
'''simple docstring'''


import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


__lowercase							:   Dict					=     logging.get_logger(__name__)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=False				)     ->							Tuple:
					'''simple docstring'''

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a PyTorch model in Flax, 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

					if not is_sharded:
										lowerCamelCase_					:      str     =		os.path.abspath(_lowercase				)
										logger.info(F"""Loading PyTorch weights from {pt_path}"""				)

										lowerCamelCase_					:      Dict     =		torch.load(_lowercase			,       map_location='''cpu'''				)
										logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()				):,} parameters."""				)

										lowerCamelCase_					:      List[Any]     =		convert_pytorch_state_dict_to_flax(_lowercase			,       _lowercase				)
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										lowerCamelCase_					:      Optional[int]     =		convert_pytorch_sharded_state_dict_to_flax(_lowercase			,       _lowercase				)
					return flax_state_dict




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )     ->							(Tuple[str], np.ndarray):
					'''simple docstring'''

					def is_key_or_prefix_key_in_dict(_lowercase				) -> bool:
										return len(set(_lowercase				) & {key, (model_prefix,) + key}				) > 0

					# layer norm
					lowerCamelCase_					:      Union[str, Any]     =		pt_tuple_key[:-1] + ('''scale''',)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowercase				):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					lowerCamelCase_					:      Optional[Any]     =		pt_tuple_key[:-1] + ('''mean''',)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowercase				):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					lowerCamelCase_					:      Any     =		pt_tuple_key[:-1] + ('''var''',)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowercase				):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					lowerCamelCase_					:      Union[str, Any]     =		pt_tuple_key[:-1] + ('''embedding''',)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowercase				):
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					lowerCamelCase_					:      Union[str, Any]     =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowercase				):
										lowerCamelCase_					:      Union[str, Any]     =		pt_tensor.transpose(2			,       3			,       1			,       0				)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					lowerCamelCase_					:      int     =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowercase				):
										lowerCamelCase_					:      List[str]     =		pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					lowerCamelCase_					:      Optional[Any]     =		pt_tuple_key[:-1] + ('''weight''',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					lowerCamelCase_					:      Any     =		pt_tuple_key[:-1] + ('''bias''',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
					lowerCamelCase_					:      Tuple     =		None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										lowerCamelCase_					:      Optional[int]     =		pt_tuple_key[-2] + '''_g'''
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										lowerCamelCase_					:      Dict     =		pt_tuple_key[-2] + '''_v'''
					if name is not None:
										lowerCamelCase_					:      Dict     =		pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		{k: v.numpy() for k, v in pt_state_dict.items()}

					lowerCamelCase_					:      List[str]     =		flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										lowerCamelCase_					:      Any     =		flax_model.params['''params''']
					else:
										lowerCamelCase_					:      Tuple     =		flax_model.params
					lowerCamelCase_					:      int     =		flatten_dict(_lowercase				)

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										lowerCamelCase_					:      int     =		flatten_dict(flax_model.params['''batch_stats''']				)
										random_flax_state_dict.update(_lowercase				)

					lowerCamelCase_					:      Any     =		{}

					lowerCamelCase_					:      Any     =		(model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('''.'''				)[0] for k in pt_state_dict.keys()}
					)
					lowerCamelCase_					:      int     =		(model_prefix in flax_model_params) and (
					    model_prefix not in {k.split('''.'''				)[0] for k in pt_state_dict.keys()}
					)

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										lowerCamelCase_					:      int     =		tuple(pt_key.split('''.'''				)				)

										# remove base model prefix if necessary
										lowerCamelCase_					:      Optional[int]     =		pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															lowerCamelCase_					:      List[str]     =		pt_tuple_key[1:]

										# Correctly rename weight parameters
										lowerCamelCase_,      lowerCamelCase_					:      str     =		rename_key_and_reshape_tensor(
										    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

										# add model prefix if necessary
										lowerCamelCase_					:      Tuple     =		(model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															lowerCamelCase_					:      Optional[int]     =		(model_prefix,) + flax_key

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																				    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""				)

        # add batch stats if the model contains batchnorm layers
										if "batch_stats" in flax_model.params:
															if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																				lowerCamelCase_					:      Any     =		jnp.asarray(_lowercase				)
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(_lowercase			,       _lowercase				)
																				continue

