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

import numpy as np
import torch

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







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   torch.FloatTensor
 __lowerCamelCase    :   torch.FloatTensor





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=0.999		,					__lowerCAmelCase="cosine"		,					)->  str:






      '''simple docstring'''





      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

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







class 			__snake_case   (     lowerCamelCase__					,   lowerCamelCase__   ):
 __lowerCamelCase    :   List[str]     =      1
 @register_to_config
 def __init__(  self		,   snake_case__ = 1000		,   snake_case__ = 0.0001		,   snake_case__ = 0.02		,   snake_case__ = "linear"		,   snake_case__ = 0.0		,   snake_case__ = None		,   snake_case__ = True		,   )	->							str:
       '''simple docstring'''





       if trained_betas is not None:
             UpperCAmelCase    : Any      =torch.from_numpy(snake_case__						)
       elif beta_schedule == "linear":
             UpperCAmelCase    : Any      =torch.linspace(snake_case__		,   snake_case__		,   snake_case__		,   dtype=torch.floataa						)
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCAmelCase    : Dict      =(
                 torch.linspace(beta_start**0.5		,   beta_end**0.5		,   snake_case__		,   dtype=torch.floataa						) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCAmelCase    : List[str]      =betas_for_alpha_bar(snake_case__						)
       elif beta_schedule == "sigmoid":
             # GeoDiff sigmoid schedule
             UpperCAmelCase    : Tuple      =torch.linspace(-6		,   6		,   snake_case__						)
             UpperCAmelCase    : Dict      =torch.sigmoid(snake_case__						) * (beta_end - beta_start) + beta_start
       else:
             raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''						)

       UpperCAmelCase    : int      =1.0 - self.betas
       UpperCAmelCase    : Tuple      =torch.cumprod(self.alphas		,   dim=0						)
       UpperCAmelCase    : List[str]      =torch.tensor(1.0						)

       UpperCAmelCase    : Optional[int]      =torch.tensor(1.0						)

       # standard deviation of the initial noise distribution
       UpperCAmelCase    : Any      =1.0

       # setable values
       UpperCAmelCase    : List[Any]      =None
       UpperCAmelCase    : Any      =torch.from_numpy(np.arange(0		,   snake_case__						)[::-1].copy()						)

       UpperCAmelCase    : Optional[Any]      =eta
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__ = None						)	->							torch.FloatTensor:
       '''simple docstring'''





       return sample
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__ = 10		,   snake_case__ = 10		,   snake_case__ = None		,   )	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =min(self.config.num_train_timesteps		,   snake_case__						)
       UpperCAmelCase    : int      =num_inference_steps

       UpperCAmelCase    : str      =[]

       UpperCAmelCase    : Dict      ={}
       for j in range(0		,   num_inference_steps - jump_length		,   snake_case__						):
             UpperCAmelCase    : List[str]      =jump_n_sample - 1

       UpperCAmelCase    : Optional[int]      =num_inference_steps
       while t >= 1:
             UpperCAmelCase    : Any      =t - 1
             timesteps.append(snake_case__						)

             if jumps.get(snake_case__		,   0						) > 0:
                   UpperCAmelCase    : Optional[Any]      =jumps[t] - 1
                   for _ in range(snake_case__						):
                         UpperCAmelCase    : Optional[Any]      =t + 1
                         timesteps.append(snake_case__						)

       UpperCAmelCase    : Optional[int]      =np.array(snake_case__						) * (self.config.num_train_timesteps // self.num_inference_steps)
       UpperCAmelCase    : Optional[int]      =torch.from_numpy(snake_case__						).to(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : str      =t - self.config.num_train_timesteps // self.num_inference_steps

       UpperCAmelCase    : Union[str, Any]      =self.alphas_cumprod[t]
       UpperCAmelCase    : int      =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
       UpperCAmelCase    : Tuple      =1 - alpha_prod_t
       UpperCAmelCase    : Any      =1 - alpha_prod_t_prev

       # 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
       # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
       # without eta.
       # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
       UpperCAmelCase    : Tuple      =(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

       return variance
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__ = None		,   snake_case__ = True		,   )	->							Union[RePaintSchedulerOutput, Tuple]:
       '''simple docstring'''





       UpperCAmelCase    : Any      =timestep
       UpperCAmelCase    : Dict      =timestep - self.config.num_train_timesteps // self.num_inference_steps

       # 1. compute alphas, betas
       UpperCAmelCase    : Union[str, Any]      =self.alphas_cumprod[t]
       UpperCAmelCase    : int      =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
       UpperCAmelCase    : Optional[int]      =1 - alpha_prod_t

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCAmelCase    : Tuple      =(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
             UpperCAmelCase    : Optional[Any]      =torch.clamp(snake_case__		,   -1		,   1						)

       # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
       # substitute formula (7) in the algorithm coming from DDPM paper
       # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
       # DDIM schedule gives the same results as DDPM with eta = 1.0
       # Noise is being reused in 7. and 8., but no impact on quality has
       # been observed.

