


import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class       __magic_name__	:


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      if isinstance(A_							,	A_			):
       # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
       # modified it, it will not be accepted here again, since `auto` values would have been overridden
       UpperCamelCase__					:    Tuple          =   deepcopy(A_			)
      elif os.path.exists(A_			):
       with io.open(A_							,	'''r'''							,	encoding='''utf-8'''			) as f:
        UpperCamelCase__					:    List[str]          =   json.load(A_			)
      else:
       try:
        UpperCamelCase__					:    Optional[int]          =   baseaa.urlsafe_baadecode(A_			).decode('''utf-8'''			)
        UpperCamelCase__					:    int          =   json.loads(A_			)
       except (UnicodeDecodeError, AttributeError, ValueError):
        raise ValueError(
            F"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"			)

      UpperCamelCase__					:    Union[str, Any]          =   config

      self.set_stage_and_offload()


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.get_value('''zero_optimization.stage'''							,	-1			)

      # offload
      UpperCamelCase__					:    Optional[Any]          =   False
      if self.is_zeroa() or self.is_zeroa():
       UpperCamelCase__					:    List[str]          =   set(['''cpu''', '''nvme''']			)
       UpperCamelCase__					:    List[Any]          =   set(
           [
               self.get_value('''zero_optimization.offload_optimizer.device'''			),
               self.get_value('''zero_optimization.offload_param.device'''			),
           ]			)
       if len(offload_devices & offload_devices_valid			) > 0:
        UpperCamelCase__					:    Optional[Any]          =   True


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     List[Any]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.config

      # find the config node of interest if it exists
      UpperCamelCase__					:    Optional[int]          =   ds_key_long.split('''.'''			)
      UpperCamelCase__					:    List[Any]          =   nodes.pop()
      for node in nodes:
       UpperCamelCase__					:    List[str]          =   config.get(A_			)
       if config is None:
        return None, ds_key

      return config, ds_key


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     List[str]=None			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       ,				UpperCamelCase__					:    str          =   self.find_config_node(A_			)
      if config is None:
       return default
      return config.get(A_							,	A_			)


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Any=False			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.config

      # find the config node of interest if it exists
      UpperCamelCase__					:    str          =   ds_key_long.split('''.'''			)
      for node in nodes:
       UpperCamelCase__					:    List[Any]          =   config
       UpperCamelCase__					:    Dict          =   config.get(A_			)
       if config is None:
        if must_exist:
         raise ValueError(F"Can\'t find {ds_key_long} entry in the config: {self.config}"			)
        else:
         return

        # if found remove it
      if parent_config is not None:
       parent_config.pop(A_			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.get_value(A_			)
      return False if value is None else bool(A_			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     int			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.get_value(A_			)
      return False if value is None else not bool(A_			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      str:





      '''simple docstring'''






      return self._stage == 2


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Dict:





      '''simple docstring'''






      return self._stage == 3


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[int]:





      '''simple docstring'''






      return self._offload



class       __magic_name__	:


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     str			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   engine


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]							,	**lowerCamelCase__							:     int			)  ->      Tuple:





      '''simple docstring'''






      self.engine.backward(A_							,	**A_			)

      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      self.engine.step()
      # and this plugin overrides the above calls with no-ops when Accelerate runs under
      # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
      # training loop that works transparently under many training regimes.



class       __magic_name__	(     _lowercase):


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     str			)  ->      List[Any]:





      '''simple docstring'''






      super().__init__(A_							,	device_placement=A_							,	scaler=A_			)
      UpperCamelCase__					:    List[str]          =   hasattr(self.optimizer							,	'''overflow'''			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Dict=None			)  ->      List[Any]:





      '''simple docstring'''






      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      int:





      '''simple docstring'''






      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


     @property
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[Any]:





      '''simple docstring'''






      if self.__has_overflow__:
       return self.optimizer.overflow
      return False



class       __magic_name__	(     _lowercase):


     def __init__(      self							:     List[str]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      Dict:





      '''simple docstring'''






      super().__init__(A_							,	A_			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Union[str, Any]:





