





"""simple docstring"""




import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					42
				UpperCamelCase			=					None





def  a_       (							_lowercase ,		_lowercase=0.999 ,		_lowercase="cosine" ,		):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						_UpperCamelCase    :      List[Any]						     =					[]
						for i in range(_lowercase     ):
												_UpperCamelCase    :      Dict						     =					i / num_diffusion_timesteps
												_UpperCamelCase    :      Dict						     =					(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowercase     ) / alpha_bar_fn(_lowercase     ) ,		_lowercase     )     )
						return torch.tensor(_lowercase ,		dtype=torch.floataa     )






class 				_a		(   _lowerCAmelCase			,       _lowerCAmelCase       ):
				UpperCamelCase			=					[e.name for e in KarrasDiffusionSchedulers]
				UpperCamelCase			=					1






				@register_to_config
				def __init__(				self	:					int,				lowerCAmelCase__	:					int = 1_0_0_0,				lowerCAmelCase__	:					float = 0.0_001,				lowerCAmelCase__	:					float = 0.02,				lowerCAmelCase__	:					str = "linear",				lowerCAmelCase__	:					Optional[Union[np.ndarray, List[float]]] = None,				lowerCAmelCase__	:					Optional[bool] = False,				lowerCAmelCase__	:					str = "epsilon",				lowerCAmelCase__	:					str = "linspace",				lowerCAmelCase__	:					int = 0,				)			->   List[str]:




										'''simple docstring'''



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

										_UpperCamelCase    :      Tuple						     =					1.0 - self.betas
										_UpperCamelCase    :      Any						     =					torch.cumprod(self.alphas,				dim=0							)

										_UpperCamelCase    :      Optional[int]						     =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
										_UpperCamelCase    :      int						     =					np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
										_UpperCamelCase    :      str						     =					torch.from_numpy(lowerCAmelCase__							)

										# setable values
										_UpperCamelCase    :      Optional[int]						     =					None
										_UpperCamelCase    :      Optional[int]						     =					use_karras_sigmas
										self.set_timesteps(lowerCAmelCase__,				lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					[]
										_UpperCamelCase    :      Union[str, Any]						     =					False






				@property
				def        snake_case			(				self	:					Tuple							)			->   List[str]:




										'''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	:					Optional[int],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Union[float, torch.FloatTensor]							)			->   torch.FloatTensor:




										'''simple docstring'''



										if isinstance(lowerCAmelCase__,				torch.Tensor							):
																_UpperCamelCase    :      Optional[Any]						     =					timestep.to(self.timesteps.device							)
										_UpperCamelCase    :      int						     =					(self.timesteps == timestep).nonzero().item()
										_UpperCamelCase    :      Dict						     =					self.sigmas[step_index]
										_UpperCamelCase    :      Union[str, Any]						     =					sample / ((sigma**2 + 1) ** 0.5)
										_UpperCamelCase    :      str						     =					True
										return sample






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Optional[int]							)			->   Any:




										'''simple docstring'''



										def lms_derivative(lowerCAmelCase__	:					Tuple							):
																_UpperCamelCase    :      Dict						     =					1.0
																for k in range(lowerCAmelCase__							):
																						if current_order == k:
																												continue
																						prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																return prod

										_UpperCamelCase    :      int						     =					integrate.quad(lowerCAmelCase__,				self.sigmas[t],				self.sigmas[t + 1],				epsrel=1e-4							)[0]

										return integrated_coeff






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Union[str, torch.device] = None							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																_UpperCamelCase    :      List[Any]						     =					np.linspace(0,				self.config.num_train_timesteps - 1,				lowerCAmelCase__,				dtype=lowerCAmelCase__							)[
																    ::-1
																].copy()
										elif self.config.timestep_spacing == "leading":
																_UpperCamelCase    :      List[Any]						     =					self.config.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
																_UpperCamelCase    :      Optional[int]						     =					(np.arange(0,				lowerCAmelCase__							) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__							)
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																_UpperCamelCase    :      int						     =					self.config.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
																_UpperCamelCase    :      Optional[Any]						     =					(np.arange(self.config.num_train_timesteps,				0,				-step_ratio							)).round().copy().astype(lowerCAmelCase__							)
																timesteps -= 1
										else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""							)

