





'''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       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					@staticmethod
					def lowerCamelCase_	(		lowerCamelCase_							:	ArgumentParser		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	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_		)		)

							SCREAMING_SNAKE_CASE				:							str						=	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       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	List[Any]     ,		lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	args



					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							warnings.warn(
							    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""		)
							SCREAMING_SNAKE_CASE				:							str						=	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						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	json.dumps(lowerCamelCase_					) + """\n"""
		sys.stdout.write(lowerCamelCase_					)
		sys.stdout.flush()



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							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       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	List[str]     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	filepath
							SCREAMING_SNAKE_CASE				:							List[str]						=	seek_from
							SCREAMING_SNAKE_CASE				:							List[Any]						=	read_limit
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	0


					def __enter__(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	open(self.filepath     ,		"""rb"""		)
							self.f.seek(self.seek_from		)
							return self


					def __len__(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	os.fstat(self.f.fileno()		).st_size
							return min(self.read_limit     ,		total_length - self.seek_from		)


					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	Optional[int]=-1		):

							'''simple docstring'''



							if self.n_seen >= self.read_limit:
									return b""
							SCREAMING_SNAKE_CASE				:							Any						=	self.read_limit - self.n_seen
							SCREAMING_SNAKE_CASE				:							Tuple						=	self.f.read(remaining_amount if n < 0 else min(lowerCamelCase_     ,		lowerCamelCase_		)		)
							self.n_seen += len(lowerCamelCase_		)
							return data


					def __iter__(		self							:	Optional[int]		):

							'''simple docstring'''



							yield self.read(n=4 * 10_24 * 10_24		)



					def __exit__(		self							:	int     ,		*lowerCamelCase_							:	Union[str, Any]		):

							'''simple docstring'''



							self.f.close()







class       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	List[Any]     ,		lowerCamelCase_							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	args



					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	json.loads(sys.stdin.readline().strip()		)
							if not (init_msg.get("""event"""		) == "init" and init_msg.get("""operation"""		) == "upload"):
									write_msg({"""error""": {"""code""": 32, """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				:							str						=	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				:							Tuple						=	msg["""oid"""]
									SCREAMING_SNAKE_CASE				:							Tuple						=	msg["""path"""]
									SCREAMING_SNAKE_CASE				:							List[Any]						=	msg["""action"""]["""href"""]
									SCREAMING_SNAKE_CASE				:							str						=	msg["""action"""]["""header"""]
									SCREAMING_SNAKE_CASE				:							List[Any]						=	int(header.pop("""chunk_size"""		)		)
									SCREAMING_SNAKE_CASE				:							List[str]						=	list(header.values()		)

									SCREAMING_SNAKE_CASE				:							Dict						=	[]
									for i, presigned_url in enumerate(lowerCamelCase_		):
											with FileSlice(lowerCamelCase_     ,		seek_from=i * chunk_size     ,		read_limit=lowerCamelCase_		) as data:
													SCREAMING_SNAKE_CASE				:							Tuple						=	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.

									SCREAMING_SNAKE_CASE				:							List[Any]						=	requests.post(
									    lowerCamelCase_     ,		json={
									        """oid""": oid,
									        """parts""": parts,
									    }     ,		)
									r.raise_for_status()

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










'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					SCREAMING_SNAKE_CASE__						=					(ScoreSdeVeScheduler,)
					SCREAMING_SNAKE_CASE__						=					()


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	4
							SCREAMING_SNAKE_CASE				:							int						=	3
							SCREAMING_SNAKE_CASE				:							Any						=	8
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	8

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.rand((batch_size, num_channels, height, width)		)

							return sample


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	4
							SCREAMING_SNAKE_CASE				:							Tuple						=	3
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	8
							SCREAMING_SNAKE_CASE				:							List[str]						=	8

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	batch_size * num_channels * height * width
							SCREAMING_SNAKE_CASE				:							Dict						=	torch.arange(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	sample.reshape(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	sample / num_elems
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	sample.permute(3     ,		0     ,		1     ,		2		)

							return sample


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							def model(lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int     ,		*lowerCamelCase_							:	int		):
									return sample * t / (t + 1)

							return model


					def lowerCamelCase_	(		self							:	str     ,		**lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
							    """num_train_timesteps""": 20_00,
							    """snr""": 0.15,
							    """sigma_min""": 0.01,
							    """sigma_max""": 13_48,
							    """sampling_eps""": 1e-5,
							}

							config.update(**lowerCamelCase_		)
							return config


					def lowerCamelCase_	(		self							:	List[str]     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							int						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							int						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)
							kwargs.update(lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							List[str]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Tuple						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							int						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

									SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							for timesteps in [10, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]     ,		[1, 1_00, 10_00]		):
									self.check_over_configs(sigma_min=lowerCamelCase_     ,		sigma_max=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							for t in [0.1, 0.5, 0.75]:
									self.check_over_forward(time_step=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							str						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Dict						=	self.get_scheduler_config()
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler_class(**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	3

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.dummy_model()
							SCREAMING_SNAKE_CASE				:							Dict						=	self.dummy_sample_deter

							scheduler.set_sigmas(lowerCamelCase_		)
							scheduler.set_timesteps(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.manual_seed(0		)

							for i, t in enumerate(scheduler.timesteps		):
									SCREAMING_SNAKE_CASE				:							int						=	scheduler.sigmas[i]

									for _ in range(scheduler.config.correct_steps		):
											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		).prev_sample

									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_     ,		lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							List[Any]						=	scheduler.step_pred(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	output.prev_sample, output.prev_sample_mean

							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert np.isclose(result_sum.item()     ,		14_372_758_528.0		)
							assert np.isclose(result_mean.item()     ,		18_714_530.0		)



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							Any						=	kwargs.pop("""num_inference_steps"""     ,		lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							List[str]						=	0.1 * sample

									if num_inference_steps is not None and hasattr(lowerCamelCase_     ,		"""set_timesteps"""		):
											scheduler.set_timesteps(lowerCamelCase_		)
									elif num_inference_steps is not None and not hasattr(lowerCamelCase_     ,		"""set_timesteps"""		):
											SCREAMING_SNAKE_CASE				:							Dict						=	num_inference_steps

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler.step_pred(lowerCamelCase_     ,		0     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler.step_pred(lowerCamelCase_     ,		1     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

									self.assertEqual(output_a.shape     ,		sample.shape		)
									self.assertEqual(output_a.shape     ,		output_a.shape		)



