



'''simple docstring'''



import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:bool = False			)		->						int:
				if device_specific:
								seed += AcceleratorState().process_index
				random.seed(UpperCamelCase__			)
				np.random.seed(UpperCamelCase__			)
				torch.manual_seed(UpperCamelCase__			)
				if is_xpu_available():
								torch.xpu.manual_seed_all(UpperCamelCase__			)
				elif is_npu_available():
								torch.npu.manual_seed_all(UpperCamelCase__			)
				else:
								torch.cuda.manual_seed_all(UpperCamelCase__			)
				# ^^ safe to call this function even if cuda is not available
				if is_tpu_available():
								xm.set_rng_state(UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[RNGType] = None   ,				UpperCamelCase__				:Optional[torch.Generator] = None			)		->						Tuple:
				# Get the proper rng state
				if rng_type == RNGType.TORCH:
								snake_case__      :   List[str]      =    torch.get_rng_state()
				elif rng_type == RNGType.CUDA:
								snake_case__      :   Dict      =    torch.cuda.get_rng_state()
				elif rng_type == RNGType.XLA:
								assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
								snake_case__      :   Tuple      =    torch.tensor(xm.get_rng_state()			)
				elif rng_type == RNGType.NPU:
								assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
								snake_case__      :   Dict      =    torch.npu.get_rng_state()
				elif rng_type == RNGType.XPU:
								assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
								snake_case__      :   int      =    torch.xpu.get_rng_state()
				elif rng_type == RNGType.GENERATOR:
								assert generator is not None, "Need a generator to synchronize its seed."
								snake_case__      :   List[Any]      =    generator.get_state()

				# Broadcast the rng state from device 0 to other devices
				snake_case__      :   Tuple      =    AcceleratorState()
				if state.distributed_type == DistributedType.TPU:
								snake_case__      :   int      =    rng_state.to(xm.xla_device()			)
								xm.collective_broadcast([rng_state]			)
								xm.mark_step()
								snake_case__      :   Dict      =    rng_state.cpu()
				elif (
				    state.distributed_type in CUDA_DISTRIBUTED_TYPES
				    or state.distributed_type == DistributedType.MULTI_NPU
				    or state.distributed_type == DistributedType.MULTI_XPU
				):
								snake_case__      :   Optional[Any]      =    rng_state.to(state.device			)
								torch.distributed.broadcast(UpperCamelCase__   ,				0			)
								snake_case__      :   Dict      =    rng_state.cpu()
				elif state.distributed_type == DistributedType.MULTI_CPU:
								torch.distributed.broadcast(UpperCamelCase__   ,				0			)

				# Set the broadcast rng state
				if rng_type == RNGType.TORCH:
								torch.set_rng_state(UpperCamelCase__			)
				elif rng_type == RNGType.CUDA:
								torch.cuda.set_rng_state(UpperCamelCase__			)
				elif rng_type == RNGType.NPU:
								torch.npu.set_rng_state(UpperCamelCase__			)
				elif rng_type == RNGType.XPU:
								torch.xpu.set_rng_state(UpperCamelCase__			)
				elif rng_type == RNGType.XLA:
								xm.set_rng_state(rng_state.item()			)
				elif rng_type == RNGType.GENERATOR:
								generator.set_state(UpperCamelCase__			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Union[str, RNGType]]   ,				UpperCamelCase__				:Optional[torch.Generator] = None			)		->						Union[str, Any]:
				for rng_type in rng_types:
								synchronize_rng_state(RNGType(UpperCamelCase__			)   ,				generator=UpperCamelCase__			)










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__						=		logging.get_logger(__name__)

UpperCamelCase__						=		{}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt_decoder"""

					def __init__(							self					,			__lowercase=0					,			__lowercase=0   ):







						'''simple docstring'''




						super().__init__()
						lowerCamelCase_				:						Tuple				  =				vocab_size
						lowerCamelCase_				:						Union[str, Any]				  =				bos_token_id


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""fsmt"""
					_lowerCamelCase						    =					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(							self					,			__lowercase=["en", "de"]					,			__lowercase=42_024					,			__lowercase=42_024					,			__lowercase="relu"					,			__lowercase=1_024					,			__lowercase=200					,			__lowercase=1_024					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=4_096					,			__lowercase=12					,			__lowercase=16					,			__lowercase=0.0					,			__lowercase=0.0					,			__lowercase=0.1					,			__lowercase=0.0					,			__lowercase=0.02					,			__lowercase=2					,			__lowercase=True					,			__lowercase=True					,			__lowercase=False					,			__lowercase=5					,			__lowercase=1.0					,			__lowercase=False					,			__lowercase=True					,			__lowercase=1					,			__lowercase=0					,			__lowercase=2					,			__lowercase=2					,			**__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				langs
						lowerCamelCase_				:						Union[str, Any]				  =				src_vocab_size
						lowerCamelCase_				:						Tuple				  =				tgt_vocab_size
						lowerCamelCase_				:						List[Any]				  =				d_model  # encoder_embed_dim and decoder_embed_dim

						lowerCamelCase_				:						str				  =				encoder_ffn_dim
						lowerCamelCase_				:						str				  =				encoder_layers
						lowerCamelCase_				:						Dict				  =				encoder_attention_heads
						lowerCamelCase_				:						Any				  =				encoder_layerdrop
						lowerCamelCase_				:						Union[str, Any]				  =				decoder_layerdrop
						lowerCamelCase_				:						Optional[int]				  =				decoder_ffn_dim
						lowerCamelCase_				:						List[str]				  =				decoder_layers
						lowerCamelCase_				:						int				  =				decoder_attention_heads
						lowerCamelCase_				:						List[str]				  =				max_position_embeddings
						lowerCamelCase_				:						int				  =				init_std  # Normal(0, this parameter)
						lowerCamelCase_				:						Optional[Any]				  =				activation_function

						lowerCamelCase_				:						Union[str, Any]				  =				DecoderConfig(vocab_size=__lowercase					,			bos_token_id=__lowercase   )
						if "decoder" in common_kwargs:
							del common_kwargs["decoder"]

						lowerCamelCase_				:						int				  =				scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						lowerCamelCase_				:						Optional[int]				  =				attention_dropout
						lowerCamelCase_				:						Union[str, Any]				  =				activation_dropout
						lowerCamelCase_				:						Dict				  =				dropout

						lowerCamelCase_				:						Optional[int]				  =				use_cache
						super().__init__(
						    pad_token_id=__lowercase					,			bos_token_id=__lowercase					,			eos_token_id=__lowercase					,			decoder_start_token_id=__lowercase					,			is_encoder_decoder=__lowercase					,			tie_word_embeddings=__lowercase					,			forced_eos_token_id=__lowercase					,			max_length=__lowercase					,			num_beams=__lowercase					,			length_penalty=__lowercase					,			early_stopping=__lowercase					,			**__lowercase					,			)





					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				copy.deepcopy(self.__dict__   )
						lowerCamelCase_				:						Optional[int]				  =				self.decoder.to_dict()
						lowerCamelCase_				:						Dict				  =				self.__class__.model_type
						return output