										_UpperCamelCase    :      Any						     =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
										_UpperCamelCase    :      Optional[Any]						     =					np.log(lowerCAmelCase__							)
										_UpperCamelCase    :      Tuple						     =					np.interp(lowerCAmelCase__,				np.arange(0,				len(lowerCAmelCase__							)							),				lowerCAmelCase__							)

										if self.use_karras_sigmas:
																_UpperCamelCase    :      Dict						     =					self._convert_to_karras(in_sigmas=lowerCAmelCase__							)
																_UpperCamelCase    :      List[str]						     =					np.array([self._sigma_to_t(lowerCAmelCase__,				lowerCAmelCase__							) for sigma in sigmas]							)

										_UpperCamelCase    :      Optional[int]						     =					np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)

										_UpperCamelCase    :      Optional[Any]						     =					torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)
										if str(lowerCAmelCase__							).startswith('''mps'''							):
																# mps does not support float64
																_UpperCamelCase    :      Dict						     =					torch.from_numpy(lowerCAmelCase__							).to(lowerCAmelCase__,				dtype=torch.floataa							)
										else:
																_UpperCamelCase    :      Union[str, Any]						     =					torch.from_numpy(lowerCAmelCase__							).to(device=lowerCAmelCase__							)

										_UpperCamelCase    :      Union[str, Any]						     =					[]






				def        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Union[str, Any]							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					np.log(lowerCAmelCase__							)

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

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

										_UpperCamelCase    :      str						     =					log_sigmas[low_idx]
										_UpperCamelCase    :      List[Any]						     =					log_sigmas[high_idx]

										# interpolate sigmas
										_UpperCamelCase    :      List[str]						     =					(low - log_sigma) / (low - high)
										_UpperCamelCase    :      Union[str, Any]						     =					np.clip(lowerCAmelCase__,				0,				1							)

										# transform interpolation to time range
										_UpperCamelCase    :      Any						     =					(1 - w) * low_idx + w * high_idx
										_UpperCamelCase    :      List[str]						     =					t.reshape(sigma.shape							)
										return t






				def        snake_case			(				self	:					str,				lowerCAmelCase__	:					torch.FloatTensor							)			->   torch.FloatTensor:




										'''simple docstring'''



										_UpperCamelCase    :      float						     =					in_sigmas[-1].item()
										_UpperCamelCase    :      float						     =					in_sigmas[0].item()

										_UpperCamelCase    :      Tuple						     =					7.0  # 7.0 is the value used in the paper
										_UpperCamelCase    :      Optional[int]						     =					np.linspace(0,				1,				self.num_inference_steps							)
										_UpperCamelCase    :      Dict						     =					sigma_min ** (1 / rho)
										_UpperCamelCase    :      List[Any]						     =					sigma_max ** (1 / rho)
										_UpperCamelCase    :      Optional[int]						     =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					Union[float, torch.FloatTensor],				lowerCAmelCase__	:					torch.FloatTensor,				lowerCAmelCase__	:					int = 4,				lowerCAmelCase__	:					bool = True,				)			->   Union[LMSDiscreteSchedulerOutput, Tuple]:




										'''simple docstring'''



										if not self.is_scale_input_called:
																warnings.warn(
																    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																    '''See `StableDiffusionPipeline` for a usage example.'''							)

										if isinstance(lowerCAmelCase__,				torch.Tensor							):
																_UpperCamelCase    :      Union[str, Any]						     =					timestep.to(self.timesteps.device							)
										_UpperCamelCase    :      Optional[int]						     =					(self.timesteps == timestep).nonzero().item()
										_UpperCamelCase    :      List[str]						     =					self.sigmas[step_index]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																_UpperCamelCase    :      Union[str, Any]						     =					sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																_UpperCamelCase    :      Tuple						     =					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										elif self.config.prediction_type == "sample":
																_UpperCamelCase    :      List[str]						     =					model_output
										else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

