




'''simple docstring'''


import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class      A       :







							def __init__(    self			:    Tuple				,     lowerCAmelCase_			:    str   )     ->				Optional[Any]:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																					# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																					# modified it, it will not be accepted here again, since `auto` values would have been overridden
																					_a      	=							deepcopy(lowerCAmelCase_   )
														elif os.path.exists(lowerCAmelCase_   ):
																					with io.open(lowerCAmelCase_				,     '''r'''				,     encoding='''utf-8'''   ) as f:
																												_a      	=							json.load(lowerCAmelCase_   )
														else:
																					try:
																												_a      	=							baseaa.urlsafe_baadecode(lowerCAmelCase_   ).decode('''utf-8'''   )
																												_a      	=							json.loads(lowerCAmelCase_   )
																					except (UnicodeDecodeError, AttributeError, ValueError):
																												raise ValueError(
																												    F'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'   )

														_a      	=							config

														self.set_stage_and_offload()







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.get_value('''zero_optimization.stage'''				,     -1   )

														# offload
														_a      	=							False
														if self.is_zeroa() or self.is_zeroa():
																					_a      	=							set(['''cpu''', '''nvme''']   )
																					_a      	=							set(
																					    [
																					        self.get_value('''zero_optimization.offload_optimizer.device'''   ),
																					        self.get_value('''zero_optimization.offload_param.device'''   ),
																					    ]   )
																					if len(offload_devices & offload_devices_valid   ) > 0:
																												_a      	=							True







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Any   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.config

														# find the config node of interest if it exists
														_a      	=							ds_key_long.split('''.'''   )
														_a      	=							nodes.pop()
														for node in nodes:
																					_a      	=							config.get(lowerCAmelCase_   )
																					if config is None:
																												return None, ds_key

														return config, ds_key







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Optional[Any]=None   )     ->				Optional[Any]:


														"""simple docstring"""


														_a	,				_a      	=							self.find_config_node(lowerCAmelCase_   )
														if config is None:
																					return default
														return config.get(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    str=False   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							self.config

														# find the config node of interest if it exists
														_a      	=							ds_key_long.split('''.'''   )
														for node in nodes:
																					_a      	=							config
																					_a      	=							config.get(lowerCAmelCase_   )
																					if config is None:
																												if must_exist:
																																			raise ValueError(F'Can\'t find {ds_key_long} entry in the config: {self.config}'   )
																												else:
																																			return

        # if found remove it
														if parent_config is not None:
																					parent_config.pop(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    List[str]   )     ->				str:


														"""simple docstring"""


														_a      	=							self.get_value(lowerCAmelCase_   )
														return False if value is None else bool(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    Tuple   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.get_value(lowerCAmelCase_   )
														return False if value is None else not bool(lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Union[str, Any]:


														"""simple docstring"""


														return self._stage == 2







							def   __lowerCAmelCase     (    self			:    Any   )     ->				str:


														"""simple docstring"""


														return self._stage == 3







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														return self._offload



class      A       :







							def __init__(    self			:    int				,     lowerCAmelCase_			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							engine







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    List[Any]				,     **lowerCAmelCase_			:    str   )     ->				List[str]:


														"""simple docstring"""


														self.engine.backward(lowerCAmelCase_				,     **lowerCAmelCase_   )

														# Deepspeed's `engine.step` performs the following operations:
														# - gradient accumulation check
														# - gradient clipping
														# - optimizer step
														# - zero grad
														# - checking overflow
														# - lr_scheduler step (only if engine.lr_scheduler is not None)
														self.engine.step()
														# and this plugin overrides the above calls with no-ops when Accelerate runs under
														# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
														# training loop that works transparently under many training regimes.



class      A       (						_a       ):







							def __init__(    self			:    Optional[int]				,     lowerCAmelCase_			:    Any   )     ->				Union[str, Any]:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_				,     device_placement=lowerCAmelCase_				,     scaler=lowerCAmelCase_   )
														_a      	=							hasattr(self.optimizer				,     '''overflow'''   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Any=None   )     ->				Optional[int]:


														"""simple docstring"""


														pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Tuple:


														"""simple docstring"""


														pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







							@property
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														if self.__has_overflow__:
																					return self.optimizer.overflow
														return False



class      A       (						_a       ):







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Tuple   )     ->				int:


														"""simple docstring"""


														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


														pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



class      A       :







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]=0.0_0_1				,     lowerCAmelCase_			:    Optional[int]=0				,     **lowerCAmelCase_			:    Any   )     ->				str:


														"""simple docstring"""


														_a      	=							params
														_a      	=							lr
														_a      	=							weight_decay
														_a      	=							kwargs



class      A       :







							def __init__(    self			:    Any				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    int=0				,     **lowerCAmelCase_			:    Any   )     ->				Dict:


														"""simple docstring"""


														_a      	=							optimizer
														_a      	=							total_num_steps
														_a      	=							warmup_num_steps
														_a      	=							kwargs







'''simple docstring'''


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

