



"""simple docstring"""







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


lowerCAmelCase_   						=   8



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=BITS	)							->      Optional[Any]:
		lowercase__   :    List[Any]				=			x.device

		lowercase__   :    List[Any]				=			(x * 2_55).int().clamp(0						,     2_55	)

		lowercase__   :    List[Any]				=			2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=__lowerCamelCase	)
		lowercase__   :    Tuple				=			rearrange(__lowerCamelCase						,     '''d -> d 1 1'''	)
		lowercase__   :    Optional[int]				=			rearrange(__lowerCamelCase						,     '''b c h w -> b c 1 h w'''	)

		lowercase__   :    Tuple				=			((x & mask) != 0).float()
		lowercase__   :    Dict				=			rearrange(__lowerCamelCase						,     '''b c d h w -> b (c d) h w'''	)
		lowercase__   :    Optional[Any]				=			bits * 2 - 1
		return bits



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase=BITS	)							->      List[Any]:
		lowercase__   :    str				=			x.device

		lowercase__   :    Tuple				=			(x > 0).int()
		lowercase__   :    Optional[Any]				=			2 ** torch.arange(bits - 1						,     -1						,     -1						,     device=__lowerCamelCase						,     dtype=torch.intaa	)

		lowercase__   :    List[Any]				=			rearrange(__lowerCamelCase						,     '''d -> d 1 1'''	)
		lowercase__   :    Tuple				=			rearrange(__lowerCamelCase						,     '''b (c d) h w -> b c d h w'''						,     d=8	)
		lowercase__   :    Union[str, Any]				=			reduce(x * mask						,     '''b c d h w -> b c h w'''						,     '''sum'''	)
		return (dec / 2_55).clamp(0.0						,     1.0	)



def      __UpperCAmelCase      (    self						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = 0.0						,     __lowerCamelCase = True						,     __lowerCamelCase=None						,     __lowerCamelCase = True						,     )							->      Union[DDIMSchedulerOutput, Tuple]:
		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)
		lowercase__   :    Union[str, Any]				=			timestep - self.config.num_train_timesteps // self.num_inference_steps

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

		lowercase__   :    str				=			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
		lowercase__   :    Optional[int]				=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

		# 4. Clip "predicted x_0"
		lowercase__   :    Dict				=			self.bit_scale
		if self.config.clip_sample:
				lowercase__   :    Union[str, Any]				=			torch.clamp(__lowerCamelCase						,     -scale						,     __lowerCamelCase	)

		# 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)
		lowercase__   :    Optional[Any]				=			self._get_variance(__lowerCamelCase						,     __lowerCamelCase	)
		lowercase__   :    Optional[int]				=			eta * variance ** 0.5

		if use_clipped_model_output:
				# the model_output is always re-derived from the clipped x_0 in Glide
				lowercase__   :    Any				=			(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
		lowercase__   :    Dict				=			(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
		lowercase__   :    Tuple				=			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
				lowercase__   :    Union[str, Any]				=			model_output.device if torch.is_tensor(__lowerCamelCase	) else '''cpu'''
				lowercase__   :    Union[str, Any]				=			torch.randn(model_output.shape						,     dtype=model_output.dtype						,     generator=__lowerCamelCase	).to(__lowerCamelCase	)
				lowercase__   :    Dict				=			self._get_variance(__lowerCamelCase						,     __lowerCamelCase	) ** 0.5 * eta * noise

				lowercase__   :    List[Any]				=			prev_sample + variance

		if not return_dict:
				return (prev_sample,)

		return DDIMSchedulerOutput(prev_sample=__lowerCamelCase						,     pred_original_sample=__lowerCamelCase	)



def      __UpperCAmelCase      (    self						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase="epsilon"						,     __lowerCamelCase=None						,     __lowerCamelCase = True						,     )							->      Union[DDPMSchedulerOutput, Tuple]:
		lowercase__   :    List[str]				=			timestep

		if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
				lowercase__		,			lowercase__   :    Optional[Any]				=			torch.split(__lowerCamelCase						,     sample.shape[1]						,     dim=1	)
		else:
				lowercase__   :    Optional[int]				=			None

		# 1. compute alphas, betas
		lowercase__   :    Tuple				=			self.alphas_cumprod[t]
		lowercase__   :    Tuple				=			self.alphas_cumprod[t - 1] if t > 0 else self.one
		lowercase__   :    Optional[Any]				=			1 - alpha_prod_t
		lowercase__   :    Union[str, Any]				=			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":
				lowercase__   :    Dict				=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif prediction_type == "sample":
				lowercase__   :    Optional[int]				=			model_output
		else:
				raise ValueError(f"""Unsupported prediction_type {prediction_type}."""	)

		# 3. Clip "predicted x_0"
		lowercase__   :    Tuple				=			self.bit_scale
		if self.config.clip_sample:
				lowercase__   :    Union[str, Any]				=			torch.clamp(__lowerCamelCase						,     -scale						,     __lowerCamelCase	)