										# 2. Convert to an ODE derivative
										_UpperCamelCase    :      List[Any]						     =					(sample - pred_original_sample) / sigma
										self.derivatives.append(lowerCAmelCase__							)
										if len(self.derivatives							) > order:
																self.derivatives.pop(0							)

										# 3. Compute linear multistep coefficients
										_UpperCamelCase    :      List[str]						     =					min(step_index + 1,				lowerCAmelCase__							)
										_UpperCamelCase    :      List[Any]						     =					[self.get_lms_coefficient(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							) for curr_order in range(lowerCAmelCase__							)]

										# 4. Compute previous sample based on the derivatives path
										_UpperCamelCase    :      Dict						     =					sample + sum(
										    coeff * derivative for coeff, derivative in zip(lowerCAmelCase__,				reversed(self.derivatives							)							)							)

										if not return_dict:
																return (prev_sample,)

										return LMSDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__,				pred_original_sample=lowerCAmelCase__							)






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




										'''simple docstring'''



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

										_UpperCamelCase    :      Any						     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										_UpperCamelCase    :      str						     =					sigmas[step_indices].flatten()
										while len(sigma.shape							) < len(original_samples.shape							):
																_UpperCamelCase    :      Optional[Any]						     =					sigma.unsqueeze(-1							)

										_UpperCamelCase    :      List[Any]						     =					original_samples + noise * sigma
										return noisy_samples






				def __len__(				self	:					Any							)			->   int:




										'''simple docstring'''



										return self.config.num_train_timesteps










"""simple docstring"""




import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


UpperCamelCase_     =logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCamelCase_     ="""lfs-multipart-upload"""




class 				_a		(   _lowerCAmelCase       ):






				@staticmethod
				def        snake_case			(				lowerCAmelCase__	:					ArgumentParser							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					parser.add_parser(
										    '''lfs-enable-largefiles''',				help=(
										        '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
										    ),				)
										enable_parser.add_argument('''path''',				type=lowerCAmelCase__,				help='''Local path to repository you want to configure.'''							)
										enable_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsEnableCommand(lowerCAmelCase__							)							)

										_UpperCamelCase    :      Any						     =					parser.add_parser(
										    lowerCAmelCase__,				help=(
										        '''Deprecated: use `huggingface-cli` instead. '''
										        '''Command will get called by git-lfs, do not call it directly.'''
										    ),				)
										upload_parser.set_defaults(func=lambda lowerCAmelCase__							: LfsUploadCommand(lowerCAmelCase__							)							)




class 				_a		:






				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					str							)			->   Dict:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					str							)			->   str:




										'''simple docstring'''



										warnings.warn(
										    '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''							)
										_UpperCamelCase    :      Union[str, Any]						     =					os.path.abspath(self.args.path							)
										if not os.path.isdir(lowerCAmelCase__							):
																print('''This does not look like a valid git repo.'''							)
																exit(1							)
										subprocess.run(
										    '''git config lfs.customtransfer.multipart.path transformers-cli'''.split(),				check=lowerCAmelCase__,				cwd=lowerCAmelCase__							)
										subprocess.run(
										    f"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split(),				check=lowerCAmelCase__,				cwd=lowerCAmelCase__,				)
										print('''Local repo set up for largefiles'''							)





def  a_       (							_lowercase     ):
						_UpperCamelCase    :      Tuple						     =					json.dumps(_lowercase     ) + '''\n'''
						sys.stdout.write(_lowercase     )
						sys.stdout.flush()





def  a_       (							):
						_UpperCamelCase    :      Optional[Any]						     =					json.loads(sys.stdin.readline().strip()     )

						if "terminate" in (msg.get('''type'''     ), msg.get('''event'''     )):
												# terminate message received
												return None

						if msg.get('''event'''     ) not in ("download", "upload"):
												logger.critical('''Received unexpected message'''     )
												sys.exit(1     )

						return msg




class 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Tuple,				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					filepath
										_UpperCamelCase    :      Tuple						     =					seek_from
										_UpperCamelCase    :      Optional[int]						     =					read_limit
										_UpperCamelCase    :      List[str]						     =					0






