
import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
      import torch

_lowerCamelCase           =logging.get_logger(__name__)





def 					_a      (		):
 return importlib.util.find_spec("""deepspeed"""							) is not None


if is_accelerate_available() and is_deepspeed_available():
      from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
      # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
      # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
      from builtins import object as DeepSpeedConfig





class 							A__	(			__SCREAMING_SNAKE_CASE):
      def __init__(   self					,						__magic_name__   ):
       # set global weakref object
       set_hf_deepspeed_config(self   )
       dep_version_check("""accelerate"""   )
       dep_version_check("""deepspeed"""   )
       super().__init__(__magic_name__   )





class 							A__	(			__SCREAMING_SNAKE_CASE):
      def __init__(   self					,						__magic_name__   ):
       super().__init__(__magic_name__   )
       lowerCamelCase	:			Tuple           =   None
       lowerCamelCase	:			List[Any]           =   []
      def 		UpperCamelCase__							(   self   ):
       if self._dtype is None:
        raise ValueError("""trainer_config_process() wasn't called yet to tell dtype"""   )
       return self._dtype
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			List[Any]           =   self.get_value(__magic_name__   )
       if val is None:
        return False
       else:
        return val == "auto"
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__=None					,						__magic_name__=True   ):
       lowerCamelCase    ,					lowerCamelCase	:			Tuple           =   self.find_config_node(__magic_name__   )
       if config is None:
        return

       if config.get(__magic_name__   ) == "auto":
        lowerCamelCase	:			Dict           =   hf_val
        return

       if not must_match:
        return

       lowerCamelCase	:			str           =   config.get(__magic_name__   )
       if ds_val is not None and ds_val != hf_val:
        self.mismatches.append(F'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''   )

      _UpperCAmelCase       :					Optional[int]					 =    partialmethod(__SCREAMING_SNAKE_CASE						,     must_match=__SCREAMING_SNAKE_CASE)
      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			int           =   args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
       self.fill_match(
           """train_micro_batch_size_per_gpu"""					,						args.per_device_train_batch_size					,						"""per_device_train_batch_size"""   )
       self.fill_match("""gradient_accumulation_steps"""					,						args.gradient_accumulation_steps					,						"""gradient_accumulation_steps"""   )
       self.fill_match("""train_batch_size"""					,						__magic_name__					,						"""train_batch_size (calculated)"""   )
       self.fill_match("""gradient_clipping"""					,						args.max_grad_norm					,						"""max_grad_norm"""   )

       self.fill_match("""optimizer.params.lr"""					,						args.learning_rate					,						"""learning_rate"""   )
       self.fill_match("""optimizer.params.betas"""					,						[args.adam_betaa, args.adam_betaa]					,						"""adam_beta1+adam_beta2"""   )
       self.fill_match("""optimizer.params.eps"""					,						args.adam_epsilon					,						"""adam_epsilon"""   )
       self.fill_match("""optimizer.params.weight_decay"""					,						args.weight_decay					,						"""weight_decay"""   )

       self.fill_only("""scheduler.params.warmup_min_lr"""					,						0   )  # not a trainer arg
       self.fill_match("""scheduler.params.warmup_max_lr"""					,						args.learning_rate					,						"""learning_rate"""   )
       # total_num_steps - will get set in trainer_config_finalize

       # fp16
       if args.fpaa or args.fpaa_full_eval:
        lowerCamelCase	:			int           =   """apex""" if args.fpaa_backend == """apex""" else """amp"""
       else:
        lowerCamelCase	:			Any           =   None

       if args.save_on_each_node:
        # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
        lowerCamelCase	:			List[str]           =   self.config.get("""checkpoint"""					,						{}   )
        lowerCamelCase	:			List[Any]           =   args.save_on_each_node

       # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
       # any here unless the user did the work
       self.fill_match(
           """fp16.enabled"""					,						((args.fpaa or args.fpaa_full_eval) and fpaa_backend == """amp""")					,						"""fp16|fp16_full_eval+fp16_backend(amp)"""					,						)

       # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
       # ZeRO features
       self.fill_match("""amp.enabled"""					,						fpaa_backend == """apex"""					,						"""fp16+fp16_backend(apex)"""   )
       self.fill_match("""amp.opt_level"""					,						args.fpaa_opt_level					,						"""fp16_opt_level"""   )

       self.fill_match("""bf16.enabled"""					,						(args.bfaa or args.bfaa_full_eval)					,						"""bf16|bf16_full_eval"""   )

