


'''simple docstring'''
from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
		from .deepspeed import (
		    DeepSpeedEngineWrapper,
		    DeepSpeedOptimizerWrapper,
		    DeepSpeedSchedulerWrapper,
		    DummyOptim,
		    DummyScheduler,
		    HfDeepSpeedConfig,
		)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers



'''simple docstring'''
import torch
from torch import nn
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=1					,     UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :Dict							      =				n_token
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Dict							      =				d_proj

							UpperCamelCase__     :Dict							      =				cutoffs + [n_token]
							UpperCamelCase__     :Union[str, Any]							      =				[0] + self.cutoffs
							UpperCamelCase__     :Any							      =				div_val

							UpperCamelCase__     :int							      =				self.cutoffs[0]
							UpperCamelCase__     :List[Any]							      =				len(self.cutoffs							) - 1
							UpperCamelCase__     :List[Any]							      =				self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												UpperCamelCase__     :Any							      =				nn.Parameter(torch.zeros(self.n_clusters					,     self.d_embed							)							)
												UpperCamelCase__     :Optional[Any]							      =				nn.Parameter(torch.zeros(self.n_clusters							)							)

							UpperCamelCase__     :Union[str, Any]							      =				nn.ModuleList()
							UpperCamelCase__     :str							      =				nn.ParameterList()

							if div_val == 1:
												for i in range(len(self.cutoffs							)							):
																	if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)
																	else:
																						self.out_projs.append(UpperCamelCase_							)

												self.out_layers.append(nn.Linear(UpperCamelCase_					,     UpperCamelCase_							)							)
							else:
												for i in range(len(self.cutoffs							)							):
																	UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	UpperCamelCase__     :Dict							      =				d_embed // (div_val**i)

																	self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)

																	self.out_layers.append(nn.Linear(UpperCamelCase_					,     r_idx - l_idx							)							)

							UpperCamelCase__     :Tuple							      =				keep_order




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if proj is None:
												UpperCamelCase__     :List[str]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												UpperCamelCase__     :Any							      =				nn.functional.linear(UpperCamelCase_					,     proj.t().contiguous()							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
												# else:
												#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
												#     if bias is not None:
												#         logit = logit + bias

							return logit




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if labels is not None:
												# Shift so that tokens < n predict n
												UpperCamelCase__     :Optional[Any]							      =				hidden[..., :-1, :].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				labels[..., 1:].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				hidden.view(-1					,     hidden.size(-1							)							)
												UpperCamelCase__     :str							      =				labels.view(-1							)
												if hidden.size(0							) != labels.size(0							):
																	raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''							)
							else:
												UpperCamelCase__     :int							      =				hidden.view(-1					,     hidden.size(-1							)							)

							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												if labels is not None:
																	UpperCamelCase__     :int							      =				labels != -100
																	UpperCamelCase__     :List[Any]							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)
																	UpperCamelCase__     :str							      =				(
																	    -nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)[mask].gather(1					,     labels[mask].unsqueeze(1							)							).squeeze(1							)
																	)
												else:
																	UpperCamelCase__     :Dict							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :str							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :Optional[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :str							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :List[str]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				weights[0], biases[0], self.out_projs[0]

												UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												if labels is None:
																	UpperCamelCase__     :Union[str, Any]							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												else:
																	UpperCamelCase__     :Any							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)

												UpperCamelCase__     :Any							      =				0
												UpperCamelCase__     :str							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																						UpperCamelCase__     :Any							      =				(labels >= l_idx) & (labels < r_idx)
																						UpperCamelCase__     :int							      =				mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																											continue

																						UpperCamelCase__     :Tuple							      =				labels.index_select(0					,     UpperCamelCase_							) - l_idx
																						UpperCamelCase__     :str							      =				head_logprob.index_select(0					,     UpperCamelCase_							)
																						UpperCamelCase__     :int							      =				hidden.index_select(0					,     UpperCamelCase_							)
																	else:
																						UpperCamelCase__     :Dict							      =				hidden

																	if i == 0:
																						if labels is not None:
																											UpperCamelCase__     :Any							      =				head_logprob_i.gather(1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)
																						UpperCamelCase__     :Any							      =				self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																											UpperCamelCase__     :List[Any]							      =				head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																											    1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																											UpperCamelCase__     :Dict							      =				logprob_i

																	if labels is not None:
																						if (hasattr(self					,     '''keep_order'''							) and self.keep_order) or keep_order:
																											out.index_copy_(0					,     UpperCamelCase_					,     -logprob_i							)
																						else:
																											out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
																						offset += logprob_i.size(0							)

							return out




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[Any]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												return nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :Union[str, Any]							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :str							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :Dict							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :Optional[Any]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				weights[0], biases[0], self.out_projs[0]
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

												UpperCamelCase__     :int							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												UpperCamelCase__     :int							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if i == 0:
																						UpperCamelCase__     :str							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :List[str]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Tuple							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

																						UpperCamelCase__     :Optional[Any]							      =				head_logprob[:, -i] + tail_logprob_i
																						UpperCamelCase__     :Union[str, Any]							      =				logprob_i

												return out