



import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers


def A						(       __UpperCamelCase					)						->      str:
			random.seed(__UpperCamelCase					)
			np.random.seed(__UpperCamelCase					)
			torch.manual_seed(__UpperCamelCase					)
			torch.cuda.manual_seed_all(__UpperCamelCase					)
			# ^^ safe to call this function even if cuda is not available






class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       Iterable[torch.nn.Parameter]				,					_snake_case					:       float = 0.9999				,					_snake_case					:       float = 0.0				,					_snake_case					:       int = 0				,					_snake_case					:       bool = False				,					_snake_case					:       Union[float, int] = 1.0				,					_snake_case					:       Union[float, int] = 2 / 3				,					_snake_case					:       Optional[Any] = None				,					_snake_case					:       Dict[str, Any] = None				,					**_snake_case					:       List[Any]				,					):






								"""simple docstring"""


								if isinstance(_snake_case				,					torch.nn.Module					):
											A__       =    (
											    'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
											    'Please pass the parameters of the module instead.'
											)
											deprecate(
											    'passing a `torch.nn.Module` to `ExponentialMovingAverage`'				,					'1.0.0'				,					_snake_case				,					standard_warn=_snake_case				,					)
											A__       =    parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											A__       =    True

								if kwargs.get('max_value'				,					_snake_case					) is not None:
											A__       =    'The `max_value` argument is deprecated. Please use `decay` instead.'
											deprecate('max_value'				,					'1.0.0'				,					_snake_case				,					standard_warn=_snake_case					)
											A__       =    kwargs['max_value']

								if kwargs.get('min_value'				,					_snake_case					) is not None:
											A__       =    'The `min_value` argument is deprecated. Please use `min_decay` instead.'
											deprecate('min_value'				,					'1.0.0'				,					_snake_case				,					standard_warn=_snake_case					)
											A__       =    kwargs['min_value']

								A__       =    list(_snake_case					)
								A__       =    [p.clone().detach() for p in parameters]

								if kwargs.get('device'				,					_snake_case					) is not None:
											A__       =    'The `device` argument is deprecated. Please use `to` instead.'
											deprecate('device'				,					'1.0.0'				,					_snake_case				,					standard_warn=_snake_case					)
											self.to(device=kwargs['device']					)

								A__       =    None

								A__       =    decay
								A__       =    min_decay
								A__       =    update_after_step
								A__       =    use_ema_warmup
								A__       =    inv_gamma
								A__       =    power
								A__       =    0
								A__       =    None  # set in `step()`

								A__       =    model_cls
								A__       =    model_config



					@classmethod
					def     _a							(     cls					:       Optional[Any]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       str					):






								"""simple docstring"""


								A__     , A__       =    model_cls.load_config(_snake_case				,					return_unused_kwargs=_snake_case					)
								A__       =    model_cls.from_pretrained(_snake_case					)

								A__       =    cls(model.parameters()				,					model_cls=_snake_case				,					model_config=model.config					)

								ema_model.load_state_dict(_snake_case					)
								return ema_model



					def     _a							(     self					:       str				,					_snake_case					:       Dict					):






								"""simple docstring"""


								if self.model_cls is None:
											raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.'					)

								if self.model_config is None:
											raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.'					)

								A__       =    self.model_cls.from_config(self.model_config					)
								A__       =    self.state_dict()
								state_dict.pop('shadow_params'				,					_snake_case					)

								model.register_to_config(**_snake_case					)
								self.copy_to(model.parameters()					)
								model.save_pretrained(_snake_case					)



					def     _a							(     self					:       List[Any]				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    max(0				,					optimization_step - self.update_after_step - 1					)

								if step <= 0:
											return 0.0

								if self.use_ema_warmup:
											A__       =    1 - (1 + step / self.inv_gamma) ** -self.power
								else:
											A__       =    (1 + step) / (10 + step)

								A__       =    min(_snake_case				,					self.decay					)
								# make sure decay is not smaller than min_decay
								A__       =    max(_snake_case				,					self.min_decay					)
								return cur_decay_value



					@torch.no_grad()
					def     _a							(     self					:       int				,					_snake_case					:       Iterable[torch.nn.Parameter]					):






