




'''simple docstring'''





import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


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

from .logging import get_logger
from .state import PartialState


lowercase__								=						get_logger(__name__)


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case = None						,  ):




   '''simple docstring'''

   for i, state in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
      SCREAMING_SNAKE_CASE__      :					int							    =			os.path.join(snake_case						,  snake_case )
      save(snake_case						,  snake_case )
      logger.info(f"""Model weights saved in {output_model_file}""" )
   # Optimizer states
   for i, opt in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			opt.state_dict()
      SCREAMING_SNAKE_CASE__      :					str							    =			f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			os.path.join(snake_case						,  snake_case )
      save(snake_case						,  snake_case )
      logger.info(f"""Optimizer state saved in {output_optimizer_file}""" )
   # Scheduler states
   for i, scheduler in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Any							    =			scheduler.state_dict()
      SCREAMING_SNAKE_CASE__      :					str							    =			f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			os.path.join(snake_case						,  snake_case )
      save(snake_case						,  snake_case )
      logger.info(f"""Scheduler state saved in {output_scheduler_file}""" )
   # GradScaler state
   if scaler is not None:
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			scaler.state_dict()
      SCREAMING_SNAKE_CASE__      :					Any							    =			os.path.join(snake_case						,  snake_case )
      torch.save(snake_case						,  snake_case )
      logger.info(f"""Gradient scaler state saved in {output_scaler_file}""" )
   # Random number generator states
   SCREAMING_SNAKE_CASE__      :					str							    =			{}
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			f"""{RNG_STATE_NAME}_{process_index}.pkl"""
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			random.getstate()
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			np.random.get_state()
   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.get_rng_state()
   if is_xpu_available():
      SCREAMING_SNAKE_CASE__      :					str							    =			torch.xpu.get_rng_state_all()
   else:
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.cuda.get_rng_state_all()
   if is_tpu_available():
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			xm.get_rng_state()
   SCREAMING_SNAKE_CASE__      :					int							    =			os.path.join(snake_case						,  snake_case )
   torch.save(snake_case						,  snake_case )
   logger.info(f"""Random states saved in {output_states_file}""" )
   return output_dir


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case=None						,  snake_case=None						,  **snake_case						,  ):




   '''simple docstring'''

   if map_location not in [None, "cpu", "on_device"]:
      raise TypeError(
          '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`''' )
   if map_location is None:
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			'''cpu'''
   elif map_location == "on_device":
      SCREAMING_SNAKE_CASE__      :					Dict							    =			PartialState().device
   # Model states
   for i, model in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Dict							    =			f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
      SCREAMING_SNAKE_CASE__      :					Any							    =			os.path.join(snake_case						,  snake_case )
      models[i].load_state_dict(torch.load(snake_case						,  map_location=snake_case )						,  **snake_case )
   logger.info('''All model weights loaded successfully''' )

   # Optimizer states
   for i, opt in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Any							    =			f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			os.path.join(snake_case						,  snake_case )
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.load(snake_case						,  map_location=snake_case )
      optimizers[i].load_state_dict(snake_case )
   logger.info('''All optimizer states loaded successfully''' )

   # Scheduler states
   for i, scheduler in enumerate(snake_case ):
      SCREAMING_SNAKE_CASE__      :					Any							    =			f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			os.path.join(snake_case						,  snake_case )
      scheduler.load_state_dict(torch.load(snake_case ) )
   logger.info('''All scheduler states loaded successfully''' )

   # GradScaler state
   if scaler is not None:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			os.path.join(snake_case						,  snake_case )
      scaler.load_state_dict(torch.load(snake_case ) )
      logger.info('''GradScaler state loaded successfully''' )

   # Random states
   try:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			torch.load(os.path.join(snake_case						,  f"""{RNG_STATE_NAME}_{process_index}.pkl""" ) )
      random.setstate(states['''random_state'''] )
      np.random.set_state(states['''numpy_random_seed'''] )
      torch.set_rng_state(states['''torch_manual_seed'''] )
      if is_xpu_available():
         torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed'''] )
      else:
         torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed'''] )
      if is_tpu_available():
         xm.set_rng_state(states['''xm_seed'''] )
      logger.info('''All random states loaded successfully''' )
   except Exception:
      logger.info('''Could not load random states''' )


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case = 0 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					int							    =			Path(snake_case ) / f"""custom_checkpoint_{index}.pkl"""
   logger.info(f"""Saving the state of {get_pretty_name(snake_case )} to {save_location}""" )
   torch.save(obj.state_dict()						,  snake_case )







def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case = 0 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					int							    =			f"""{path}/custom_checkpoint_{index}.pkl"""
   logger.info(f"""Loading the state of {get_pretty_name(snake_case )} from {load_location}""" )
   obj.load_state_dict(torch.load(snake_case						,  map_location='''cpu''' ) )







'''simple docstring'''





from typing import List

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


if is_torch_available():
       import torch






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''OneFormerImageProcessor'''
      __lowercase					=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	a__ = 7_7			,	a__ = 7_7			,	**a__       )							-> str:
         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`.'''       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			max_seq_length
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			task_seq_length

         super().__init__(a__			,	a__       )






      def  A__(self			,	a__=None			,	a__=7_7       )							-> str:
         if text_list is None:
            raise ValueError('''tokens cannot be None.'''       )

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(a__			,	padding='''max_length'''			,	max_length=a__			,	truncation=a__       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			tokens['''attention_mask'''], tokens['''input_ids''']

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         for attn_mask, input_id in zip(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(a__       ) * torch.tensor(a__       )
            token_inputs.append(token.unsqueeze(0       )       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.cat(a__			,	dim=0       )
         return token_inputs






      def __call__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> str:

         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.'''       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.image_processor(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[task_inputs]

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

         if hasattr(a__			,	'''text_inputs'''       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					int							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> Any:

         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.'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.image_processor.encode_inputs(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[task_inputs]

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

         if hasattr(a__			,	'''text_inputs'''       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.image_processor.post_process_semantic_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.image_processor.post_process_instance_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Tuple:
         return self.image_processor.post_process_panoptic_segmentation(*a__			,	**a__       )