       # deepspeed's default mode is fp16 unless there is a config that says differently
       if self.is_true("""bf16.enabled"""   ):
        lowerCamelCase	:			Optional[int]           =   torch.bfloataa
       elif self.is_false("""fp16.enabled"""   ):
        lowerCamelCase	:			Dict           =   torch.floataa
       else:
        lowerCamelCase	:			Optional[int]           =   torch.floataa






      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			List[Any]           =   [
           """zero_optimization.reduce_bucket_size""",
           """zero_optimization.stage3_prefetch_bucket_size""",
           """zero_optimization.stage3_param_persistence_threshold""",
       ]
       lowerCamelCase	:			Optional[int]           =   [x for x in hidden_size_based_keys if self.is_auto(__magic_name__   )]

       if len(__magic_name__   ) > 0:
        if hasattr(model.config					,						"""hidden_size"""   ):
         lowerCamelCase	:			Union[str, Any]           =   model.config.hidden_size
        elif hasattr(model.config					,						"""hidden_sizes"""   ):
         # if there are many hidden sizes pick the largest one
         lowerCamelCase	:			Any           =   max(model.config.hidden_sizes   )
        else:
         raise ValueError(
             """The model's config file has neither `hidden_size` nor `hidden_sizes` entry, """
             """therefore it's not possible to automatically fill out the following `auto` entries """
             F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
             """`auto` values for these keys with an integer value of your choice."""   )

        self.fill_only("""zero_optimization.reduce_bucket_size"""					,						hidden_size * hidden_size   )
        if self.is_zeroa():
         # automatically assign the optimal config values based on model config
         self.fill_only("""zero_optimization.stage3_prefetch_bucket_size"""					,						0.9 * hidden_size * hidden_size   )
         self.fill_only("""zero_optimization.stage3_param_persistence_threshold"""					,						1_0 * hidden_size   )

        # scheduler
       self.fill_match("""scheduler.params.total_num_steps"""					,						__magic_name__					,						"""num_training_steps (calculated)"""   )
       self.fill_match("""scheduler.params.warmup_num_steps"""					,						args.get_warmup_steps(__magic_name__   )					,						"""warmup_steps"""   )

       if len(self.mismatches   ) > 0:
        lowerCamelCase	:			List[Any]           =   """\n""".join(self.mismatches   )
        raise ValueError(
            """Please correct the following DeepSpeed config values that mismatch TrainingArguments"""
            F''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''   )


_lowerCamelCase           =None





def 					_a      (		lowerCamelCase							):
 # this is a special weakref global object to allow us to get to Deepspeed config from APIs
 # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
 global _hf_deepspeed_config_weak_ref
 # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
 lowerCamelCase	:			Tuple           =   weakref.ref(lowerCamelCase							)





def 					_a      (		):
 # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
 global _hf_deepspeed_config_weak_ref
 lowerCamelCase	:			Optional[int]           =   None





def 					_a      (		):
 if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
  return _hf_deepspeed_config_weak_ref().is_zeroa()
 else:
  return False





def 					_a      (		):
 if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
  return _hf_deepspeed_config_weak_ref().config
 else:
  return None





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 from accelerate.utils import DummyOptim, DummyScheduler

 lowerCamelCase	:			List[str]           =   hf_deepspeed_config.config

 # Optimizer + Scheduler
 # Currently supported combos:
 # 1. DS scheduler + DS optimizer: Yes
 # 2. HF scheduler + HF optimizer: Yes
 # 3. DS scheduler + HF optimizer: Yes
 # 4. HF scheduler + DS optimizer: No
 #
 # Unless Offload is enabled in which case it's:
 # 1. DS scheduler + DS optimizer: Yes
 # 2. HF scheduler + HF optimizer: Mostly*
 # 3. DS scheduler + HF optimizer: Mostly*
 # 4. HF scheduler + DS optimizer: No
 #
 # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

 lowerCamelCase	:			List[str]           =   None
 if "optimizer" in config:
  if args.adafactor:
   raise ValueError(
       """--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. """
       """Only one optimizer can be configured."""							)
  lowerCamelCase	:			Optional[int]           =   DummyOptim(params=lowerCamelCase							)
 else:
  if hf_deepspeed_config.is_offload():
   logger.info(
       """Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"""
       """ custom optimizer has both CPU and GPU implementation (except LAMB)"""							)

  # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
  # But trainer uses AdamW by default.
  lowerCamelCase	:			List[str]           =   trainer.create_optimizer()
  # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  lowerCamelCase	:			Optional[int]           =   True

 lowerCamelCase	:			Dict           =   None
 if "scheduler" in config:
  lowerCamelCase	:			List[Any]           =   DummyScheduler(lowerCamelCase							)
 else:
  if isinstance(lowerCamelCase,			lowerCamelCase							):
   raise ValueError(
       """Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. """
       """Please configure a scheduler in the DeepSpeed config."""							)
  lowerCamelCase	:			Dict           =   trainer.create_scheduler(num_training_steps=lowerCamelCase,			optimizer=lowerCamelCase							)

 return optimizer, lr_scheduler





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase=False							):
 from deepspeed.utils import logger as ds_logger

 lowerCamelCase	:			List[Any]           =   trainer.model
 lowerCamelCase	:			Tuple           =   trainer.args

 lowerCamelCase	:			List[Any]           =   trainer.accelerator.state.deepspeed_plugin.hf_ds_config

 # resume config update - some bits like `model` and `num_training_steps` only become available during train
 hf_deepspeed_config.trainer_config_finalize(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 # set the Deepspeed log level consistent with the Trainer
 ds_logger.setLevel(args.get_process_log_level()							)

 if inference:
  # only Z3 makes sense for the inference
  if not hf_deepspeed_config.is_zeroa():
   raise ValueError("""ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"""							)

  # in case the training config is re-used for inference
  hf_deepspeed_config.del_config_sub_tree("""optimizer"""							)
  hf_deepspeed_config.del_config_sub_tree("""lr_scheduler"""							)
  lowerCamelCase    ,					lowerCamelCase	:			Optional[int]           =   None, None
  lowerCamelCase	:			int           =   None
 else:
  lowerCamelCase	:			List[Any]           =   None  # important for when deepspeed_init is used as re-init
  lowerCamelCase	:			str           =   list(filter(lambda lowerCamelCase							: p.requires_grad,			model.parameters()							)							)
  lowerCamelCase    ,					lowerCamelCase	:			Union[str, Any]           =   deepspeed_optim_sched(
      lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)

 # keep for quick debug:
 # from pprint import pprint; pprint(config)

 return optimizer, lr_scheduler





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 # it's possible that the user is trying to resume from model_path, which doesn't necessarily
 # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
 # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
 # path contains what looks like a deepspeed checkpoint
 import glob

 lowerCamelCase	:			Tuple           =   sorted(glob.glob(F'''{checkpoint_path}/global_step*'''							)							)

 if len(lowerCamelCase							) > 0:
  logger.info(F'''Attempting to resume from {checkpoint_path}'''							)
  # this magically updates self.optimizer and self.lr_scheduler
  lowerCamelCase    ,					lowerCamelCase	:			int           =   deepspeed_engine.load_checkpoint(
      lowerCamelCase,			load_optimizer_states=lowerCamelCase,			load_lr_scheduler_states=lowerCamelCase							)
  if load_path is None:
   raise ValueError(F'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''							)
 else:
  raise ValueError(F'''Can\'t find a valid checkpoint at {checkpoint_path}'''							)






from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
      import torch





class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Optional[int]					 =    ["""image_processor""", """tokenizer"""]
      _UpperCAmelCase       :					List[Any]					 =    """OneFormerImageProcessor"""
      _UpperCAmelCase       :					Optional[int]					 =    ("""CLIPTokenizer""", """CLIPTokenizerFast""")
      def __init__(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__ = 7_7					,						__magic_name__ = 7_7					,						**__magic_name__   ):
       if image_processor is None:
        raise ValueError("""You need to specify an `image_processor`."""   )
       if tokenizer is None:
        raise ValueError("""You need to specify a `tokenizer`."""   )

       lowerCamelCase	:			List[Any]           =   max_seq_length
       lowerCamelCase	:			Dict           =   task_seq_length

       super().__init__(__magic_name__					,						__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__=None					,						__magic_name__=7_7   ):
       if text_list is None:
        raise ValueError("""tokens cannot be None."""   )

       lowerCamelCase	:			Tuple           =   self.tokenizer(__magic_name__					,						padding="""max_length"""					,						max_length=__magic_name__					,						truncation=__magic_name__   )

       lowerCamelCase    ,					lowerCamelCase	:			int           =   tokens["""attention_mask"""], tokens["""input_ids"""]

