



import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


lowerCamelCase__			= logging.get_logger(__name__)

def   lowerCAmelCase__						(							):
    """simple docstring"""







    __a       =		os.getenv("""SM_HP_MP_PARAMETERS"""				,     """{}"""    )
    try:
        # Parse it and check the field "partitions" is included, it is required for model parallel.
        __a       =		json.loads(_SCREAMING_SNAKE_CASE    )
        if "partitions" not in smp_options:
            return False
    except json.JSONDecodeError:
        return False

    # Get the sagemaker specific framework parameters from mpi_options variable.
    __a       =		os.getenv("""SM_FRAMEWORK_PARAMS"""				,     """{}"""    )
    try:
        # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
        __a       =		json.loads(_SCREAMING_SNAKE_CASE    )
        if not mpi_options.get("""sagemaker_mpi_enabled"""				,     _SCREAMING_SNAKE_CASE    ):
            return False
    except json.JSONDecodeError:
        return False
    # Lastly, check if the `smdistributed` module is present.
    return importlib.util.find_spec("""smdistributed"""    ) is not None


if is_sagemaker_model_parallel_available():
      import smdistributed.modelparallel.torch as smp

      smp.init()






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
       __lowerCamelCase	:	str    =field(
           default=''			,      metadata={'help': 'Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'}			,      )





       def 						UpperCamelCase_					(   self		:		str	):

           '''simple docstring'''
           super().__post_init__()
           warnings.warn(
               """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
               """`TrainingArguments` instead."""      ,				__lowercase      ,				)





       @cached_property
       def 						UpperCamelCase_					(   self		:		List[Any]	):

           '''simple docstring'''
           logger.info("""PyTorch: setting up devices"""	)
           if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
               logger.warning(
                   """torch.distributed process group is initialized, but local_rank == -1. """
                   """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""	)
           if self.no_cuda:
               __a       =		torch.device("""cpu"""	)
               __a       =		0
           elif is_sagemaker_model_parallel_available():
               __a       =		smp.local_rank()
               __a       =		torch.device("""cuda"""      ,				__lowercase	)
               __a       =		1
           elif is_sagemaker_dp_enabled():
               import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

               torch.distributed.init_process_group(backend="""smddp"""      ,				timeout=self.ddp_timeout_delta	)
               __a       =		int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""	)	)
               __a       =		torch.device("""cuda"""      ,				self.local_rank	)
               __a       =		1
           elif self.local_rank == -1:
               # if n_gpu is > 1 we'll use nn.DataParallel.
               # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
               # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
               # trigger an error that a device index is missing. Index 0 takes into account the
               # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
               # will use the first GPU in that env, i.e. GPU#1
               __a       =		torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""	)
               # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
               # the default value.
               __a       =		torch.cuda.device_count()
           else:
               # Here, we'll use torch.distributed.
               # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
               if not torch.distributed.is_initialized():
                   torch.distributed.init_process_group(backend="""nccl"""      ,				timeout=self.ddp_timeout_delta	)
               __a       =		torch.device("""cuda"""      ,				self.local_rank	)
               __a       =		1

           if device.type == "cuda":
               torch.cuda.set_device(__lowercase	)

           return device





       @property
       def 						UpperCamelCase_					(   self		:		str	):

           '''simple docstring'''
           if is_sagemaker_model_parallel_available():
               return smp.dp_size()

           return super().world_size





       @property
       def 						UpperCamelCase_					(   self		:		Optional[Any]	):

           '''simple docstring'''
           return not is_sagemaker_model_parallel_available()







       @property
       def 						UpperCamelCase_					(   self		:		Optional[int]	):

           '''simple docstring'''
           return False





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
