CASE     				=  torch.clamp(UpperCAmelCase_	,-scale	,UpperCAmelCase_		)

						# 5. compute variance: "sigma_t(η)" -> see formula (16)
						# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
						_SCREAMING_SNAKE_CASE     				=  self._get_variance(UpperCAmelCase_	,UpperCAmelCase_		)
						_SCREAMING_SNAKE_CASE     				=  eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												_SCREAMING_SNAKE_CASE     				=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

						# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_SCREAMING_SNAKE_CASE     				=  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

						# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_SCREAMING_SNAKE_CASE     				=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if eta > 0:
												# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
												_SCREAMING_SNAKE_CASE     				=  model_output.device if torch.is_tensor(UpperCAmelCase_		) else """cpu"""
												_SCREAMING_SNAKE_CASE     				=  torch.randn(model_output.shape	,dtype=model_output.dtype	,generator=UpperCAmelCase_		).to(UpperCAmelCase_		)
												_SCREAMING_SNAKE_CASE     				=  self._get_variance(UpperCAmelCase_	,UpperCAmelCase_		) ** 0.5 * eta * noise

												_SCREAMING_SNAKE_CASE     				=  prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=UpperCAmelCase_	,pred_original_sample=UpperCAmelCase_		)

def 	__lowerCamelCase      (		self	,snake_case__	,snake_case__	,snake_case__	,snake_case__="epsilon"	,snake_case__=None	,snake_case__ = True	,)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  torch.split(UpperCAmelCase_	,sample.shape[1]	,dim=1		)
						else:
												_SCREAMING_SNAKE_CASE     				=  None

						# 1. compute alphas, betas
						_SCREAMING_SNAKE_CASE     				=  self.alphas_cumprod[t]
						_SCREAMING_SNAKE_CASE     				=  self.alphas_cumprod[t - 1] if t > 0 else self.one
						_SCREAMING_SNAKE_CASE     				=  1 - alpha_prod_t
						_SCREAMING_SNAKE_CASE     				=  1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if prediction_type == "epsilon":
												_SCREAMING_SNAKE_CASE     				=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												_SCREAMING_SNAKE_CASE     				=  model_output
						else:
												raise ValueError(F'Unsupported prediction_type {prediction_type}.'		)

						# 3. Clip "predicted x_0"
						_SCREAMING_SNAKE_CASE     				=  self.bit_scale
						if self.config.clip_sample:
												_SCREAMING_SNAKE_CASE     				=  torch.clamp(UpperCAmelCase_	,-scale	,UpperCAmelCase_		)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						_SCREAMING_SNAKE_CASE     				=  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						_SCREAMING_SNAKE_CASE     				=  self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						_SCREAMING_SNAKE_CASE     				=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						_SCREAMING_SNAKE_CASE     				=  0
						if t > 0:
												_SCREAMING_SNAKE_CASE     				=  torch.randn(
												    model_output.size()	,dtype=model_output.dtype	,layout=model_output.layout	,generator=UpperCAmelCase_		).to(model_output.device		)
												_SCREAMING_SNAKE_CASE     				=  (self._get_variance(UpperCAmelCase_	,predicted_variance=UpperCAmelCase_		) ** 0.5) * noise

						_SCREAMING_SNAKE_CASE     				=  pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=UpperCAmelCase_	,pred_original_sample=UpperCAmelCase_		)




class 				UpperCamelCase__		(_lowerCamelCase   ):







			def __init__(     self:  Dict				,     UpperCAmelCase_:  UNetaDConditionModel				,     UpperCAmelCase_:  Union[DDIMScheduler, DDPMScheduler]				,     UpperCAmelCase_:  Optional[float] = 1.0				,     ):







									'''simple docstring'''




									super().__init__()
									_SCREAMING_SNAKE_CASE     				=  bit_scale
									_SCREAMING_SNAKE_CASE     				=  (
									    ddim_bit_scheduler_step if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) else ddpm_bit_scheduler_step
									)

									self.register_modules(unet=UpperCAmelCase_				,     scheduler=UpperCAmelCase_					)







			@torch.no_grad()
			def __call__(     self:  Union[str, Any]				,     UpperCAmelCase_:  Optional[int] = 256				,     UpperCAmelCase_:  Optional[int] = 256				,     UpperCAmelCase_:  Optional[int] = 50				,     UpperCAmelCase_:  Optional[torch.Generator] = None				,     UpperCAmelCase_:  Optional[int] = 1				,     UpperCAmelCase_:  Optional[str] = "pil"				,     UpperCAmelCase_:  bool = True				,     **UpperCAmelCase_:  Optional[Any]				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)				,     generator=UpperCAmelCase_				,     )
									_SCREAMING_SNAKE_CASE     				=  decimal_to_bits(UpperCAmelCase_					) * self.bit_scale
									_SCREAMING_SNAKE_CASE     				=  latents.to(self.device					)

									self.scheduler.set_timesteps(UpperCAmelCase_					)

									for t in self.progress_bar(self.scheduler.timesteps					):
															# predict the noise residual
															_SCREAMING_SNAKE_CASE     				=  self.unet(UpperCAmelCase_				,     UpperCAmelCase_					).sample

															# compute the previous noisy sample x_t -> x_t-1
															_SCREAMING_SNAKE_CASE     				=  self.scheduler.step(UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_					).prev_sample

									_SCREAMING_SNAKE_CASE     				=  bits_to_decimal(UpperCAmelCase_					)

									if output_type == "pil":
															_SCREAMING_SNAKE_CASE     				=  self.numpy_to_pil(UpperCAmelCase_					)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=UpperCAmelCase_					)









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_					)