															# also add unexpected weight so that warning is thrown
															lowerCamelCase_					:      List[str]     =		jnp.asarray(_lowercase				)

										else:
															# also add unexpected weight so that warning is thrown
															lowerCamelCase_					:      Union[str, Any]     =		jnp.asarray(_lowercase				)

					return unflatten_dict(_lowercase				)




def lowercase_	(   _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					import torch

					# Load the index
					lowerCamelCase_					:      Dict     =		{}
					for shard_file in shard_filenames:
										# load using msgpack utils
										lowerCamelCase_					:      Optional[Any]     =		torch.load(_lowercase				)
										lowerCamelCase_					:      Any     =		{k: v.numpy() for k, v in pt_state_dict.items()}

										lowerCamelCase_					:      Optional[Any]     =		flax_model.base_model_prefix

										# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
										if "batch_stats" in flax_model.params:
															lowerCamelCase_					:      List[Any]     =		flax_model.params['''params''']

															lowerCamelCase_					:      Union[str, Any]     =		flatten_dict(_lowercase				)
															random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']				)				)
										else:
															lowerCamelCase_					:      List[Any]     =		flax_model.params
															lowerCamelCase_					:      Union[str, Any]     =		flatten_dict(_lowercase				)

										lowerCamelCase_					:      Tuple     =		(model_prefix not in flax_model_params) and (
										    model_prefix in {k.split('''.'''				)[0] for k in pt_state_dict.keys()}
										)
										lowerCamelCase_					:      int     =		(model_prefix in flax_model_params) and (
										    model_prefix not in {k.split('''.'''				)[0] for k in pt_state_dict.keys()}
										)
										# Need to change some parameters name to match Flax names
										for pt_key, pt_tensor in pt_state_dict.items():
															lowerCamelCase_					:      Dict     =		tuple(pt_key.split('''.'''				)				)

															# remove base model prefix if necessary
															lowerCamelCase_					:      Optional[Any]     =		pt_tuple_key[0] == model_prefix
															if load_model_with_head_into_base_model and has_base_model_prefix:
																				lowerCamelCase_					:      Dict     =		pt_tuple_key[1:]

															# Correctly rename weight parameters
															lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		rename_key_and_reshape_tensor(
															    _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
															# add model prefix if necessary
															lowerCamelCase_					:      Optional[Any]     =		(model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				lowerCamelCase_					:      Optional[int]     =		(model_prefix,) + flax_key

															if flax_key in random_flax_state_dict:
																				if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																									raise ValueError(
																									    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																									    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""				)

            # add batch stats if the model contains batchnorm layers
															if "batch_stats" in flax_model.params:
																				if "mean" in flax_key[-1]:
																									lowerCamelCase_					:      List[str]     =		jnp.asarray(_lowercase				)
																									continue
																				if "var" in flax_key[-1]:
																									lowerCamelCase_					:      Tuple     =		jnp.asarray(_lowercase				)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(_lowercase			,       _lowercase				)
																									continue

																				# also add unexpected weight so that warning is thrown
																				lowerCamelCase_					:      Optional[Any]     =		jnp.asarray(_lowercase				)

															else:
																				# also add unexpected weight so that warning is thrown
																				lowerCamelCase_					:      List[Any]     =		jnp.asarray(_lowercase				)
					return unflatten_dict(_lowercase				)




def lowercase_	(   _lowercase			,       _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		os.path.abspath(_lowercase				)
					logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""				)

					# import correct flax class
					lowerCamelCase_					:      Dict     =		getattr(_lowercase			,       '''Flax''' + model.__class__.__name__				)