								"""simple docstring"""


								if isinstance(_snake_case				,					torch.nn.Module					):
											A__       =    (
											    'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
											    'Please pass the parameters of the module instead.'
											)
											deprecate(
											    'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'				,					'1.0.0'				,					_snake_case				,					standard_warn=_snake_case				,					)
											A__       =    parameters.parameters()

								A__       =    list(_snake_case					)

								self.optimization_step += 1

								# Compute the decay factor for the exponential moving average.
								A__       =    self.get_decay(self.optimization_step					)
								A__       =    decay
								A__       =    1 - decay

								A__       =    contextlib.nullcontext
								if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

								for s_param, param in zip(self.shadow_params				,					_snake_case					):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
														A__       =    deepspeed.zero.GatheredParameters(_snake_case				,					modifier_rank=_snake_case					)

											with context_manager():
														if param.requires_grad:
																	s_param.sub_(one_minus_decay * (s_param - param)					)
														else:
																	s_param.copy_(_snake_case					)



					def     _a							(     self					:       Dict				,					_snake_case					:       Iterable[torch.nn.Parameter]					):






								"""simple docstring"""


								A__       =    list(_snake_case					)
								for s_param, param in zip(self.shadow_params				,					_snake_case					):
											param.data.copy_(s_param.to(param.device					).data					)



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Optional[int]=None				,					_snake_case					:       List[str]=None					):






								"""simple docstring"""


								A__       =    [
								    p.to(device=_snake_case				,					dtype=_snake_case					) if p.is_floating_point() else p.to(device=_snake_case					)
								    for p in self.shadow_params
								]



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								return {
								    "decay": self.decay,
								    "min_decay": self.min_decay,
								    "optimization_step": self.optimization_step,
								    "update_after_step": self.update_after_step,
								    "use_ema_warmup": self.use_ema_warmup,
								    "inv_gamma": self.inv_gamma,
								    "power": self.power,
								    "shadow_params": self.shadow_params,
								}



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       Iterable[torch.nn.Parameter]					):






								"""simple docstring"""


								A__       =    [param.detach().cpu().clone() for param in parameters]



					def     _a							(     self					:       Tuple				,					_snake_case					:       Iterable[torch.nn.Parameter]					):






								"""simple docstring"""


								if self.temp_stored_params is None:
											raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`'					)
								for c_param, param in zip(self.temp_stored_params				,					_snake_case					):
											param.data.copy_(c_param.data					)

								# Better memory-wise.
								A__       =    None



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       dict					):






								"""simple docstring"""


								A__       =    copy.deepcopy(_snake_case					)

								A__       =    state_dict.get('decay'				,					self.decay					)
								if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError('Decay must be between 0 and 1'					)

								A__       =    state_dict.get('min_decay'				,					self.min_decay					)
								if not isinstance(self.min_decay				,					_snake_case					):
											raise ValueError('Invalid min_decay'					)

								A__       =    state_dict.get('optimization_step'				,					self.optimization_step					)
								if not isinstance(self.optimization_step				,					_snake_case					):
											raise ValueError('Invalid optimization_step'					)

								A__       =    state_dict.get('update_after_step'				,					self.update_after_step					)
								if not isinstance(self.update_after_step				,					_snake_case					):
											raise ValueError('Invalid update_after_step'					)

								A__       =    state_dict.get('use_ema_warmup'				,					self.use_ema_warmup					)
								if not isinstance(self.use_ema_warmup				,					_snake_case					):
											raise ValueError('Invalid use_ema_warmup'					)

								A__       =    state_dict.get('inv_gamma'				,					self.inv_gamma					)
								if not isinstance(self.inv_gamma				,					(float, int)					):
											raise ValueError('Invalid inv_gamma'					)

								A__       =    state_dict.get('power'				,					self.power					)
								if not isinstance(self.power				,					(float, int)					):
											raise ValueError('Invalid power'					)

								A__       =    state_dict.get('shadow_params'				,					_snake_case					)
								if shadow_params is not None:
											A__       =    shadow_params
											if not isinstance(self.shadow_params				,					_snake_case					):
														raise ValueError('shadow_params must be a list'					)
											if not all(isinstance(_snake_case				,					torch.Tensor					) for p in self.shadow_params					):
														raise ValueError('shadow_params must all be Tensors'					)









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      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""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(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

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

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

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