		# 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
		lowercase__   :    Any				=			(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
		lowercase__   :    int				=			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
		lowercase__   :    List[str]				=			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		lowercase__   :    Any				=			0
		if t > 0:
				lowercase__   :    List[Any]				=			torch.randn(
				    model_output.size()						,     dtype=model_output.dtype						,     layout=model_output.layout						,     generator=__lowerCamelCase	).to(model_output.device	)
				lowercase__   :    Dict				=			(self._get_variance(__lowerCamelCase						,     predicted_variance=__lowerCamelCase	) ** 0.5) * noise

		lowercase__   :    str				=			pred_prev_sample + variance

		if not return_dict:
				return (pred_prev_sample,)

		return DDPMSchedulerOutput(prev_sample=__lowerCamelCase						,     pred_original_sample=__lowerCamelCase	)





class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  str			,_snake_case						:  UNetaDConditionModel			,_snake_case						:  Union[DDIMScheduler, DDPMScheduler]			,_snake_case						:  Optional[float] = 1.0			,)					->		Optional[Any]:






				"""simple docstring"""



				super().__init__()
				lowercase__   :    Optional[Any]				=			bit_scale
				lowercase__   :    Dict				=			(
				    ddim_bit_scheduler_step if isinstance(_snake_case			,_snake_case						) else ddpm_bit_scheduler_step
				)

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







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






				"""simple docstring"""



				lowercase__   :    Dict				=			torch.randn(
				    (batch_size, self.unet.config.in_channels, height, width)			,generator=_snake_case			,)
				lowercase__   :    Dict				=			decimal_to_bits(_snake_case						) * self.bit_scale
				lowercase__   :    Optional[Any]				=			latents.to(self.device						)

				self.scheduler.set_timesteps(_snake_case						)

				for t in self.progress_bar(self.scheduler.timesteps						):
						# predict the noise residual
						lowercase__   :    List[Any]				=			self.unet(_snake_case			,_snake_case						).sample

						# compute the previous noisy sample x_t -> x_t-1
						lowercase__   :    Any				=			self.scheduler.step(_snake_case			,_snake_case			,_snake_case						).prev_sample

				lowercase__   :    str				=			bits_to_decimal(_snake_case						)

				if output_type == "pil":
						lowercase__   :    str				=			self.numpy_to_pil(_snake_case						)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=_snake_case						)





"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
							from transformers import (
							    AutoConfig,
							    BertConfig,
							    GPTaConfig,
							    TaConfig,
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
							from transformers import (
							    AutoModel,
							    AutoModelForCausalLM,
							    AutoModelForMaskedLM,
							    AutoModelForPreTraining,
							    AutoModelForQuestionAnswering,
							    AutoModelForSeqaSeqLM,
							    AutoModelForSequenceClassification,
							    AutoModelWithLMHead,
							    BertForMaskedLM,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    BertModel,
							    GPTaLMHeadModel,
							    RobertaForMaskedLM,
							    TaForConditionalGeneration,
							)
@is_pt_tf_cross_test
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@slow
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Dict				=			TFAutoModel.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModel.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Tuple:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Dict				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForPreTraining.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			AutoModelForPreTraining.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForCausalLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			TFAutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForCausalLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[Any]				=			AutoModelForCausalLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Any						)					->		Tuple:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Any				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    str				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			TFAutoModelForMaskedLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    str				=			TFAutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			AutoModelForMaskedLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Any				=			AutoModelForMaskedLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Dict:






				"""simple docstring"""



				for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Union[str, Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_pt=_snake_case						)
						lowercase__		,			lowercase__   :    List[str]				=			TFAutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForSeqaSeqLM.from_pretrained(_snake_case			,from_tf=_snake_case						)
						lowercase__		,			lowercase__   :    Optional[int]				=			AutoModelForSeqaSeqLM.from_pretrained(
						    _snake_case			,output_loading_info=_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union[str, Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    Tuple				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			TFAutoModelForSequenceClassification.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Union[str, Any]				=			AutoModelForSequenceClassification.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				for model_name in ["bert-base-uncased"]:
						lowercase__   :    List[Any]				=			AutoConfig.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    str				=			TFAutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_pt=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)

						lowercase__   :    Any				=			AutoModelForQuestionAnswering.from_pretrained(_snake_case			,from_tf=_snake_case						)
						self.assertIsNotNone(_snake_case						)
						self.assertIsInstance(_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    Union[str, Any]				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)







		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			TFAutoModelWithLMHead.from_pretrained(_snake_case			,from_pt=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)

				lowercase__   :    int				=			AutoModelWithLMHead.from_pretrained(_snake_case			,from_tf=_snake_case						)
				self.assertIsInstance(_snake_case			,_snake_case						)
				self.assertEqual(model.num_parameters()			,14_410						)
				self.assertEqual(model.num_parameters(only_trainable=_snake_case						)			,14_410						)