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




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					open(self.filepath,				'''rb'''							)
										self.f.seek(self.seek_from							)
										return self






				def __len__(				self	:					str							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					os.fstat(self.f.fileno()							).st_size
										return min(self.read_limit,				total_length - self.seek_from							)






				def        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					List[str]=-1							)			->   int:




										'''simple docstring'''



										if self.n_seen >= self.read_limit:
																return b""
										_UpperCamelCase    :      str						     =					self.read_limit - self.n_seen
										_UpperCamelCase    :      Dict						     =					self.f.read(remaining_amount if n < 0 else min(lowerCAmelCase__,				lowerCAmelCase__							)							)
										self.n_seen += len(lowerCAmelCase__							)
										return data






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




										'''simple docstring'''



										yield self.read(n=4 * 1_0_2_4 * 1_0_2_4							)






				def __exit__(				self	:					Optional[Any],				*lowerCAmelCase__	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										self.f.close()




class 				_a		:






				def __init__(				self	:					List[str],				lowerCAmelCase__	:					Optional[Any]							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					args






				def        snake_case			(				self	:					List[Any]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					json.loads(sys.stdin.readline().strip()							)
										if not (init_msg.get('''event'''							) == "init" and init_msg.get('''operation'''							) == "upload"):
																write_msg({'''error''': {'''code''': 3_2, '''message''': '''Wrong lfs init operation'''}}							)
																sys.exit(1							)

										# The transfer process should use the information it needs from the
										# initiation structure, and also perform any one-off setup tasks it
										# needs to do. It should then respond on stdout with a simple empty
										# confirmation structure, as follows:
										write_msg({}							)

										# After the initiation exchange, git-lfs will send any number of
										# transfer requests to the stdin of the transfer process, in a serial sequence.
										while True:
																_UpperCamelCase    :      int						     =					read_msg()
																if msg is None:
																						# When all transfers have been processed, git-lfs will send
																						# a terminate event to the stdin of the transfer process.
																						# On receiving this message the transfer process should
																						# clean up and terminate. No response is expected.
																						sys.exit(0							)

																_UpperCamelCase    :      Union[str, Any]						     =					msg['''oid''']
																_UpperCamelCase    :      List[str]						     =					msg['''path''']
																_UpperCamelCase    :      Optional[int]						     =					msg['''action''']['''href''']
																_UpperCamelCase    :      Optional[int]						     =					msg['''action''']['''header''']
																_UpperCamelCase    :      Tuple						     =					int(header.pop('''chunk_size'''							)							)
																_UpperCamelCase    :      List[str]						     =					list(header.values()							)

																_UpperCamelCase    :      str						     =					[]
																for i, presigned_url in enumerate(lowerCAmelCase__							):
																						with FileSlice(lowerCAmelCase__,				seek_from=i * chunk_size,				read_limit=lowerCAmelCase__							) as data:
																												_UpperCamelCase    :      Any						     =					requests.put(lowerCAmelCase__,				data=lowerCAmelCase__							)
																												r.raise_for_status()
																												parts.append(
																												    {
																												        '''etag''': r.headers.get('''etag'''							),
																												        '''partNumber''': i + 1,
																												    }							)
																												# In order to support progress reporting while data is uploading / downloading,
																												# the transfer process should post messages to stdout
																												write_msg(
																												    {
																												        '''event''': '''progress''',
																												        '''oid''': oid,
																												        '''bytesSoFar''': (i + 1) * chunk_size,
																												        '''bytesSinceLast''': chunk_size,
																												    }							)
																												# Not precise but that's ok.

																_UpperCamelCase    :      str						     =					requests.post(
																    lowerCAmelCase__,				json={
																        '''oid''': oid,
																        '''parts''': parts,
																    },				)
																r.raise_for_status()

																write_msg({'''event''': '''complete''', '''oid''': oid}							)



