





from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_UpperCamelCase							=      'Create a default config file for Accelerate with only a few flags set.'



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]="no"    ,						SCREAMING_SNAKE_CASE       :							str = default_json_config_file    ,						SCREAMING_SNAKE_CASE       :							bool = False   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =Path(SCREAMING_SNAKE_CASE   )
							path.parent.mkdir(parents=SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
							if path.exists():
														print(
														    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'   )
														return False
							__lowerCamelCase      :      Any         =mixed_precision.lower()
							if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
														raise ValueError(
														    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'   )
							__lowerCamelCase      :      Optional[int]         ={
							    '''compute_environment''': '''LOCAL_MACHINE''',
							    '''mixed_precision''': mixed_precision,
							}
							if torch.cuda.is_available():
														__lowerCamelCase      :      List[Any]         =torch.cuda.device_count()
														__lowerCamelCase      :      Any         =num_gpus
														__lowerCamelCase      :      Any         =False
														if num_gpus > 1:
																					__lowerCamelCase      :      List[Any]         ='''MULTI_GPU'''
														else:
																					__lowerCamelCase      :      Any         ='''NO'''
							elif is_xpu_available() and use_xpu:
														__lowerCamelCase      :      Optional[Any]         =torch.xpu.device_count()
														__lowerCamelCase      :      str         =num_xpus
														__lowerCamelCase      :      List[str]         =False
														if num_xpus > 1:
																					__lowerCamelCase      :      Any         ='''MULTI_XPU'''
														else:
																					__lowerCamelCase      :      List[Any]         ='''NO'''
							elif is_npu_available():
														__lowerCamelCase      :      Tuple         =torch.npu.device_count()
														__lowerCamelCase      :      Union[str, Any]         =num_npus
														__lowerCamelCase      :      List[str]         =False
														if num_npus > 1:
																					__lowerCamelCase      :      int         ='''MULTI_NPU'''
														else:
																					__lowerCamelCase      :      Tuple         ='''NO'''
							else:
														__lowerCamelCase      :      Optional[Any]         =0
														__lowerCamelCase      :      int         =True
														__lowerCamelCase      :      Optional[int]         =1
														__lowerCamelCase      :      Any         ='''NO'''
							__lowerCamelCase      :      Optional[int]         =ClusterConfig(**SCREAMING_SNAKE_CASE   )
							config.to_json_file(SCREAMING_SNAKE_CASE   )
							return path



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =parser.add_parser('''default'''    ,						parents=SCREAMING_SNAKE_CASE    ,						help=SCREAMING_SNAKE_CASE    ,						formatter_class=SCREAMING_SNAKE_CASE   )
							parser.add_argument(
							    '''--config_file'''    ,						default=SCREAMING_SNAKE_CASE    ,						help=(
							        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
							        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
							        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
							        '''with \'huggingface\'.'''
							    )    ,						dest='''save_location'''    ,						)

							parser.add_argument(
							    '''--mixed_precision'''    ,						choices=['''no''', '''fp16''', '''bf16''']    ,						type=SCREAMING_SNAKE_CASE    ,						help='''Whether or not to use mixed precision training. '''
							    '''Choose between FP16 and BF16 (bfloat16) training. '''
							    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''    ,						default='''no'''    ,						)
							parser.set_defaults(func=SCREAMING_SNAKE_CASE   )
							return parser



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =write_basic_config(args.mixed_precision    ,						args.save_location   )
							if config_file:
														print(F'accelerate configuration saved at {config_file}'   )











"""simple docstring"""





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							try:
														with open(SCREAMING_SNAKE_CASE    ,						'''rb'''   ) as flax_state_f:
																					__lowerCamelCase      :      int         =from_bytes(SCREAMING_SNAKE_CASE    ,						flax_state_f.read()   )
							except UnpicklingError as e:
														try:
																					with open(SCREAMING_SNAKE_CASE   ) as f:
																												if f.read().startswith('''version'''   ):
																																			raise OSError(
																																			    '''You seem to have cloned a repository without having git-lfs installed. Please'''
																																			    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
																																			    ''' folder you cloned.'''   )
																												else:
																																			raise ValueError from e
														except (UnicodeDecodeError, ValueError):
																					raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '   )

							return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
														    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
														    ''' instructions.'''   )
														raise

							# check if we have bf16 weights
							__lowerCamelCase      :      str         =flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE   : x.dtype == jnp.bfloataa    ,						SCREAMING_SNAKE_CASE   )   ).values()
							if any(SCREAMING_SNAKE_CASE   ):
														# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

														# and bf16 is not fully supported in PT yet.
														logger.warning(
														    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
														    '''before loading those in PyTorch model.'''   )
														__lowerCamelCase      :      List[str]         =jax.tree_util.tree_map(
														    lambda SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =''''''

							__lowerCamelCase      :      List[str]         =flatten_dict(SCREAMING_SNAKE_CASE    ,						sep='''.'''   )
							__lowerCamelCase      :      List[str]         =pt_model.state_dict()

							# keep track of unexpected & missing keys
							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      int         =set(pt_model_dict.keys()   )

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														__lowerCamelCase      :      Tuple         =flax_key_tuple.split('''.'''   )

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					__lowerCamelCase      :      Optional[int]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Union[str, Any]         =jnp.transpose(SCREAMING_SNAKE_CASE    ,						(3, 2, 0, 1)   )
														elif flax_key_tuple_array[-1] == "kernel":
																					__lowerCamelCase      :      Optional[Any]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Dict         =flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					__lowerCamelCase      :      str         =flax_key_tuple_array[:-1] + ['''weight''']

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE   ):
																												__lowerCamelCase      :      Union[str, Any]         =(
																												    flax_key_tuple_string.replace('''_0'''    ,						'''.0'''   )
																												    .replace('''_1'''    ,						'''.1'''   )
																												    .replace('''_2'''    ,						'''.2'''   )
																												    .replace('''_3'''    ,						'''.3'''   )
																												    .replace('''_4'''    ,						'''.4'''   )
																												    .replace('''_5'''    ,						'''.5'''   )
																												    .replace('''_6'''    ,						'''.6'''   )
																												    .replace('''_7'''    ,						'''.7'''   )
																												    .replace('''_8'''    ,						'''.8'''   )
																												    .replace('''_9'''    ,						'''.9'''   )
																												)

														__lowerCamelCase      :      Optional[int]         ='''.'''.join(SCREAMING_SNAKE_CASE   )

														if flax_key in pt_model_dict:
																					if flax_tensor.shape != pt_model_dict[flax_key].shape:
																												raise ValueError(
																												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'   )
																					else:
																												# add weight to pytorch dict
																												__lowerCamelCase      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   ) if not isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ) else flax_tensor
																												__lowerCamelCase      :      Any         =torch.from_numpy(SCREAMING_SNAKE_CASE   )
																												# remove from missing keys
																												missing_keys.remove(SCREAMING_SNAKE_CASE   )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(SCREAMING_SNAKE_CASE   )

							pt_model.load_state_dict(SCREAMING_SNAKE_CASE   )

							# re-transform missing_keys to list
							__lowerCamelCase      :      Optional[Any]         =list(SCREAMING_SNAKE_CASE   )

							if len(SCREAMING_SNAKE_CASE   ) > 0:
														logger.warning(
														    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
														    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
														    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
														    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
														    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
														    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
														    ''' FlaxBertForSequenceClassification model).'''   )
							if len(SCREAMING_SNAKE_CASE   ) > 0:
														logger.warning(
														    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
														    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
														    ''' use it for predictions and inference.'''   )

							return pt_model




