





"""simple docstring"""






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def     __A      (			a_						:Optional[int]   ,       a_						:List[str]=0.9_9_9   ,       a_						:Union[str, Any]="cosine"   ,       )		->		int:
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(a_						:Optional[Any]):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(a_						:Dict):
												return math.exp(t * -1_2.0)

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""")

				__a :						Any			       =  []
				for i in range(lowerCamelCase__):
								__a :						List[str]			       =  i / num_diffusion_timesteps
								__a :						Optional[int]			       =  (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase__) / alpha_bar_fn(lowerCamelCase__)   ,       lowerCamelCase__))
				return torch.tensor(lowerCamelCase__   ,       dtype=torch.floataa)






class 				__lowercase			(			_UpperCamelCase   ,      _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  [e.name for e in KarrasDiffusionSchedulers]
					__lowerCAmelCase  =  2







					@register_to_config
					def __init__(						self	,			_UpperCAmelCase = 1000	,			_UpperCAmelCase = 0.0_0_0_8_5	,			_UpperCAmelCase = 0.0_1_2	,			_UpperCAmelCase = "linear"	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "epsilon"	,			_UpperCAmelCase = "linspace"	,			_UpperCAmelCase = 0	,			):

									if trained_betas is not None:
													__a :						List[Any]			       =  torch.tensor(_UpperCAmelCase	,			dtype=torch.floataa     )
									elif beta_schedule == "linear":
													__a :						int			       =  torch.linspace(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			dtype=torch.floataa     )
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__a :						Any			       =  (
													    torch.linspace(beta_start**0.5	,			beta_end**0.5	,			_UpperCAmelCase	,			dtype=torch.floataa     ) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__a :						List[Any]			       =  betas_for_alpha_bar(_UpperCAmelCase     )
									else:
													raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

									__a :						Union[str, Any]			       =  1.0 - self.betas
									__a :						Optional[int]			       =  torch.cumprod(self.alphas	,			dim=0     )

									#  set all values
									self.set_timesteps(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=None     ):

									if schedule_timesteps is None:
													__a :						List[Any]			       =  self.timesteps

									__a :						Union[str, Any]			       =  (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter     ) == 0:
													__a :						Any			       =  1 if len(_UpperCAmelCase     ) > 1 else 0
									else:
													__a :						Optional[int]			       =  timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase     ) else timestep
													__a :						Any			       =  self._index_counter[timestep_int]

									return indices[pos].item()







					@property
					def  _lowerCamelCase		(						self     ):

									if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			):
									__a :						Optional[int]			       =  self.index_for_timestep(_UpperCAmelCase     )

									if self.state_in_first_order:
													__a :						Optional[int]			       =  self.sigmas[step_index]
									else:
													__a :						str			       =  self.sigmas_interpol[step_index]

									__a :						List[Any]			       =  sample / ((sigma**2 + 1) ** 0.5)
									return sample







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			):
									__a :						Any			       =  num_inference_steps

									__a :						Dict			       =  num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
													__a :						Union[str, Any]			       =  np.linspace(0	,			num_train_timesteps - 1	,			_UpperCAmelCase	,			dtype=_UpperCAmelCase     )[::-1].copy()
									elif self.config.timestep_spacing == "leading":
													__a :						int			       =  num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__a :						str			       =  (np.arange(0	,			_UpperCAmelCase     ) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase     )
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__a :						List[Any]			       =  num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__a :						Tuple			       =  (np.arange(_UpperCAmelCase	,			0	,			-step_ratio     )).round().copy().astype(_UpperCAmelCase     )
													timesteps -= 1
									else:
													raise ValueError(
													    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""     )

									__a :						Tuple			       =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
									__a :						Dict			       =  torch.from_numpy(np.log(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )

									__a :						List[str]			       =  np.interp(_UpperCAmelCase	,			np.arange(0	,			len(_UpperCAmelCase     )     )	,			_UpperCAmelCase     )
									__a :						Dict			       =  np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
									__a :						str			       =  torch.from_numpy(_UpperCAmelCase     ).to(device=_UpperCAmelCase     )