       lowerCamelCase	:			List[str]           =   []
       for attn_mask, input_id in zip(__magic_name__					,						__magic_name__   ):
        lowerCamelCase	:			Optional[int]           =   torch.tensor(__magic_name__   ) * torch.tensor(__magic_name__   )
        token_inputs.append(token.unsqueeze(0   )   )

       lowerCamelCase	:			Optional[Any]           =   torch.cat(__magic_name__					,						dim=0   )
       return token_inputs
      def __call__(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__   ):

       if task_inputs is None:
        raise ValueError("""You have to specify the task_input. Found None."""   )
       elif images is None:
        raise ValueError("""You have to specify the image. Found None."""   )

       if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs   ):
        raise ValueError("""task_inputs must be semantic, instance, or panoptic."""   )

       lowerCamelCase	:			Any           =   self.image_processor(__magic_name__					,						__magic_name__					,						__magic_name__					,						**__magic_name__   )

       if isinstance(__magic_name__					,						__magic_name__   ):
        lowerCamelCase	:			List[str]           =   [task_inputs]

       if isinstance(__magic_name__					,						__magic_name__   ) and all(isinstance(__magic_name__					,						__magic_name__   ) for task_input in task_inputs   ):
        lowerCamelCase	:			int           =   []
        for task in task_inputs:
         lowerCamelCase	:			int           =   F'''the task is {task}'''
         task_token_inputs.append(__magic_name__   )
        lowerCamelCase	:			Union[str, Any]           =   self._preprocess_text(__magic_name__					,						max_length=self.task_seq_length   )
       else:
        raise TypeError("""Task Inputs should be a string or a list of strings."""   )

       if hasattr(__magic_name__					,						"""text_inputs"""   ):
        lowerCamelCase	:			List[str]           =   encoded_inputs.text_inputs

        lowerCamelCase	:			str           =   []
        for texts in texts_list:
         lowerCamelCase	:			int           =   self._preprocess_text(__magic_name__					,						max_length=self.max_seq_length   )
         text_inputs.append(text_input_list.unsqueeze(0   )   )

        lowerCamelCase	:			int           =   torch.cat(__magic_name__					,						dim=0   )

       return encoded_inputs
      def 		UpperCamelCase__							(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__   ):

       if task_inputs is None:
        raise ValueError("""You have to specify the task_input. Found None."""   )
       elif images is None:
        raise ValueError("""You have to specify the image. Found None."""   )

       if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs   ):
        raise ValueError("""task_inputs must be semantic, instance, or panoptic."""   )

       lowerCamelCase	:			List[Any]           =   self.image_processor.encode_inputs(__magic_name__					,						__magic_name__					,						__magic_name__					,						**__magic_name__   )

       if isinstance(__magic_name__					,						__magic_name__   ):
        lowerCamelCase	:			int           =   [task_inputs]

       if isinstance(__magic_name__					,						__magic_name__   ) and all(isinstance(__magic_name__					,						__magic_name__   ) for task_input in task_inputs   ):
        lowerCamelCase	:			Union[str, Any]           =   []
        for task in task_inputs:
         lowerCamelCase	:			Optional[int]           =   F'''the task is {task}'''
         task_token_inputs.append(__magic_name__   )
        lowerCamelCase	:			Tuple           =   self._preprocess_text(__magic_name__					,						max_length=self.task_seq_length   )
       else:
        raise TypeError("""Task Inputs should be a string or a list of strings."""   )

       if hasattr(__magic_name__					,						"""text_inputs"""   ):
        lowerCamelCase	:			int           =   encoded_inputs.text_inputs

        lowerCamelCase	:			Dict           =   []
        for texts in texts_list:
         lowerCamelCase	:			Union[str, Any]           =   self._preprocess_text(__magic_name__					,						max_length=self.max_seq_length   )
         text_inputs.append(text_input_list.unsqueeze(0   )   )

        lowerCamelCase	:			Optional[int]           =   torch.cat(__magic_name__					,						dim=0   )

       return encoded_inputs
      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       return self.image_processor.post_process_semantic_segmentation(*__magic_name__					,						**__magic_name__   )
      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       return self.image_processor.post_process_instance_segmentation(*__magic_name__					,						**__magic_name__   )






      def 		UpperCamelCase__							(   self					,						*__magic_name__					,						**__magic_name__   ):
       return self.image_processor.post_process_panoptic_segmentation(*__magic_name__					,						**__magic_name__   )




