

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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple=0.999   ,      SCREAMING_SNAKE_CASE__      :     List[Any]="cosine"   ,      )							->				int:





    '''simple docstring'''






    if alpha_transform_type == "cosine":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE__      :     Any				):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(SCREAMING_SNAKE_CASE__      :     str				):
            return math.exp(t * -12.0				)

    else:
        raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

    A__   				=       []
    for i in range(SCREAMING_SNAKE_CASE__				):
        A__   				=       i / num_diffusion_timesteps
        A__   				=       (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__				) / alpha_bar_fn(SCREAMING_SNAKE_CASE__				)   ,      SCREAMING_SNAKE_CASE__				)				)
    return torch.tensor(SCREAMING_SNAKE_CASE__   ,      dtype=torch.floataa				)


class 			A						(							_UpperCAmelCase   ,							_UpperCAmelCase			):







    """simple docstring"""





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


    @register_to_config
    def __init__(				self			:	Tuple,lowercase_			:	int = 1_0_0_0,lowercase_			:	float = 0.00_085,lowercase_			:	float = 0.012,lowercase_			:	str = "linear",lowercase_			:	Optional[Union[np.ndarray, List[float]]] = None,lowercase_			:	str = "epsilon",lowercase_			:	Optional[bool] = False,lowercase_			:	Optional[bool] = False,lowercase_			:	float = 1.0,lowercase_			:	str = "linspace",lowercase_			:	int = 0,)->   Any:





        '''simple docstring'''







        if trained_betas is not None:
            A__   				=       torch.tensor(lowercase_,dtype=torch.floataa		)
        elif beta_schedule == "linear":
            A__   				=       torch.linspace(lowercase_,lowercase_,lowercase_,dtype=torch.floataa		)
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            A__   				=       (
                torch.linspace(beta_start**0.5,beta_end**0.5,lowercase_,dtype=torch.floataa		) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            A__   				=       betas_for_alpha_bar(lowercase_,alpha_transform_type='cosine'		)
        elif beta_schedule == "exp":
            A__   				=       betas_for_alpha_bar(lowercase_,alpha_transform_type='exp'		)
        else:
            raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'		)

        A__   				=       1.0 - self.betas
        A__   				=       torch.cumprod(self.alphas,dim=0		)

        #  set all values
        self.set_timesteps(lowercase_,lowercase_,lowercase_		)
        A__   				=       use_karras_sigmas


    def 						snake_case__					(				self			:	Any,lowercase_			:	str,lowercase_			:	Tuple=None		)->   str:





        '''simple docstring'''







        if schedule_timesteps is None:
            A__   				=       self.timesteps

        A__   				=       (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__   				=       1 if len(lowercase_		) > 1 else 0
        else:
            A__   				=       timestep.cpu().item() if torch.is_tensor(lowercase_		) else timestep
            A__   				=       self._index_counter[timestep_int]

        return indices[pos].item()


    @property
    def 						snake_case__					(				self			:	Tuple		)->   Optional[int]:





        '''simple docstring'''







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

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


    def 						snake_case__					(				self			:	List[str],lowercase_			:	torch.FloatTensor,lowercase_			:	Union[float, torch.FloatTensor],)->   torch.FloatTensor:





        '''simple docstring'''







        A__   				=       self.index_for_timestep(lowercase_		)

        A__   				=       self.sigmas[step_index]
        A__   				=       sample / ((sigma**2 + 1) ** 0.5)
        return sample


    def 						snake_case__					(				self			:	int,lowercase_			:	int,lowercase_			:	Union[str, torch.device] = None,lowercase_			:	Optional[int] = None,)->   Optional[int]:





        '''simple docstring'''







        A__   				=       num_inference_steps

        A__   				=       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__   				=       np.linspace(0,num_train_timesteps - 1,lowercase_,dtype=lowercase_		)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
            A__   				=       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__   				=       (np.arange(0,lowercase_		) * step_ratio).round()[::-1].copy().astype(lowercase_		)
            timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
            A__   				=       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__   				=       (np.arange(lowercase_,0,-step_ratio		)).round().copy().astype(lowercase_		)
            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__   				=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
        A__   				=       np.log(lowercase_		)
        A__   				=       np.interp(lowercase_,np.arange(0,len(lowercase_		)		),lowercase_		)

        if self.config.use_karras_sigmas:
            A__   				=       self._convert_to_karras(in_sigmas=lowercase_,num_inference_steps=self.num_inference_steps		)
            A__   				=       np.array([self._sigma_to_t(lowercase_,lowercase_		) for sigma in sigmas]		)

        A__   				=       np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
        A__   				=       torch.from_numpy(lowercase_		).to(device=lowercase_		)
        A__   				=       torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2		), sigmas[-1:]]		)

        A__   				=       torch.from_numpy(lowercase_		)
        A__   				=       torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2		)]		)

        if str(lowercase_		).startswith('mps'		):
            # mps does not support float64
            A__   				=       timesteps.to(lowercase_,dtype=torch.floataa		)
        else:
            A__   				=       timesteps.to(device=lowercase_		)

        # empty dt and derivative
        A__   				=       None
        A__   				=       None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        A__   				=       defaultdict(lowercase_		)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any,lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       np.log(lowercase_		)

        # get distribution
        A__   				=       log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        A__   				=       np.cumsum((dists >= 0),axis=0		).argmax(axis=0		).clip(max=log_sigmas.shape[0] - 2		)
        A__   				=       low_idx + 1

        A__   				=       log_sigmas[low_idx]
        A__   				=       log_sigmas[high_idx]

        # interpolate sigmas
        A__   				=       (low - log_sigma) / (low - high)
        A__   				=       np.clip(lowercase_,0,1		)