       # 5. Add noise
       UpperCAmelCase    : Tuple      =model_output.device
       UpperCAmelCase    : Optional[Any]      =randn_tensor(model_output.shape		,   generator=snake_case__		,   device=snake_case__		,   dtype=model_output.dtype						)
       UpperCAmelCase    : int      =self.eta * self._get_variance(snake_case__						) ** 0.5

       UpperCAmelCase    : str      =0
       if t > 0 and self.eta > 0:
             UpperCAmelCase    : str      =std_dev_t * noise

       # 6. compute "direction pointing to x_t" of formula (12)
       # from https://arxiv.org/pdf/2010.02502.pdf
       UpperCAmelCase    : Optional[int]      =(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

       # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       UpperCAmelCase    : Tuple      =alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

       # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
       UpperCAmelCase    : Optional[int]      =(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

       # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
       UpperCAmelCase    : Optional[Any]      =mask * prev_known_part + (1.0 - mask) * prev_unknown_part

       if not return_dict:
             return (
                 pred_prev_sample,
                 pred_original_sample,
             )

       return RePaintSchedulerOutput(prev_sample=snake_case__		,   pred_original_sample=snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__=None						)	->							int:
       '''simple docstring'''





       UpperCAmelCase    : str      =self.config.num_train_timesteps // self.num_inference_steps

       for i in range(snake_case__						):
             UpperCAmelCase    : Optional[int]      =self.betas[timestep + i]
             if sample.device.type == "mps":
                   # randn does not work reproducibly on mps
                   UpperCAmelCase    : Dict      =randn_tensor(sample.shape		,   dtype=sample.dtype		,   generator=snake_case__						)
                   UpperCAmelCase    : Tuple      =noise.to(sample.device						)
             else:
                   UpperCAmelCase    : List[Any]      =randn_tensor(sample.shape		,   generator=snake_case__		,   device=sample.device		,   dtype=sample.dtype						)

             # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
             UpperCAmelCase    : Dict      =(1 - beta) ** 0.5 * sample + beta**0.5 * noise

       return sample
 def 	UpperCAmelCase__       (  self		,   snake_case__		,   snake_case__		,   snake_case__		,   )	->							torch.FloatTensor:
       '''simple docstring'''





       raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''						)






 def __len__(  self						)	->							str:
       '''simple docstring'''





       return self.config.num_train_timesteps

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__snake_case			      =    os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
__snake_case			      =    os.path.join(hf_cache_home, '''accelerate''')
__snake_case			      =    os.path.join(cache_dir, '''default_config.yaml''')
__snake_case			      =    os.path.join(cache_dir, '''default_config.yaml''')

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
     __snake_case			      =    default_yaml_config_file
else:
     __snake_case			      =    default_json_config_file





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Tuple:






      '''simple docstring'''





      if config_file is not None:
            if not os.path.isfile(__lowerCAmelCase						):
                  raise FileNotFoundError(
                      f'''The passed configuration file `{config_file}` does not exist. '''
                      '''Please pass an existing file to `accelerate launch`, or use the the default one '''
                      '''created through `accelerate config` and run `accelerate launch` '''
                      '''without the `--config_file` argument.'''						)
      else:
            UpperCAmelCase    : Union[str, Any]      =default_config_file
      with open(__lowerCAmelCase		,					'''r'''		,					encoding='''utf-8'''						) as f:
            if config_file.endswith('''.json'''						):
                  if (
                      json.load(__lowerCAmelCase						).get('''compute_environment'''		,					ComputeEnvironment.LOCAL_MACHINE						)
                      == ComputeEnvironment.LOCAL_MACHINE
                  ):
                        UpperCAmelCase    : List[str]      =ClusterConfig
                  else:
                        UpperCAmelCase    : List[Any]      =SageMakerConfig
                  return config_class.from_json_file(json_file=__lowerCAmelCase						)
            else:
                  if (
                      yaml.safe_load(__lowerCAmelCase						).get('''compute_environment'''		,					ComputeEnvironment.LOCAL_MACHINE						)
                      == ComputeEnvironment.LOCAL_MACHINE
                  ):
                        UpperCAmelCase    : Union[str, Any]      =ClusterConfig
                  else:
                        UpperCAmelCase    : List[Any]      =SageMakerConfig
                  return config_class.from_yaml_file(yaml_file=__lowerCAmelCase						)