					# load flax weight dict
					with open(_lowercase			,       '''rb'''				) as state_f:
										try:
															lowerCamelCase_					:      Optional[Any]     =		from_bytes(_lowercase			,       state_f.read()				)
										except UnpicklingError:
															raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """				)

					return load_flax_weights_in_pytorch_model(_lowercase			,       _lowercase				)






def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a 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_					:      List[Any]     =		flatten_dict(jax.tree_util.tree_map(lambda _lowercase				: x.dtype == jnp.bfloataa			,       _lowercase				)				).values()
					if any(_lowercase				):
										# convert all weights to fp32 if the 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_					:      Tuple     =		jax.tree_util.tree_map(
										    lambda _lowercase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params			,       _lowercase				)

					lowerCamelCase_					:      Any     =		flatten_dict(_lowercase				)
					lowerCamelCase_					:      List[Any]     =		pt_model.state_dict()

					lowerCamelCase_					:      Union[str, Any]     =		(pt_model.base_model_prefix in flax_state) and (
					    pt_model.base_model_prefix not in {k.split('''.'''				)[0] for k in pt_model_dict.keys()}
					)
					lowerCamelCase_					:      Optional[Any]     =		(pt_model.base_model_prefix not in flax_state) and (
					    pt_model.base_model_prefix in {k.split('''.'''				)[0] for k in pt_model_dict.keys()}
					)

					# keep track of unexpected & missing keys
					lowerCamelCase_					:      List[Any]     =		[]
					lowerCamelCase_					:      Optional[int]     =		set(pt_model_dict.keys()				)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										lowerCamelCase_					:      List[str]     =		flax_key_tuple[0] == pt_model.base_model_prefix
										lowerCamelCase_					:      List[str]     =		'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple				) in pt_model_dict

										# adapt flax_key to prepare for loading from/to base model only
										if load_model_with_head_into_base_model and has_base_model_prefix:
															lowerCamelCase_					:      List[str]     =		flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															lowerCamelCase_					:      Optional[Any]     =		(pt_model.base_model_prefix,) + flax_key_tuple

										# rename flax weights to PyTorch format
										if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowercase				) not in pt_model_dict:
															# conv layer
															lowerCamelCase_					:      Any     =		flax_key_tuple[:-1] + ('''weight''',)
															lowerCamelCase_					:      Union[str, Any]     =		jnp.transpose(_lowercase			,       (3, 2, 0, 1)				)
										elif flax_key_tuple[-1] == "kernel" and ".".join(_lowercase				) not in pt_model_dict:
															# linear layer
															lowerCamelCase_					:      str     =		flax_key_tuple[:-1] + ('''weight''',)
															lowerCamelCase_					:      Union[str, Any]     =		flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															lowerCamelCase_					:      int     =		flax_key_tuple[:-1] + ('''weight''',)

										# adding batch stats from flax batch norm to pt
										elif "mean" in flax_key_tuple[-1]:
															lowerCamelCase_					:      Optional[int]     =		flax_key_tuple[:-1] + ('''running_mean''',)
										elif "var" in flax_key_tuple[-1]:
															lowerCamelCase_					:      Dict     =		flax_key_tuple[:-1] + ('''running_var''',)

										if "batch_stats" in flax_state:
															lowerCamelCase_					:      List[Any]     =		'''.'''.join(flax_key_tuple[1:]				)  # Remove the params/batch_stats header
										else:
															lowerCamelCase_					:      Optional[Any]     =		'''.'''.join(_lowercase				)

										# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
										lowerCamelCase_					:      List[str]     =		{}
										# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
										for key in pt_model_dict:
															lowerCamelCase_					:      List[Any]     =		key.split('''.'''				)
															lowerCamelCase_					:      int     =		None
															if key_components[-3::2] == ["parametrizations", "original0"]:
																				lowerCamelCase_					:      Dict     =		key_components[-2] + '''_g'''
															elif key_components[-3::2] == ["parametrizations", "original1"]:
																				lowerCamelCase_					:      Any     =		key_components[-2] + '''_v'''
															if name is not None:
																				lowerCamelCase_					:      Union[str, Any]     =		key_components[:-3] + [name]
																				lowerCamelCase_					:      List[str]     =		'''.'''.join(_lowercase				)
																				lowerCamelCase_					:      Any     =		key

										if flax_key in special_pt_names:
															lowerCamelCase_					:      List[str]     =		special_pt_names[flax_key]

										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_					:      Optional[int]     =		np.asarray(_lowercase				) if not isinstance(_lowercase			,       np.ndarray				) else flax_tensor
																				lowerCamelCase_					:      Optional[Any]     =		torch.from_numpy(_lowercase				)
																				# remove from missing keys
																				missing_keys.remove(_lowercase				)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(_lowercase				)

					pt_model.load_state_dict(_lowercase				)

