

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__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  ['image_processor', 'tokenizer']
   lowerCamelCase__					:      Optional[int]        =  'LayoutLMv2ImageProcessor'
   lowerCamelCase__					:      List[Any]        =  ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



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






     """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.''',       lowercase_,       )
       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__(lowercase_,       lowercase_			)



   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=lowercase_,       return_tensors=lowercase_			)

     # second, apply the tokenizer
     if text is not None and self.image_processor.apply_ocr and text_pair is None:
       if isinstance(lowercase_,       lowercase_			):
         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=lowercase_,       add_special_tokens=lowercase_,       padding=lowercase_,       truncation=lowercase_,       max_length=lowercase_,       stride=lowercase_,       pad_to_multiple_of=lowercase_,       return_token_type_ids=lowercase_,       return_attention_mask=lowercase_,       return_overflowing_tokens=lowercase_,       return_special_tokens_mask=lowercase_,       return_offsets_mapping=lowercase_,       return_length=lowercase_,       verbose=lowercase_,       return_tensors=lowercase_,       **lowercase_,       )

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

     return encoded_inputs



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






     """simple docstring"""






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

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

     return images_with_overflow



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






     """simple docstring"""






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



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






     """simple docstring"""






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



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






     """simple docstring"""






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



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






     """simple docstring"""






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



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






     """simple docstring"""






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



import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  a__    =Dataset.from_dict({'''i''': range(17				)}				)
  a__    =len(_A				)
  a__    =3
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert sum(len(_A				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =3
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert sum(len(list(_A				)				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




@pytest.mark.parametrize('''shards_per_node'''      , [1, 2, 3]				)
def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  def gen(_A :					List[str]				):
    for shard in shards:
      yield from ({"i": i, "shard": shard} for i in range(17				))

  a__    =3
  a__    =shards_per_node * world_size
  a__    ={'''shards''': [F"""shard_{shard_idx}.txt""" for shard_idx in range(_A				)]}
  a__    =IterableDataset.from_generator(_A      , gen_kwargs=_A				)
  a__    =len(list(_A				)				)
  assert full_ds.n_shards == world_size * shards_per_node
  a__    =[
      split_dataset_by_node(_A      , rank=_A      , world_size=_A				) for rank in range(_A				)
  ]
  assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
  assert sum(len(list(_A				)				) for ds in datasets_per_rank				) == full_size
  assert len({tuple(x.values()				) for ds in datasets_per_rank for x in ds}				) == full_size




def      UpperCAmelCase__							(     ):





  '''simple docstring'''







  def gen():
    return ({"i": i} for i in range(17				))

  a__    =2
  a__    =IterableDataset.from_generator(_A				)
  a__    =len(list(_A				)				)

  a__    =split_dataset_by_node(_A      , rank=0      , world_size=_A				).shuffle(seed=42				)
  assert len(list(_A				)				) == 1 + full_size // world_size
  with pytest.raises(_A				):
    split_dataset_by_node(_A      , rank=0      , world_size=_A				).shuffle()

  a__    =split_dataset_by_node(full_ds.shuffle(seed=42				)      , rank=0      , world_size=_A				)
  assert len(list(_A				)				) == 1 + full_size // world_size
  with pytest.raises(_A				):
    split_dataset_by_node(full_ds.shuffle()      , rank=0      , world_size=_A				)




@pytest.mark.parametrize('''streaming'''      , [False, True]				)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''      , reason='''execute_subprocess_async doesn\'t support windows'''				)
@pytest.mark.integration
def      UpperCAmelCase__							(     _A :					Dict				):





  '''simple docstring'''







  a__    =2
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)




@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''      , [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]      , )
@require_torch
@pytest.mark.skipif(os.name == '''nt'''      , reason='''execute_subprocess_async doesn\'t support windows'''				)
@pytest.mark.integration
def      UpperCAmelCase__							(     _A :					List[str]      , _A :					str				):





  '''simple docstring'''







  a__    =True
  a__    =get_torch_dist_unique_port()
  a__    =Path(_A				).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
  a__    =F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
  a__    =F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
  a__    =[sys.executable] + distributed_args + args
  execute_subprocess_async(_A      , env=os.environ.copy()				)
