






"""simple docstring"""



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

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




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			snake_case__      :	str							):


        '''simple docstring'''
        if isinstance(snake_case__							,			snake_case__							):
             # 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
             UpperCAmelCase__						:    Optional[int]							=							deepcopy(snake_case__							)
        elif os.path.exists(snake_case__							):
             with io.open(snake_case__							,			"r"							,			encoding="utf-8"							) as f:
                  UpperCAmelCase__						:    List[str]							=							json.load(snake_case__							)
        else:
             try:
                  UpperCAmelCase__						:    Tuple							=							baseaa.urlsafe_baadecode(snake_case__							).decode("utf-8"							)
                  UpperCAmelCase__						:    Dict							=							json.loads(snake_case__							)
             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}'							)

        UpperCAmelCase__						:    str							=							config

        self.set_stage_and_offload()
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        UpperCAmelCase__						:    Dict							=							self.get_value("zero_optimization.stage"							,			-1							)

        # offload
        UpperCAmelCase__						:    Dict							=							False
        if self.is_zeroa() or self.is_zeroa():
             UpperCAmelCase__						:    List[Any]							=							set(["cpu", "nvme"]							)
             UpperCAmelCase__						:    Tuple							=							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:
                  UpperCAmelCase__						:    Any							=							True
   def       __a							(		self      :	Dict							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							self.config

        # find the config node of interest if it exists
        UpperCAmelCase__						:    List[Any]							=							ds_key_long.split("."							)
        UpperCAmelCase__						:    Any							=							nodes.pop()
        for node in nodes:
             UpperCAmelCase__						:    Union[str, Any]							=							config.get(snake_case__							)
             if config is None:
                  return None, ds_key

        return config, ds_key
   def       __a							(		self      :	Any							,			snake_case__      :	str							,			snake_case__      :	Tuple=None							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    int							=							self.find_config_node(snake_case__							)
        if config is None:
             return default
        return config.get(snake_case__							,			snake_case__							)
   def       __a							(		self      :	List[Any]							,			snake_case__      :	Any							,			snake_case__      :	int=False							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.config

        # find the config node of interest if it exists
        UpperCAmelCase__						:    str							=							ds_key_long.split("."							)
        for node in nodes:
             UpperCAmelCase__						:    Any							=							config
             UpperCAmelCase__						:    List[str]							=							config.get(snake_case__							)
             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(snake_case__							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							self.get_value(snake_case__							)
        return False if value is None else bool(snake_case__							)
   def       __a							(		self      :	List[Any]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							self.get_value(snake_case__							)
        return False if value is None else not bool(snake_case__							)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        return self._stage == 2
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        return self._stage == 3




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        return self._offload




class 			lowerCAmelCase__			:
   def __init__(		self      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							engine




   def       __a							(		self      :	str							,			snake_case__      :	Optional[Any]							,			**snake_case__      :	Any							):


        '''simple docstring'''
        # runs backpropagation and handles mixed precision
        self.engine.backward(snake_case__							,			**snake_case__							)

        # 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 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	str							,			snake_case__      :	Optional[int]							):


        '''simple docstring'''
        super().__init__(snake_case__							,			device_placement=snake_case__							,			scaler=snake_case__							)
        UpperCAmelCase__						:    int							=							hasattr(self.optimizer							,			"overflow"							)
   def       __a							(		self      :	List[str]							,			snake_case__      :	List[Any]=None							):


        '''simple docstring'''
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
   def       __a							(		self      :	str							):


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




   @property
   def       __a							(		self      :	int							):


        '''simple docstring'''
        if self.__has_overflow__:
             return self.optimizer.overflow
        return False




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	List[Any]							,			snake_case__      :	Optional[int]							,			snake_case__      :	Optional[int]							):


        '''simple docstring'''
        super().__init__(snake_case__							,			snake_case__							)




   def       __a							(		self      :	List[str]							):


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




class 			lowerCAmelCase__			:
   def __init__(		self      :	Union[str, Any]							,			snake_case__      :	int							,			snake_case__      :	Optional[int]=0.001							,			snake_case__      :	List[Any]=0							,			**snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							params
        UpperCAmelCase__						:    Tuple							=							lr
        UpperCAmelCase__						:    int							=							weight_decay
        UpperCAmelCase__						:    Union[str, Any]							=							kwargs




class 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							,			snake_case__      :	str							,			snake_case__      :	Optional[int]=None							,			snake_case__      :	Any=0							,			**snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							optimizer
        UpperCAmelCase__						:    Union[str, Any]							=							total_num_steps
        UpperCAmelCase__						:    Dict							=							warmup_num_steps
        UpperCAmelCase__						:    str							=							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 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