@dataclass
class 			__snake_case   :
 __lowerCamelCase    :   ComputeEnvironment
 __lowerCamelCase    :   Union[DistributedType, SageMakerDistributedType]
 __lowerCamelCase    :   str
 __lowerCamelCase    :   bool
 def 	UpperCAmelCase__       (  self						)	->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Union[str, Any]      =self.__dict__
       # For serialization, it's best to convert Enums to strings (or their underlying value type).
       for key, value in result.items():
             if isinstance(snake_case__		,   snake_case__						):
                   UpperCAmelCase    : Any      =value.value
             if isinstance(snake_case__		,   snake_case__						) and not bool(snake_case__						):
                   UpperCAmelCase    : Union[str, Any]      =None
       UpperCAmelCase    : Dict      ={k: v for k, v in result.items() if v is not None}
       return result
 @classmethod
 def 	UpperCAmelCase__       (  cls		,   snake_case__=None						)	->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase    : Tuple      =default_json_config_file if json_file is None else json_file
       with open(snake_case__		,   '''r'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : Optional[int]      =json.load(snake_case__						)
       if "compute_environment" not in config_dict:
             UpperCAmelCase    : Optional[int]      =ComputeEnvironment.LOCAL_MACHINE
       if "mixed_precision" not in config_dict:
             UpperCAmelCase    : Optional[int]      ='''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
       if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
       if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             UpperCAmelCase    : Optional[Any]      =config_dict.pop('''dynamo_backend'''						)
             UpperCAmelCase    : List[Any]      ={} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
       if "use_cpu" not in config_dict:
             UpperCAmelCase    : Dict      =False
       return cls(**snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       with open(snake_case__		,   '''w'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : Dict      =json.dumps(self.to_dict()		,   indent=2		,   sort_keys=snake_case__						) + '''\n'''
             f.write(snake_case__						)
 @classmethod
 def 	UpperCAmelCase__       (  cls		,   snake_case__=None						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =default_yaml_config_file if yaml_file is None else yaml_file
       with open(snake_case__		,   '''r'''		,   encoding='''utf-8'''						) as f:
             UpperCAmelCase    : List[str]      =yaml.safe_load(snake_case__						)
       if "compute_environment" not in config_dict:
             UpperCAmelCase    : str      =ComputeEnvironment.LOCAL_MACHINE

       if "mixed_precision" not in config_dict:
             UpperCAmelCase    : Optional[Any]      ='''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
       if isinstance(config_dict['''mixed_precision''']		,   snake_case__						) and not config_dict["mixed_precision"]:
             UpperCAmelCase    : Any      ='''no'''
       if "fp16" in config_dict:  # Convert the config to the new format.
             del config_dict["fp16"]
       if "dynamo_backend" in config_dict:  # Convert the config to the new format.
             UpperCAmelCase    : List[str]      =config_dict.pop('''dynamo_backend'''						)
             UpperCAmelCase    : str      ={} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
       if "use_cpu" not in config_dict:
             UpperCAmelCase    : Optional[int]      =False
       return cls(**snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							List[str]:
       '''simple docstring'''





       with open(snake_case__		,   '''w'''		,   encoding='''utf-8'''						) as f:
             yaml.safe_dump(self.to_dict()		,   snake_case__						)






 def 	UpperCAmelCase__       (  self						)	->							int:
       '''simple docstring'''





       if isinstance(self.compute_environment		,   snake_case__						):
             UpperCAmelCase    : Any      =ComputeEnvironment(self.compute_environment						)
       if isinstance(self.distributed_type		,   snake_case__						):
             if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                   UpperCAmelCase    : int      =SageMakerDistributedType(self.distributed_type						)
             else:
                   UpperCAmelCase    : int      =DistributedType(self.distributed_type						)
       if self.dynamo_config is None:
             UpperCAmelCase    : Optional[int]      ={}







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   int
 __lowerCamelCase    :   int     =      0
 __lowerCamelCase    :   int     =      1
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[int]     =      None
 __lowerCamelCase    :   Optional[str]     =      "static"
 __lowerCamelCase    :   Optional[bool]     =      False
 __lowerCamelCase    :   str     =      "main"

 # args for deepspeed_plugin
 __lowerCamelCase    :   dict     =      None
 # args for fsdp
 __lowerCamelCase    :   dict     =      None
 # args for megatron_lm
 __lowerCamelCase    :   dict     =      None
 # args for ipex
 __lowerCamelCase    :   dict     =      None
 # args for TPU
 __lowerCamelCase    :   bool     =      False

 # args for TPU pods
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   bool     =      False
 __lowerCamelCase    :   bool     =      False
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   List[str]     =      None
 __lowerCamelCase    :   List[str]     =      None
 __lowerCamelCase    :   List[str]     =      None

 # args for dynamo
 __lowerCamelCase    :   dict     =      None
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       if self.deepspeed_config is None:
             UpperCAmelCase    : Union[str, Any]      ={}
       if self.fsdp_config is None:
             UpperCAmelCase    : Any      ={}
       if self.megatron_lm_config is None:
             UpperCAmelCase    : List[Any]      ={}
       if self.ipex_config is None:
             UpperCAmelCase    : int      ={}
       return super().__post_init__()







@dataclass
class 			__snake_case   (     lowerCamelCase__   ):
 __lowerCamelCase    :   str
 __lowerCamelCase    :   str
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   Optional[str]     =      None
 __lowerCamelCase    :   str     =      "us-east-1"
 __lowerCamelCase    :   int     =      1
 __lowerCamelCase    :   str     =      "all"
 __lowerCamelCase    :   str     =      F'accelerate-sagemaker-{num_machines}'
 __lowerCamelCase    :   str     =      SAGEMAKER_PYTORCH_VERSION
 __lowerCamelCase    :   str     =      SAGEMAKER_TRANSFORMERS_VERSION
 __lowerCamelCase    :   str     =      SAGEMAKER_PYTHON_VERSION
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   str     =      None
 __lowerCamelCase    :   dict     =      None
 __lowerCamelCase    :   dict     =      None
