

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 						__magic_name__      (							_lowerCamelCase							):
   lowerCamelCase__			:     Dict    =       ['image_processor', 'tokenizer']
   lowerCamelCase__			:     Union[str, Any]    =       'LayoutLMv2ImageProcessor'
   lowerCamelCase__			:     Any    =       ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



   def __init__(    self,   lowercase_=None,   lowercase_=None,   **lowercase_	)							->   str:






     """simple docstring"""






     a__  =None
     if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.''',   __UpperCamelCase,   )
       a__  =kwargs.pop('''feature_extractor'''	)

     a__  =image_processor if image_processor is not None else feature_extractor
     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`.'''	)

     super().__init__(__UpperCamelCase,   __UpperCamelCase	)



   def __call__(    self,   lowercase_,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = True,   lowercase_ = False,   lowercase_ = False,   lowercase_ = None,   lowercase_ = 0,   lowercase_ = None,   lowercase_ = None,   lowercase_ = None,   lowercase_ = False,   lowercase_ = False,   lowercase_ = False,   lowercase_ = False,   lowercase_ = True,   lowercase_ = None,   **lowercase_,   )							->   BatchEncoding:






     """simple docstring"""






     if self.image_processor.apply_ocr and (boxes is not None):
       raise ValueError(
           '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''	)

     if self.image_processor.apply_ocr and (word_labels is not None):
       raise ValueError(
           '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

     if return_overflowing_tokens is True and return_offsets_mapping is False:
       raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

     # first, apply the image processor
     a__  =self.image_processor(images=__UpperCamelCase,   return_tensors=__UpperCamelCase	)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
       if isinstance(__UpperCamelCase,   __UpperCamelCase	):
         a__  =[text]  # add batch dimension (as the image processor always adds a batch dimension)
       a__  =features['words']

     a__  =self.tokenizer(
         text=text if text is not None else features['''words'''],   text_pair=text_pair if text_pair is not None else None,   boxes=boxes if boxes is not None else features['''boxes'''],   word_labels=__UpperCamelCase,   add_special_tokens=__UpperCamelCase,   padding=__UpperCamelCase,   truncation=__UpperCamelCase,   max_length=__UpperCamelCase,   stride=__UpperCamelCase,   pad_to_multiple_of=__UpperCamelCase,   return_token_type_ids=__UpperCamelCase,   return_attention_mask=__UpperCamelCase,   return_overflowing_tokens=__UpperCamelCase,   return_special_tokens_mask=__UpperCamelCase,   return_offsets_mapping=__UpperCamelCase,   return_length=__UpperCamelCase,   verbose=__UpperCamelCase,   return_tensors=__UpperCamelCase,   **__UpperCamelCase,   )

     # add pixel values
     a__  =features.pop('''pixel_values'''	)
     if return_overflowing_tokens is True:
       a__  =self.get_overflowing_images(__UpperCamelCase,   encoded_inputs['''overflow_to_sample_mapping''']	)
     a__  =images

     return encoded_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =[]
     for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]	)

     if len(__UpperCamelCase	) != len(__UpperCamelCase	):
       raise ValueError(
           '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
           F""" {len(__UpperCamelCase	)} and {len(__UpperCamelCase	)}"""	)

     return images_with_overflow



   def 			_UpperCAmelCase			(    self,   *lowercase_,   **lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     return self.tokenizer.batch_decode(*__UpperCamelCase,   **__UpperCamelCase	)



   def 			_UpperCAmelCase			(    self,   *lowercase_,   **lowercase_	)							->   Tuple:






     """simple docstring"""






     return self.tokenizer.decode(*__UpperCamelCase,   **__UpperCamelCase	)



   @property
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



   @property
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     warnings.warn(
         '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',   __UpperCamelCase,   )
     return self.image_processor_class



   @property
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     warnings.warn(
         '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',   __UpperCamelCase,   )
     return self.image_processor



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


lowerCamelCase									= get_logger(__name__)




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			List[dict]     ,				_A				:			list     ,				_A				:			list     ,				_A				:			int     ,				_A				:			GradScaler = None     ,				):





  '''simple docstring'''







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




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			List[str]=None     ,				_A				:			Dict=None     ,				**_A				:			Tuple     ,				):





  '''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:
    a__  ='''cpu'''
  elif map_location == "on_device":
    a__  =PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    models[i].load_state_dict(torch.load(_A     ,				map_location=_A							)     ,				**_A							)
  logger.info('''All model weights loaded successfully'''							)

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

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__  =os.path.join(_A     ,				_A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__  =torch.load(os.path.join(_A     ,				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__    (      _A				:			Tuple     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()     ,				_A							)




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A     ,				map_location='''cpu'''							)							)