					# re-transform missing_keys to list
					lowerCamelCase_					:      Optional[Any]     =		list(_lowercase				)

					if len(_lowercase				) > 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).'''				)
					else:
										logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""				)
					if len(_lowercase				) > 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.'''				)
					else:
										logger.warning(
										    F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
										    '''If your task is similar to the task the model of the checkpoint was trained on, '''
										    F"""you can already use {pt_model.__class__.__name__} for predictions without further training."""				)

					return pt_model




'''simple docstring'''


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)




def lowercase_	(   _lowercase			,       _lowercase=False				)     ->							int:
					'''simple docstring'''

					try:
										lowerCamelCase_					:      Union[str, Any]     =		os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										lowerCamelCase_					:      Union[str, Any]     =		default
					else:
										# KEY is set, convert it to True or False.
										try:
															lowerCamelCase_					:      List[str]     =		strtobool(_lowercase				)
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(F"""If set, {key} must be yes or no."""				)
					return _value


__lowercase							:   Union[str, Any]					=     parse_flag_from_env('''RUN_SLOW''', default=False)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skip('''Test was skipped'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					return unittest.skipUnless(_run_slow_tests			,       '''test is slow'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

					return unittest.skipUnless(not torch.cuda.is_available()			,       '''test requires only a CPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(torch.cuda.is_available()			,       '''test requires a GPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(is_xpu_available()			,       '''test requires a XPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

					return unittest.skipUnless(is_mps_available()			,       '''test requires a `mps` backend support in `torch`'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available()			,       '''test requires the Hugging Face suite'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					return unittest.skipUnless(is_bnb_available()			,       '''test requires the bitsandbytes library'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(is_tpu_available()			,       '''test requires TPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(torch.cuda.device_count() == 1			,       '''test requires a GPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							List[str]:
					'''simple docstring'''

					return unittest.skipUnless(torch.xpu.device_count() == 1			,       '''test requires a XPU'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					return unittest.skipUnless(torch.cuda.device_count() > 1			,       '''test requires multiple GPUs'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(torch.xpu.device_count() > 1			,       '''test requires multiple XPUs'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Tuple:
					'''simple docstring'''

					return unittest.skipUnless(is_safetensors_available()			,       '''test requires safetensors'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

					return unittest.skipUnless(is_deepspeed_available()			,       '''test requires DeepSpeed'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(is_torch_version('''>='''			,       '''1.12.0'''				)			,       '''test requires torch version >= 1.12.0'''				)(_lowercase				)




def lowercase_	(   _lowercase=None			,       _lowercase=None				)     ->							Any:
					'''simple docstring'''

					if test_case is None:
										return partial(_lowercase			,       version=_lowercase				)
					return unittest.skipUnless(is_torch_version('''>='''			,       _lowercase				)			,       F"""test requires torch version >= {version}"""				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							str:
					'''simple docstring'''

					return unittest.skipUnless(is_tensorboard_available()			,       '''test requires Tensorboard'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							Union[str, Any]:
					'''simple docstring'''

					return unittest.skipUnless(is_wandb_available()			,       '''test requires wandb'''				)(_lowercase				)




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

					return unittest.skipUnless(is_comet_ml_available()			,       '''test requires comet_ml'''				)(_lowercase				)


__lowercase							:   str					=     (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					return unittest.skipUnless(
					    _atleast_one_tracker_available			,       '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''			,       )(_lowercase				)

class 	__lowercase				(  unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			True

			@classmethod
			def        UpperCAmelCase__ (cls      ):
								lowerCamelCase_					:      Optional[Any]     =		tempfile.mkdtemp()

			@classmethod
			def        UpperCAmelCase__ (cls      ):
								if os.path.exists(cls.tmpdir      ):
													shutil.rmtree(cls.tmpdir      )






			def        UpperCAmelCase__ (self      ):
								if self.clear_on_setup:
													for path in Path(self.tmpdir      ).glob('''**/*'''      ):
																		if path.is_file():
																							path.unlink()
																		elif path.is_dir():
																							shutil.rmtree(A      )

class 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self      ):
								super().tearDown()
								# Reset the state of the AcceleratorState singleton.
								AcceleratorState._reset_state()
								PartialState._reset_state()

class 	__lowercase				(  unittest.TestCase    ):

