



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


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


_lowerCamelCase         =	"""lfs-multipart-upload"""

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				ArgumentParser					)->Any:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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=UpperCamelCase  ,	help="Local path to repository you want to configure."					)
      enable_parser.set_defaults(func=lambda UpperCamelCase					: LfsEnableCommand(UpperCamelCase					)					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parser.add_parser(
          UpperCamelCase  ,	help=(
              "Deprecated: use `huggingface-cli` instead. "
              "Command will get called by git-lfs, do not call it directly."
          )  ,	)
      upload_parser.set_defaults(func=lambda UpperCamelCase					: LfsUploadCommand(UpperCamelCase					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				List[Any]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     str							=						args






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






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						json.dumps(__lowerCamelCase  ) + "\n"
 sys.stdout.write(__lowerCamelCase  )
 sys.stdout.flush()






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						filepath
      __SCREAMING_SNAKE_CASE							:     List[str]							=						seek_from
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						read_limit
      __SCREAMING_SNAKE_CASE							:     int							=						0





     def __enter__(							self			:				Optional[Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     int							=						open(self.filepath  ,	"rb"					)
      self.f.seek(self.seek_from					)
      return self





     def __len__(							self			:				int					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						os.fstat(self.f.fileno()					).st_size
      return min(self.read_limit  ,	total_length - self.seek_from					)





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				List[str]=-1					)->Optional[Any]:
      if self.n_seen >= self.read_limit:
       return b""
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.read_limit - self.n_seen
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.f.read(remaining_amount if n < 0 else min(UpperCamelCase  ,	UpperCamelCase					)					)
      self.n_seen += len(UpperCamelCase					)
      return data





     def __iter__(							self			:				Optional[Any]					)->Any:
      yield self.read(n=4 * 1_0_2_4 * 1_0_2_4					)






     def __exit__(							self			:				Tuple  ,	*UpperCamelCase			:				int					)->str:
      self.f.close()






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				List[str]  ,	UpperCamelCase			:				Optional[int]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						args






     def 						__snake_case     (							self			:				int					)->int:
      # Immediately after invoking a custom transfer process, git-lfs
      # sends initiation data to the process over stdin.
      # This tells the process useful information about the configuration.
      __SCREAMING_SNAKE_CASE							:     Optional[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:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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					)

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

       __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
       for i, presigned_url in enumerate(UpperCamelCase					):
        with FileSlice(UpperCamelCase  ,	seek_from=i * chunk_size  ,	read_limit=UpperCamelCase					) as data:
         __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.put(UpperCamelCase  ,	data=UpperCamelCase					)
         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.

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.post(
           UpperCamelCase  ,	json={
               "oid": oid,
               "parts": parts,
           }  ,	)
       r.raise_for_status()

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














'''simple docstring'''

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

import numpy as np
import torch

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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 				__magic_name__  (						UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   torch.FloatTensor
	SCREAMING_SNAKE_CASE_       :   Optional[torch.FloatTensor]   =	None






def 						_UpperCAmelCase     (	a		:						Any    ,      a		:						Dict=0.9_99    ,      a		:						Optional[int]="cosine"    ,      )      ->      Tuple:





						"""simple docstring"""
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(a		:						Optional[int]				):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(a		:						Optional[Any]				):
																		return math.exp(t * -12.0				)

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

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






class 				__magic_name__  (						UpperCAmelCase_,    UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   Union[str, Any]   =	[e.name for e in KarrasDiffusionSchedulers]
	SCREAMING_SNAKE_CASE_       :   Tuple   =	1
	@register_to_config
	def __init__(				self	,   _lowercase = 1000	,   _lowercase = 0.00_01	,   _lowercase = 0.02	,   _lowercase = "linear"	,   _lowercase = None	,   _lowercase = "epsilon"	,   _lowercase = "linspace"	,   _lowercase = 0	,   )    ->	List[str]:
							if trained_betas is not None:
													lowercase_   : Any   	=      torch.tensor(_lowercase	,   dtype=torch.floataa							)
							elif beta_schedule == "linear":
													lowercase_   : Optional[int]   	=      torch.linspace(_lowercase	,   _lowercase	,   _lowercase	,   dtype=torch.floataa							)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													lowercase_   : Dict   	=      (
													    torch.linspace(beta_start**0.5	,   beta_end**0.5	,   _lowercase	,   dtype=torch.floataa							) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													lowercase_   : Any   	=      betas_for_alpha_bar(_lowercase							)
							else:
													raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"							)

							lowercase_   : Dict   	=      1.0 - self.betas
							lowercase_   : int   	=      torch.cumprod(self.alphas	,   dim=0							)