									# interpolate sigmas
									__a :						str			       =  sigmas.log().lerp(sigmas.roll(1     ).log()	,			0.5     ).exp()

									__a :						List[Any]			       =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
									__a :						List[Any]			       =  torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )

									if str(_UpperCAmelCase     ).startswith('''mps'''     ):
													# mps does not support float64
													__a :						Any			       =  torch.from_numpy(_UpperCAmelCase     ).to(_UpperCAmelCase	,			dtype=torch.floataa     )
									else:
													__a :						Optional[int]			       =  torch.from_numpy(_UpperCAmelCase     ).to(_UpperCAmelCase     )

									# interpolate timesteps
									__a :						Tuple			       =  self.sigma_to_t(_UpperCAmelCase     ).to(_UpperCAmelCase	,			dtype=timesteps.dtype     )
									__a :						Optional[int]			       =  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])	,			dim=-1     ).flatten()

									__a :						List[str]			       =  torch.cat([timesteps[:1], interleaved_timesteps]     )

									__a :						Tuple			       =  None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									__a :						str			       =  defaultdict(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Dict			       =  sigma.log()

									# get distribution
									__a :						Tuple			       =  log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									__a :						Union[str, Any]			       =  dists.ge(0     ).cumsum(dim=0     ).argmax(dim=0     ).clamp(max=self.log_sigmas.shape[0] - 2     )
									__a :						Tuple			       =  low_idx + 1

									__a :						Any			       =  self.log_sigmas[low_idx]
									__a :						List[Any]			       =  self.log_sigmas[high_idx]

									# interpolate sigmas
									__a :						Dict			       =  (low - log_sigma) / (low - high)
									__a :						Optional[Any]			       =  w.clamp(0	,			1     )

									# transform interpolation to time range
									__a :						Union[str, Any]			       =  (1 - w) * low_idx + w * high_idx
									__a :						Any			       =  t.view(sigma.shape     )
									return t







					@property
					def  _lowerCamelCase		(						self     ):
									return self.sample is None







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = True	,			):
									__a :						Optional[int]			       =  self.index_for_timestep(_UpperCAmelCase     )

									# advance index counter by 1
									__a :						str			       =  timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase     ) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
													__a :						str			       =  self.sigmas[step_index]
													__a :						Optional[int]			       =  self.sigmas_interpol[step_index + 1]
													__a :						Dict			       =  self.sigmas[step_index + 1]
									else:
													# 2nd order / KDPM2's method
													__a :						str			       =  self.sigmas[step_index - 1]
													__a :						Optional[Any]			       =  self.sigmas_interpol[step_index]
													__a :						int			       =  self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									__a :						Any			       =  0
									__a :						Tuple			       =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
													__a :						Optional[Any]			       =  sigma_hat if self.state_in_first_order else sigma_interpol
													__a :						List[Any]			       =  sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
													__a :						Any			       =  sigma_hat if self.state_in_first_order else sigma_interpol
													__a :						Tuple			       =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
									elif self.config.prediction_type == "sample":
													raise NotImplementedError('''prediction_type not implemented yet: sample'''     )
									else:
													raise ValueError(
													    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""     )

									if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													__a :						Union[str, Any]			       =  (sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													__a :						List[str]			       =  sigma_interpol - sigma_hat

													# store for 2nd order step
													__a :						str			       =  sample
									else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													__a :						Any			       =  (sample - pred_original_sample) / sigma_interpol

													# 3. delta timestep
													__a :						str			       =  sigma_next - sigma_hat

													__a :						Optional[Any]			       =  self.sample
													__a :						Optional[Any]			       =  None

									__a :						Dict			       =  sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			):
									__a :						str			       =  self.sigmas.to(device=original_samples.device	,			dtype=original_samples.dtype     )
									if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase     ):
													# mps does not support float64
													__a :						Dict			       =  self.timesteps.to(original_samples.device	,			dtype=torch.floataa     )
													__a :						Any			       =  timesteps.to(original_samples.device	,			dtype=torch.floataa     )
									else:
													__a :						List[Any]			       =  self.timesteps.to(original_samples.device     )
													__a :						Any			       =  timesteps.to(original_samples.device     )

