





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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class 				_a		:
				UpperCamelCase			=					42
				UpperCamelCase			=					42

				# setable values
				UpperCamelCase			=					42
				UpperCamelCase			=					42
				UpperCamelCase			=					None






				@classmethod
				def        snake_case			(				cls	:					List[str],				lowerCAmelCase__	:					CommonSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				)			->   List[str]:




										'''simple docstring'''



										return cls(
										    common=lowerCAmelCase__,				final_alpha_cumprod=lowerCAmelCase__,				init_noise_sigma=lowerCAmelCase__,				timesteps=lowerCAmelCase__,				)




@dataclass
class 				_a		(   _lowerCAmelCase       ):
				UpperCamelCase			=					42




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

				UpperCamelCase			=					42






				@property
				def        snake_case			(				self	:					str							)			->   Optional[Any]:




										'''simple docstring'''



										return True






				@register_to_config
				def __init__(				self	:					Union[str, Any],				lowerCAmelCase__	:					int = 1_0_0_0,				lowerCAmelCase__	:					float = 0.0_001,				lowerCAmelCase__	:					float = 0.02,				lowerCAmelCase__	:					str = "linear",				lowerCAmelCase__	:					Optional[jnp.ndarray] = None,				lowerCAmelCase__	:					bool = True,				lowerCAmelCase__	:					int = 0,				lowerCAmelCase__	:					str = "epsilon",				lowerCAmelCase__	:					jnp.dtype = jnp.floataa,				)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					dtype






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					Optional[CommonSchedulerState] = None							)			->   DDIMSchedulerState:




										'''simple docstring'''



										if common is None:
																_UpperCamelCase    :      Dict						     =					CommonSchedulerState.create(self							)

										# At every step in ddim, we are looking into the previous alphas_cumprod
										# For the final step, there is no previous alphas_cumprod because we are already at 0
										# `set_alpha_to_one` decides whether we set this parameter simply to one or
										# whether we use the final alpha of the "non-previous" one.
										_UpperCamelCase    :      Dict						     =					(
										    jnp.array(1.0,				dtype=self.dtype							) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
										)

										# standard deviation of the initial noise distribution
										_UpperCamelCase    :      str						     =					jnp.array(1.0,				dtype=self.dtype							)

										_UpperCamelCase    :      Union[str, Any]						     =					jnp.arange(0,				self.config.num_train_timesteps							).round()[::-1]

										return DDIMSchedulerState.create(
										    common=lowerCAmelCase__,				final_alpha_cumprod=lowerCAmelCase__,				init_noise_sigma=lowerCAmelCase__,				timesteps=lowerCAmelCase__,				)






				def        snake_case			(				self	:					Union[str, Any],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					Optional[int] = None							)			->   jnp.ndarray:




										'''simple docstring'''



										return sample






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Tuple = ()							)			->   DDIMSchedulerState:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										_UpperCamelCase    :      Any						     =					(jnp.arange(0,				lowerCAmelCase__							) * step_ratio).round()[::-1] + self.config.steps_offset

										return state.replace(
										    num_inference_steps=lowerCAmelCase__,				timesteps=lowerCAmelCase__,				)






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Dict							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					state.common.alphas_cumprod[timestep]
										_UpperCamelCase    :      Optional[Any]						     =					jnp.where(
										    prev_timestep >= 0,				state.common.alphas_cumprod[prev_timestep],				state.final_alpha_cumprod							)
										_UpperCamelCase    :      Tuple						     =					1 - alpha_prod_t
										_UpperCamelCase    :      List[str]						     =					1 - alpha_prod_t_prev

										_UpperCamelCase    :      Optional[Any]						     =					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					float = 0.0,				lowerCAmelCase__	:					bool = True,				)			->   Union[FlaxDDIMSchedulerOutput, Tuple]:




										'''simple docstring'''



										if state.num_inference_steps is None:
																raise ValueError(
																    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''							)

										# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
										# Ideally, read DDIM paper in-detail understanding

										# Notation (<variable name> -> <name in paper>
										# - pred_noise_t -> e_theta(x_t, t)
										# - pred_original_sample -> f_theta(x_t, t) or x_0
										# - std_dev_t -> sigma_t
										# - eta -> η
										# - pred_sample_direction -> "direction pointing to x_t"
										# - pred_prev_sample -> "x_t-1"

										# 1. get previous step value (=t-1)
										_UpperCamelCase    :      Any						     =					timestep - self.config.num_train_timesteps // state.num_inference_steps

										_UpperCamelCase    :      Union[str, Any]						     =					state.common.alphas_cumprod
										_UpperCamelCase    :      Optional[Any]						     =					state.final_alpha_cumprod

										# 2. compute alphas, betas
										_UpperCamelCase    :      str						     =					alphas_cumprod[timestep]
										_UpperCamelCase    :      List[str]						     =					jnp.where(prev_timestep >= 0,				alphas_cumprod[prev_timestep],				lowerCAmelCase__							)

										_UpperCamelCase    :      List[Any]						     =					1 - alpha_prod_t

										# 3. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										if self.config.prediction_type == "epsilon":
																_UpperCamelCase    :      Tuple						     =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_UpperCamelCase    :      List[Any]						     =					model_output
										elif self.config.prediction_type == "sample":
																_UpperCamelCase    :      List[Any]						     =					model_output
																_UpperCamelCase    :      Tuple						     =					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
										elif self.config.prediction_type == "v_prediction":
																_UpperCamelCase    :      int						     =					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_UpperCamelCase    :      Dict						     =					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
										else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																    ''' `v_prediction`'''							)

										# 4. compute variance: "sigma_t(η)" -> see formula (16)
										# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
										_UpperCamelCase    :      Any						     =					self._get_variance(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)
										_UpperCamelCase    :      Union[str, Any]						     =					eta * variance ** 0.5

										# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										_UpperCamelCase    :      str						     =					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

										# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										_UpperCamelCase    :      Optional[Any]						     =					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

										if not return_dict:
																return (prev_sample, state)

										return FlaxDDIMSchedulerOutput(prev_sample=lowerCAmelCase__,				state=lowerCAmelCase__							)






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				)			->   jnp.ndarray:




										'''simple docstring'''



										return add_noise_common(state.common,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)






				def        snake_case			(				self	:					List[Any],				lowerCAmelCase__	:					DDIMSchedulerState,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				lowerCAmelCase__	:					jnp.ndarray,				)			->   jnp.ndarray:




										'''simple docstring'''



										return get_velocity_common(state.common,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__							)






				def __len__(				self	:					Union[str, 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}							)