        # transform interpolation to time range
        A__   				=       (1 - w) * low_idx + w * high_idx
        A__   				=       t.reshape(sigma.shape		)
        return t


    def 						snake_case__					(				self			:	Dict,lowercase_			:	torch.FloatTensor,lowercase_			:	Tuple		)->   torch.FloatTensor:





        '''simple docstring'''







        A__   				=       in_sigmas[-1].item()
        A__   				=       in_sigmas[0].item()

        A__   				=       7.0  # 7.0 is the value used in the paper
        A__   				=       np.linspace(0,1,lowercase_		)
        A__   				=       sigma_min ** (1 / rho)
        A__   				=       sigma_max ** (1 / rho)
        A__   				=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas


    @property
    def 						snake_case__					(				self			:	Tuple		)->   int:





        '''simple docstring'''







        return self.dt is None


    def 						snake_case__					(				self			:	Any,lowercase_			:	Union[torch.FloatTensor, np.ndarray],lowercase_			:	Union[float, torch.FloatTensor],lowercase_			:	Union[torch.FloatTensor, np.ndarray],lowercase_			:	bool = True,)->   Union[SchedulerOutput, Tuple]:





        '''simple docstring'''







        A__   				=       self.index_for_timestep(lowercase_		)

        # advance index counter by 1
        A__   				=       timestep.cpu().item() if torch.is_tensor(lowercase_		) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
            A__   				=       self.sigmas[step_index]
            A__   				=       self.sigmas[step_index + 1]
        else:
            # 2nd order / Heun's method
            A__   				=       self.sigmas[step_index - 1]
            A__   				=       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__   				=       0
        A__   				=       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__   				=       sigma_hat if self.state_in_first_order else sigma_next
            A__   				=       sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
            A__   				=       sigma_hat if self.state_in_first_order else sigma_next
            A__   				=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
        elif self.config.prediction_type == "sample":
            A__   				=       model_output
        else:
            raise ValueError(
                F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'		)

        if self.config.clip_sample:
            A__   				=       pred_original_sample.clamp(
                -self.config.clip_sample_range,self.config.clip_sample_range		)

        if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            A__   				=       (sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            A__   				=       sigma_next - sigma_hat

            # store for 2nd order step
            A__   				=       derivative
            A__   				=       dt
            A__   				=       sample
        else:
            # 2. 2nd order / Heun's method
            A__   				=       (sample - pred_original_sample) / sigma_next
            A__   				=       (self.prev_derivative + derivative) / 2

            # 3. take prev timestep & sample
            A__   				=       self.dt
            A__   				=       self.sample

            # free dt and derivative
            # Note, this puts the scheduler in "first order mode"
            A__   				=       None
            A__   				=       None
            A__   				=       None

        A__   				=       sample + derivative * dt

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=lowercase_		)


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	torch.FloatTensor,lowercase_			:	torch.FloatTensor,lowercase_			:	torch.FloatTensor,)->   torch.FloatTensor:





        '''simple docstring'''







        A__   				=       self.sigmas.to(device=original_samples.device,dtype=original_samples.dtype		)
        if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_		):
            # mps does not support float64
            A__   				=       self.timesteps.to(original_samples.device,dtype=torch.floataa		)
            A__   				=       timesteps.to(original_samples.device,dtype=torch.floataa		)
        else:
            A__   				=       self.timesteps.to(original_samples.device		)
            A__   				=       timesteps.to(original_samples.device		)

        A__   				=       [self.index_for_timestep(lowercase_,lowercase_		) for t in timesteps]

        A__   				=       sigmas[step_indices].flatten()
        while len(sigma.shape		) < len(original_samples.shape		):
            A__   				=       sigma.unsqueeze(-1		)

        A__   				=       original_samples + noise * sigma
        return noisy_samples


    def __len__(				self			:	List[str]		)->   List[Any]:





        '''simple docstring'''







        return self.config.num_train_timesteps




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str		)->   List[Any]:





        '''simple docstring'''







        for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['bs'],model_result['ss']		):
                A__   				=       model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(lowercase_		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sgugger/tiny-distilbert-classification'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    @unittest.skipIf(torch_device == 'cpu','Cant do half precision'		)
    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__   				=       None
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    @unittest.skipIf(torch_device == 'cpu','Can\'t do half precision'		)
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,save_to_csv=lowercase_,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(lowercase_,'inf_time.csv'		),train_memory_csv_file=os.path.join(lowercase_,'train_mem.csv'		),inference_memory_csv_file=os.path.join(lowercase_,'inf_mem.csv'		),train_time_csv_file=os.path.join(lowercase_,'train_time.csv'		),env_info_csv_file=os.path.join(lowercase_,'env.csv'		),multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            benchmark.run()
            self.assertTrue(Path(os.path.join(lowercase_,'inf_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'inf_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'env.csv'		)		).exists()		)


    def 						snake_case__					(				self			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_			:	Optional[Any]		):
            self.assertTrue(hasattr(lowercase_,'sequential'		)		)
            self.assertTrue(hasattr(lowercase_,'cumulative'		)		)
            self.assertTrue(hasattr(lowercase_,'current'		)		)
            self.assertTrue(hasattr(lowercase_,'total'		)		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],log_filename=os.path.join(lowercase_,'log.txt'		),log_print=lowercase_,trace_memory_line_by_line=lowercase_,multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            A__   				=       benchmark.run()
            _check_summary_is_not_empty(result.inference_summary		)
            _check_summary_is_not_empty(result.train_summary		)
            self.assertTrue(Path(os.path.join(lowercase_,'log.txt'		)		).exists()		)