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

							# setable values
							lowercase_   : Union[str, Any]   	=      None
							lowercase_   : Optional[Any]   	=      np.linspace(0	,   num_train_timesteps - 1	,   _lowercase	,   dtype=_lowercase							)[::-1].copy()
							lowercase_   : Dict   	=      torch.from_numpy(_lowercase							)
							lowercase_   : int   	=      False
	@property
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase							)    ->	torch.FloatTensor:
							if isinstance(_lowercase	,   torch.Tensor							):
													lowercase_   : List[str]   	=      timestep.to(self.timesteps.device							)
							lowercase_   : List[str]   	=      (self.timesteps == timestep).nonzero().item()
							lowercase_   : Any   	=      self.sigmas[step_index]
							lowercase_   : Tuple   	=      sample / ((sigma**2 + 1) ** 0.5)
							lowercase_   : List[str]   	=      True
							return sample
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	Tuple:
							lowercase_   : Any   	=      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":
													lowercase_   : Tuple   	=      np.linspace(0	,   self.config.num_train_timesteps - 1	,   _lowercase	,   dtype=_lowercase							)[
													    ::-1
													].copy()
							elif self.config.timestep_spacing == "leading":
													lowercase_   : 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
													lowercase_   : Optional[int]   	=      (np.arange(0	,   _lowercase							) * step_ratio).round()[::-1].copy().astype(_lowercase							)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													lowercase_   : Optional[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
													lowercase_   : Dict   	=      (np.arange(self.config.num_train_timesteps	,   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'."							)

							lowercase_   : str   	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
							lowercase_   : Dict   	=      np.interp(_lowercase	,   np.arange(0	,   len(_lowercase							)							)	,   _lowercase							)
							lowercase_   : Any   	=      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
							lowercase_   : Dict   	=      torch.from_numpy(_lowercase							).to(device=_lowercase							)
							if str(_lowercase							).startswith('mps'							):
													# mps does not support float64
													lowercase_   : Union[str, Any]   	=      torch.from_numpy(_lowercase							).to(_lowercase	,   dtype=torch.floataa							)
							else:
													lowercase_   : Optional[int]   	=      torch.from_numpy(_lowercase							).to(device=_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   _lowercase = None	,   _lowercase = True	,   )    ->	Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

							if (
							    isinstance(_lowercase	,   _lowercase							)
							    or isinstance(_lowercase	,   torch.IntTensor							)
							    or isinstance(_lowercase	,   torch.LongTensor							)
							):
													raise ValueError(
													    (
													        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
													        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
													        ' one of the `scheduler.timesteps` as a timestep.'
													    )	,   )

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

							if isinstance(_lowercase	,   torch.Tensor							):
													lowercase_   : Optional[int]   	=      timestep.to(self.timesteps.device							)

							lowercase_   : List[Any]   	=      (self.timesteps == timestep).nonzero().item()
							lowercase_   : List[str]   	=      self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													lowercase_   : List[str]   	=      sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													lowercase_   : int   	=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`"							)

							lowercase_   : Tuple   	=      self.sigmas[step_index]
							lowercase_   : str   	=      self.sigmas[step_index + 1]
							lowercase_   : Dict   	=      (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							lowercase_   : Optional[Any]   	=      (sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							lowercase_   : Any   	=      (sample - pred_original_sample) / sigma

							lowercase_   : Tuple   	=      sigma_down - sigma

							lowercase_   : str   	=      sample + derivative * dt

							lowercase_   : Optional[int]   	=      model_output.device
							lowercase_   : Union[str, Any]   	=      randn_tensor(model_output.shape	,   dtype=model_output.dtype	,   device=_lowercase	,   generator=_lowercase							)

							lowercase_   : int   	=      prev_sample + noise * sigma_up

							if not return_dict:
													return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=_lowercase	,   pred_original_sample=_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase	,   _lowercase	,   )    ->	torch.FloatTensor:
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							lowercase_   : List[str]   	=      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
													lowercase_   : Dict   	=      self.timesteps.to(original_samples.device	,   dtype=torch.floataa							)
													lowercase_   : List[str]   	=      timesteps.to(original_samples.device	,   dtype=torch.floataa							)
							else:
													lowercase_   : List[Any]   	=      self.timesteps.to(original_samples.device							)
													lowercase_   : List[Any]   	=      timesteps.to(original_samples.device							)

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

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

							lowercase_   : str   	=      original_samples + noise * sigma
							return noisy_samples







	def __len__(				self							)    ->	Dict:
							return self.config.num_train_timesteps