									__a :						List[Any]			       =  [self.index_for_timestep(_UpperCAmelCase	,			_UpperCAmelCase     ) for t in timesteps]

									__a :						Dict			       =  sigmas[step_indices].flatten()
									while len(sigma.shape     ) < len(original_samples.shape     ):
													__a :						Optional[int]			       =  sigma.unsqueeze(-1     )

									__a :						int			       =  original_samples + noise * sigma
									return noisy_samples







					def __len__(						self     ):
									return self.config.num_train_timesteps






"""simple docstring"""






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A 			=  ['''bert-base-uncased''', '''bert-base-cased''']
A 			=  '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():




		class 				__lowercase			(			tf.keras.Model							):







							'''simple docstring'''




							def __init__(						self	,			_UpperCAmelCase     ):
											super().__init__()
											__a :						Any			       =  tokenizer
											__a :						Optional[Any]			       =  AutoConfig.from_pretrained(_UpperCAmelCase     )
											__a :						str			       =  TFAutoModel.from_config(_UpperCAmelCase     )







							def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
											__a :						Any			       =  self.tokenizer(_UpperCAmelCase     )
											__a :						int			       =  self.bert(**_UpperCAmelCase     )
											return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									super().setUp()

									__a :						Any			       =  [
									    BertTokenizer.from_pretrained(_UpperCAmelCase     ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									__a :						Union[str, Any]			       =  [TFBertTokenizer.from_pretrained(_UpperCAmelCase     ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
									    TFBertTokenizer.from_pretrained(_UpperCAmelCase	,			use_fast_bert_tokenizer=_UpperCAmelCase     )
									    for checkpoint in TOKENIZER_CHECKPOINTS
									]
									assert len(self.tokenizers     ) == len(self.tf_tokenizers     )

									__a :						Tuple			       =  [
									    '''This is a straightforward English test sentence.''',
									    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
									    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
									    '''And some much more rare Chinese: 齉 堃 齉堃''',
									    '''Je vais aussi écrire en français pour tester les accents''',
									    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
									]
									__a :						Any			       =  list(zip(self.test_sentences	,			self.test_sentences[::-1]     )     )







					def  _lowerCamelCase		(						self     ):
									for tokenizer, tf_tokenizer in zip(self.tokenizers	,			self.tf_tokenizers     ):
													for test_inputs in (self.test_sentences, self.paired_sentences):
																	__a :						List[Any]			       =  tokenizer(_UpperCAmelCase	,			return_tensors='''tf'''	,			padding='''longest'''     )
																	__a :						List[str]			       =  tf_tokenizer(_UpperCAmelCase     )

																	for key in python_outputs.keys():
																					self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape     )     )
																					self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,			tf.intaa     ) == tf_outputs[key]     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Dict			       =  tf_tokenizer(self.paired_sentences     )
													__a :						str			       =  tf_tokenizer(
													    text=[sentence[0] for sentence in self.paired_sentences]	,			text_pair=[sentence[1] for sentence in self.paired_sentences]	,			)
													for key in merged_outputs.keys():
																	self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,			tf.intaa     ) == separated_outputs[key]     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Tuple			       =  tf.function(_UpperCAmelCase     )
													for test_inputs in (self.test_sentences, self.paired_sentences):
																	__a :						List[str]			       =  tf.constant(_UpperCAmelCase     )
																	__a :						Tuple			       =  compiled_tokenizer(_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  tf_tokenizer(_UpperCAmelCase     )

																	for key in eager_outputs.keys():
																					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Dict			       =  ModelToSave(tokenizer=_UpperCAmelCase     )
													__a :						Tuple			       =  tf.convert_to_tensor(self.test_sentences     )
													__a :						List[str]			       =  model(_UpperCAmelCase     )  # Build model with some sample inputs
													with TemporaryDirectory() as tempdir:
																	__a :						Tuple			       =  Path(_UpperCAmelCase     ) / '''saved.model'''
																	model.save(_UpperCAmelCase     )
																	__a :						Tuple			       =  tf.keras.models.load_model(_UpperCAmelCase     )
													__a :						int			       =  loaded_model(_UpperCAmelCase     )
													# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
													self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output     )     )	,			1e-5     )