





'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 				lowercase__   (   __lowercase						:  Union[str, Any]     ) ->      Tuple:



       """simple docstring"""






       return EnvironmentCommand()
def 				lowercase__   (   __lowercase						:  Dict     ) ->      str:



       """simple docstring"""






       return EnvironmentCommand(args.accelerate_config_file     )


class      snake_case     (    lowerCamelCase_       ):




 """simple docstring"""



 @staticmethod
 def        _lowerCamelCase					(       __A       :							List[Any]	):
        __UpperCamelCase       =  parser.add_parser('env'	)
        download_parser.set_defaults(func=__SCREAMING_SNAKE_CASE	)
        download_parser.add_argument(
            '--accelerate-config_file'							,	default=__SCREAMING_SNAKE_CASE							,	help='The accelerate config file to use for the default values in the launching script.'							,	)
        download_parser.set_defaults(func=__SCREAMING_SNAKE_CASE	)






 def __init__(       self       :							List[Any]							,	__A       :							Optional[Any]							,	*__A       :							Dict	):
        __UpperCamelCase       =  accelerate_config_file






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  '''not installed'''
        if is_safetensors_available():
               import safetensors

               __UpperCamelCase       =  safetensors.__version__
        elif importlib.util.find_spec('safetensors'	) is not None:
               import safetensors

               __UpperCamelCase       =  f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

        __UpperCamelCase       =  '''not installed'''
        __UpperCamelCase       =  '''not found'''
        if is_accelerate_available():
               import accelerate
               from accelerate.commands.config import default_config_file, load_config_from_file

               __UpperCamelCase       =  accelerate.__version__
               # Get the default from the config file.
               if self._accelerate_config_file is not None or os.path.isfile(__SCREAMING_SNAKE_CASE	):
                      __UpperCamelCase       =  load_config_from_file(self._accelerate_config_file	).to_dict()

               __UpperCamelCase       =  (
                   '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
                   if isinstance(__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE	)
                   else f'''\t{accelerate_config}'''
               )

        __UpperCamelCase       =  '''not installed'''
        __UpperCamelCase       =  '''NA'''
        if is_torch_available():
               import torch

               __UpperCamelCase       =  torch.__version__
               __UpperCamelCase       =  torch.cuda.is_available()

        __UpperCamelCase       =  '''not installed'''
        __UpperCamelCase       =  '''NA'''
        if is_tf_available():
               import tensorflow as tf

               __UpperCamelCase       =  tf.__version__
               try:
                      # deprecated in v2.1
                      __UpperCamelCase       =  tf.test.is_gpu_available()
               except AttributeError:
                      # returns list of devices, convert to bool
                      __UpperCamelCase       =  bool(tf.config.list_physical_devices('GPU'	)	)

        __UpperCamelCase       =  '''not installed'''
        __UpperCamelCase       =  '''not installed'''
        __UpperCamelCase       =  '''not installed'''
        __UpperCamelCase       =  '''NA'''
        if is_flax_available():
               import flax
               import jax
               import jaxlib

               __UpperCamelCase       =  flax.__version__
               __UpperCamelCase       =  jax.__version__
               __UpperCamelCase       =  jaxlib.__version__
               __UpperCamelCase       =  jax.lib.xla_bridge.get_backend().platform

        __UpperCamelCase       =  {
            '''`transformers` version''': version,
            '''Platform''': platform.platform(),
            '''Python version''': platform.python_version(),
            '''Huggingface_hub version''': huggingface_hub.__version__,
            '''Safetensors version''': f'''{safetensors_version}''',
            '''Accelerate version''': f'''{accelerate_version}''',
            '''Accelerate config''': f'''{accelerate_config_str}''',
            '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
            '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
            '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
            '''Jax version''': f'''{jax_version}''',
            '''JaxLib version''': f'''{jaxlib_version}''',
            '''Using GPU in script?''': '''<fill in>''',
            '''Using distributed or parallel set-up in script?''': '''<fill in>''',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'	)
        print(self.format_dict(__SCREAMING_SNAKE_CASE	)	)

        return info






 @staticmethod
 def        _lowerCamelCase					(       __A       :							Optional[Any]	):
        return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"


'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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  lowerCAmelCase__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Tuple													=       CommonSchedulerState.create(self		)

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, Any]													=       jnp.array(1.0				,							dtype=self.dtype		)

											lowercase_	:     List[Any]													=       jnp.arange(0				,							self.config.num_train_timesteps		).round()[::-1]

											return DDPMSchedulerState.create(
											    common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[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
											lowercase_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     str													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											lowercase_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowercase_						, lowercase_	:     Optional[Any]													=       jnp.split(__SCREAMING_SNAKE_CASE				,							sample.shape[1]				,							axis=1		)
											else:
																	lowercase_	:     int													=       None

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[int]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     int													=       1 - alpha_prod_t
											lowercase_	:     str													=       1 - alpha_prod_t_prev

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																	lowercase_	:     Tuple													=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											else:
																	raise ValueError(
																	    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																	    ''' for the FlaxDDPMScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	lowercase_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-1				,							1		)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[Any]													=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     Optional[Any]													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																	lowercase_	:     str													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							num=1		)
																	lowercase_	:     List[Any]													=       jax.random.normal(__SCREAMING_SNAKE_CASE				,							shape=model_output.shape				,							dtype=self.dtype		)
																	return (self._get_variance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							predicted_variance=__SCREAMING_SNAKE_CASE		) ** 0.5) * noise

											lowercase_	:     Optional[Any]													=       jnp.where(t > 0				,							random_variance()				,							jnp.zeros(model_output.shape				,							dtype=self.dtype		)		)

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



