



'''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 math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin






class 		_UpperCamelCase						(							lowerCamelCase__					,      lowerCamelCase__					):


			'''simple docstring'''

			@register_to_config
			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 1_2_8							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 20_00.0							,  lowerCAmelCase__  :				int = 7_6_8							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 1_2							,  lowerCAmelCase__  :				int = 6_4							,  lowerCAmelCase__  :				int = 2_0_4_8							,  lowerCAmelCase__  :				float = 0.1							,  ):





					"""simple docstring"""






					super().__init__()

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Sequential(
					    nn.Linear(lowerCAmelCase__							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  nn.Linear(d_model * 4							,  d_model * 4							,  bias=lowerCAmelCase__		)							,  nn.SiLU()							,  )

					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Embedding(lowerCAmelCase__							,  lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	False

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	nn.Dropout(p=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.ModuleList()
					for lyr_num in range(lowerCAmelCase__		):
							# FiLM conditional T5 decoder
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	DecoderLayer(d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
							self.decoders.append(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	TaLayerNorm(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	nn.Dropout(p=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mul(query_input.unsqueeze(-1		)							,  key_input.unsqueeze(-2		)		)
					return mask.unsqueeze(-3		)



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	List[str]					 =	decoder_input_tokens.shape
					assert decoder_noise_time.shape == (batch,)

					# decoder_noise_time is in [0, 1), so rescale to expected timing range.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	get_timestep_embedding(
					    decoder_noise_time * self.config.max_decoder_noise_time							,  embedding_dim=self.config.d_model							,  max_period=self.config.max_decoder_noise_time							,  ).to(dtype=self.dtype		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.conditioning_emb(lowerCAmelCase__		).unsqueeze(1		)

					assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	decoder_input_tokens.shape[1]

					# If we want to use relative positions for audio context, we can just offset
					# this sequence by the length of encodings_and_masks.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.broadcast_to(
					    torch.arange(lowerCAmelCase__							,  device=decoder_input_tokens.device		)							,  (batch, seq_length)							,  )

					__SCREAMING_SNAKE_CASE :	str					 =	self.position_encoding(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.continuous_inputs_projection(lowerCAmelCase__		)
					inputs += position_encodings
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dropout(lowerCAmelCase__		)

					# decoder: No padding present.
					__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.ones(
					    decoder_input_tokens.shape[:2]							,  device=decoder_input_tokens.device							,  dtype=inputs.dtype		)

					# Translate encoding masks to encoder-decoder masks.
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	[(x, self.encoder_decoder_mask(lowerCAmelCase__							,  lowerCAmelCase__		)) for x, y in encodings_and_masks]

					# cross attend style: concat encodings
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.cat([x[0] for x in encodings_and_encdec_masks]							,  dim=1		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.cat([x[1] for x in encodings_and_encdec_masks]							,  dim=-1		)

					for lyr in self.decoders:
							__SCREAMING_SNAKE_CASE :	Dict					 =	lyr(
							    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  encoder_attention_mask=lowerCAmelCase__							,  )[0]

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.decoder_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.post_dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.spec_out(lowerCAmelCase__		)
					return spec_out






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Tuple=1E-6		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.ModuleList()

					# cond self attention: layer 0
					self.layer.append(
					    TaLayerSelfAttentionCond(d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)		)

					# cross attention: layer 1
					self.layer.append(
					    TaLayerCrossAttention(
					        d_model=lowerCAmelCase__							,  d_kv=lowerCAmelCase__							,  num_heads=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__							,  layer_norm_epsilon=lowerCAmelCase__							,  )		)

					# Film Cond MLP + dropout: last layer
					self.layer.append(
					    TaLayerFFCond(d_model=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__							,  layer_norm_epsilon=lowerCAmelCase__		)		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				List[Any]=None							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Optional[int]=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer[0](
					    lowerCAmelCase__							,  conditioning_emb=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					if encoder_hidden_states is not None:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.where(encoder_attention_mask > 0							,  0							,  -1E10		).to(
							    encoder_hidden_states.dtype		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[1](
							    lowerCAmelCase__							,  key_value_states=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  )

					# Apply Film Conditional Feed Forward layer
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.layer[-1](lowerCAmelCase__							,  lowerCAmelCase__		)

					return (hidden_states,)






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	TaLayerNorm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				int=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.layer_norm(lowerCAmelCase__		)

					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	str					 =	self.FiLMLayer(lowerCAmelCase__							,  lowerCAmelCase__		)

					# Self-attention block
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.attention(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	str					 =	hidden_states + self.dropout(lowerCAmelCase__		)

					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	Attention(query_dim=lowerCAmelCase__							,  heads=lowerCAmelCase__							,  dim_head=lowerCAmelCase__							,  out_bias=lowerCAmelCase__							,  scale_qk=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Any=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.layer_norm(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.attention(
					    lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  attention_mask=attention_mask.squeeze(1		)							,  )
					__SCREAMING_SNAKE_CASE :	Dict					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return layer_output






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Any							,  lowerCAmelCase__  :				Tuple							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Any					 =	TaDenseGatedActDense(d_model=lowerCAmelCase__							,  d_ff=lowerCAmelCase__							,  dropout_rate=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	TaFiLMLayer(in_features=d_model * 4							,  out_features=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	TaLayerNorm(lowerCAmelCase__							,  eps=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Dropout(lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				int							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Tuple=None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	self.layer_norm(lowerCAmelCase__		)
					if conditioning_emb is not None:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.film(lowerCAmelCase__							,  lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	int					 =	self.DenseReluDense(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states + self.dropout(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Linear(lowerCAmelCase__							,  lowerCAmelCase__							,  bias=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	nn.Dropout(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	NewGELUActivation()



			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.act(self.wi_a(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	self.wi_a(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	hidden_gelu * hidden_linear
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dropout(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.wo(lowerCAmelCase__		)
					return hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Tuple=1E-6		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Parameter(torch.ones(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	str					 =	eps



			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	hidden_states.to(torch.floataa		).pow(2		).mean(-1							,  keepdim=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

					# convert into half-precision if necessary
					if self.weight.dtype in [torch.floataa, torch.bfloataa]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	hidden_states.to(self.weight.dtype		)

					return self.weight * hidden_states






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				torch.Tensor		):





					"""simple docstring"""






					return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase__							,  3.0		))		))






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.Linear(lowerCAmelCase__							,  out_features * 2							,  bias=lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scale_bias(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Tuple					 =	torch.chunk(lowerCAmelCase__							,  2							,  -1		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	x * (1 + scale) + shift
					return x