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      List[str]     =		mocks if isinstance(A			,   (tuple, list)      ) else [mocks]
								for m in self.mocks:
													m.start()
													self.addCleanup(m.stop      )




def lowercase_	(   _lowercase				)     ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		AcceleratorState()
					lowerCamelCase_					:      Optional[Any]     =		tensor[None].clone().to(state.device				)
					lowerCamelCase_					:      Dict     =		gather(_lowercase				).cpu()
					lowerCamelCase_					:      List[str]     =		tensor[0].cpu()
					for i in range(tensors.shape[0]				):
										if not torch.equal(tensors[i]			,       _lowercase				):
															return False
					return True

class 	__lowercase				:

			def __init__(self			,   A			,   A			,   A      ):
								lowerCamelCase_					:      List[Any]     =		returncode
								lowerCamelCase_					:      Optional[int]     =		stdout
								lowerCamelCase_					:      List[str]     =		stderr




async def lowercase_	(   _lowercase			,       _lowercase				)     ->							Tuple:
					'''simple docstring'''

					while True:
										lowerCamelCase_					:      Any     =		await stream.readline()
										if line:
															callback(_lowercase				)
										else:
															break




async def lowercase_	(   _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None			,       _lowercase=False			,       _lowercase=False				)     ->							_RunOutput:
					'''simple docstring'''

					if echo:
										print('''\nRunning: '''			,       ''' '''.join(_lowercase				)				)

					lowerCamelCase_					:      Any     =		await asyncio.create_subprocess_exec(
					    cmd[0]			,       *cmd[1:]			,       stdin=_lowercase			,       stdout=asyncio.subprocess.PIPE			,       stderr=asyncio.subprocess.PIPE			,       env=_lowercase			,       )

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					lowerCamelCase_					:      List[Any]     =		[]
					lowerCamelCase_					:      Optional[int]     =		[]

					def tee(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase=""				):
										lowerCamelCase_					:      Union[str, Any]     =		line.decode('''utf-8'''				).rstrip()
										sink.append(_lowercase				)
										if not quiet:
															print(_lowercase			,       _lowercase			,       file=_lowercase				)

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout			,       lambda _lowercase				: tee(_lowercase			,       _lowercase			,       sys.stdout			,       label='''stdout:'''				)				)				),
					        asyncio.create_task(_read_stream(p.stderr			,       lambda _lowercase				: tee(_lowercase			,       _lowercase			,       sys.stderr			,       label='''stderr:'''				)				)				),
					    ]			,       timeout=_lowercase			,       )
					return _RunOutput(await p.wait()			,       _lowercase			,       _lowercase				)




def lowercase_	(   _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=180			,       _lowercase=False			,       _lowercase=True				)     ->							_RunOutput:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		asyncio.get_event_loop()
					lowerCamelCase_					:      List[str]     =		loop.run_until_complete(
					    _stream_subprocess(_lowercase			,       env=_lowercase			,       stdin=_lowercase			,       timeout=_lowercase			,       quiet=_lowercase			,       echo=_lowercase				)				)

					lowerCamelCase_					:      List[Any]     =		''' '''.join(_lowercase				)
					if result.returncode > 0:
										lowerCamelCase_					:      Optional[int]     =		'''\n'''.join(result.stderr				)
										raise RuntimeError(
										    F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
										    F"""The combined stderr from workers follows:\n{stderr}"""				)

					return result

class 	__lowercase				(  _lowercase    ):
			pass




def lowercase_	(   _lowercase			,       _lowercase=False				)     ->							Dict:
					'''simple docstring'''

					try:
										lowerCamelCase_					:      List[str]     =		subprocess.check_output(_lowercase			,       stderr=subprocess.STDOUT				)
										if return_stdout:
															if hasattr(_lowercase			,       '''decode'''				):
																				lowerCamelCase_					:      str     =		output.decode('''utf-8'''				)
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    F"""Command `{" ".join(_lowercase				)}` failed with the following error:\n\n{e.output.decode()}"""				) from e