      '''simple docstring'''






      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



class       __magic_name__	:


     def __init__(      self							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     List[str]=0.001							,	lowerCamelCase__							:     Tuple=0							,	**lowerCamelCase__							:     Tuple			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   params
      UpperCamelCase__					:    List[Any]          =   lr
      UpperCamelCase__					:    Union[str, Any]          =   weight_decay
      UpperCamelCase__					:    Union[str, Any]          =   kwargs



class       __magic_name__	:


     def __init__(      self							:     Optional[int]							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     int=None							,	lowerCamelCase__							:     List[Any]=0							,	**lowerCamelCase__							:     Optional[int]			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   optimizer
      UpperCamelCase__					:    str          =   total_num_steps
      UpperCamelCase__					:    List[str]          =   warmup_num_steps
      UpperCamelCase__					:    Optional[Any]          =   kwargs






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):
     A:		Optional[Any]			=      ["image_processor", "tokenizer"]
     A:		Optional[Any]			=      "LayoutLMv2ImageProcessor"
     A:		List[str]			=      ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


     def __init__(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]=None							,	lowerCamelCase__							:     List[str]=None							,	**lowerCamelCase__							:     Any			)  ->      Optional[Any]:





      '''simple docstring'''






      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''							,	lowerCamelCase__							,	)
       UpperCamelCase__					:    str          =   kwargs.pop('''feature_extractor'''			)

      UpperCamelCase__					:    List[Any]          =   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__(lowerCamelCase__							,	lowerCamelCase__			)


     def __call__(      self							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	lowerCamelCase__							:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None							,	lowerCamelCase__							:     Union[List[List[int]], List[List[List[int]]]] = None							,	lowerCamelCase__							:     Optional[Union[List[int], List[List[int]]]] = None							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Union[bool, str, PaddingStrategy] = False							,	lowerCamelCase__							:     Union[bool, str, TruncationStrategy] = None							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     int = 0							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     Optional[bool] = None							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = False							,	lowerCamelCase__							:     bool = True							,	lowerCamelCase__							:     Optional[Union[str, TensorType]] = None							,	**lowerCamelCase__							:     Any							,	)  ->      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
      UpperCamelCase__					:    Optional[Any]          =   self.image_processor(images=lowerCamelCase__							,	return_tensors=lowerCamelCase__			)

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

      UpperCamelCase__					:    Optional[int]          =   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=lowerCamelCase__							,	add_special_tokens=lowerCamelCase__							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	max_length=lowerCamelCase__							,	stride=lowerCamelCase__							,	pad_to_multiple_of=lowerCamelCase__							,	return_token_type_ids=lowerCamelCase__							,	return_attention_mask=lowerCamelCase__							,	return_overflowing_tokens=lowerCamelCase__							,	return_special_tokens_mask=lowerCamelCase__							,	return_offsets_mapping=lowerCamelCase__							,	return_length=lowerCamelCase__							,	verbose=lowerCamelCase__							,	return_tensors=lowerCamelCase__							,	**lowerCamelCase__							,	)

      # add pixel values
      UpperCamelCase__					:    Optional[Any]          =   features.pop('''pixel_values'''			)
      if return_overflowing_tokens is True:
       UpperCamelCase__					:    Union[str, Any]          =   self.get_overflowing_images(lowerCamelCase__							,	encoded_inputs['''overflow_to_sample_mapping''']			)
      UpperCamelCase__					:    Tuple          =   images

      return encoded_inputs


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     List[Any]			)  ->      str:





      '''simple docstring'''






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

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

      return images_with_overflow


     def      UpperCAmelCase__	(      self							:     int							,	*lowerCamelCase__							:     Dict							,	**lowerCamelCase__							:     str			)  ->      str:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     List[str]							,	*lowerCamelCase__							:     int							,	**lowerCamelCase__							:     str			)  ->      Tuple:





      '''simple docstring'''






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


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






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


     @property
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






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


     @property
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[int]:





      '''simple docstring'''






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


