
"""simple docstring"""
from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

_a   :      Union[str, Any]       =				{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

_a   :      Any       =				datasets.load_dataset("""xnli""", split="""test+validation""")

_a   :      Any       =				0
_a   :      int       =				0
_a   :      Optional[Any]       =				0
_a   :      Dict       =				0
def 					a__ ( a :					Dict							,  a :					Tuple							,  a :					Optional[Any]							,  a :					Optional[int]		):

  """simple docstring"""







  if spm_diff == list(reversed(a		)		):
    # AAA -> AA+A vs A+AA case.
    return True
  elif len(a		) == len(a		) and fast.decode(a		) == fast.decode(a		):
    # Second order OK
    # Barrich -> Barr + ich vs Bar + rich
    return True
  _snake_case  :    Any            = slow.encode(slow.decode(a		)		)
  _snake_case  :    Union[str, Any]            = fast.encode(fast.decode(a		)		)
  if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
    # Type 3 error.
    # Snehagatha ->
    #       Sne, h, aga, th, a
    #       Sne, ha, gat, ha
    # Encoding the wrong with sp does not even recover what spm gave us
    # It fits tokenizer however...
    return True
  return False
def 					a__ ( a :					str							,  a :					str							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = fast.encode_plus(a		)[0]
  _snake_case  :    List[Any]            = enc.offsets
  _snake_case      ,       _snake_case  :    Dict            = offsets[idx], offsets[idx - 1]
  if curr is not None and line[curr[0] : curr[1]] == "\u200f":
    return True
  if prev is not None and line[prev[0] : prev[1]] == "\u200f":
    return True
def 					a__ ( a :					Optional[Any]							,  a :					List[str]							,  a :					List[Any]							,  a :					Optional[Any]							,  a :					Any		):

  """simple docstring"""







  for i, (spm_id, tok_id) in enumerate(zip(a							,  a		)		):
    if spm_id != tok_id:
      break
  _snake_case  :    Any            = i
  for i, (spm_id, tok_id) in enumerate(zip(reversed(a		)							,  reversed(a		)		)		):
    if spm_id != tok_id:
      break
  _snake_case  :    Dict            = len(a		) - i

  _snake_case  :    int            = spm_ids[first:last]
  _snake_case  :    Union[str, Any]            = tok_ids[first:last]

  if check_diff(a							,  a							,  a							,  a		):
    return True

  if check_LTR_mark(a							,  a							,  a		):
    return True

  if last - first > 5:
    # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
    _snake_case  :    str            = Counter(spm_ids[first:last]		)
    _snake_case  :    List[Any]            = Counter(tok_ids[first:last]		)

    _snake_case  :    Optional[Any]            = {spm_ for (spm_, si) in spms.items() if toks.get(spm_							,  0		) == si}
    _snake_case  :    str            = 3
    for i in range(last - first - min_width		):
      if all(spm_ids[first + i + j] in removable_tokens for j in range(a		)		):
        _snake_case  :    Dict            = [
            k
            for k in range(last - first - min_width		)
            if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
        ]
        for j in possible_matches:
          if check_diff(spm_ids[first : first + i]							,  tok_ids[first : first + j]							,  a							,  a		) and check_details(
              a							,  spm_ids[first + i : last]							,  tok_ids[first + j : last]							,  a							,  a							,  ):
            return True

  print(f'Spm: {[fast.decode([spm_ids[i]]		) for i in range(a							,  a		)]}'		)
  try:
    print(f'Tok: {[fast.decode([tok_ids[i]]		) for i in range(a							,  a		)]}'		)
  except Exception:
    pass

  _snake_case  :    str            = fast.decode(spm_ids[:first]		)
  _snake_case  :    Optional[Any]            = fast.decode(spm_ids[last:]		)
  _snake_case  :    Optional[Any]            = fast.decode(spm_ids[first:last]		)
  print()
  print(a		)
  return False
def 					a__ ( a :					str							,  a :					Optional[int]							,  a :					Optional[int]		):

  """simple docstring"""







  global perfect
  global imperfect
  global wrong
  global total

  _snake_case  :    List[str]            = slow.encode(a		)
  _snake_case  :    List[str]            = fast.encode(a		)

  _snake_case  :    List[str]            = False
  total += 1

  if slow_ids != fast_ids:
    if check_details(a							,  a							,  a							,  a							,  a		):
      _snake_case  :    Optional[int]            = True
      imperfect += 1
    else:
      wrong += 1
  else:
    perfect += 1

  if total % 10_000 == 0:
    print(f'({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'		)

  if skip_assert:
    return

  assert (
      slow_ids == fast_ids
  ), f'line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(a		)}\n{fast.tokenize(a		)}'
def 					a__ ( a :					str							,  a :					Any		):

  """simple docstring"""







  global batch_total
  for i in range(len(a		)		):
    # premise, all languages
    for text in dataset[i]["premise"].values():
      test_string(a							,  a							,  a		)

    # hypothesis, all languages
    for text in dataset[i]["hypothesis"]["translation"]:
      test_string(a							,  a							,  a		)


if __name__ == "__main__":
    for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
        _a   :      str       =				list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            _a   :      Optional[Any]       =				0
            _a   :      Tuple       =				0
            _a   :      Dict       =				0
            _a   :      List[str]       =				0

            print(f'========================== Checking {name}: {checkpoint} ==========================')
            _a   :      Union[str, Any]       =				slow_class.from_pretrained(checkpoint, force_download=True)
            _a   :      int       =				fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f'Accuracy {perfect * 100 / total:.2f}')






from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
      import pyximport

      pyximport.install(setup_args={"""include_dirs""": np.get_include()})
      from . import algos_graphormer  # noqa E402






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  int = 512  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						x.shape[1] if len(x.shape  ) > 1 else 1
 __SCREAMING_SNAKE_CASE							:     int							=						1 + np.arange(0		,						feature_num * offset		,						__lowerCamelCase		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						x + feature_offset
 return x






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  int=True  ):



 """simple docstring"""





 requires_backends(__lowerCamelCase		,						["cython"]  )

 if keep_features and "edge_attr" in item.keys():  # edge_attr
  __SCREAMING_SNAKE_CASE							:     List[str]							=						np.asarray(item["edge_attr"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						np.ones((len(item["edge_index"][0]  ), 1)		,						dtype=np.intaa  )  # same embedding for all

 if keep_features and "node_feat" in item.keys():  # input_nodes
  __SCREAMING_SNAKE_CASE							:     Dict							=						np.asarray(item["node_feat"]		,						dtype=np.intaa  )
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						np.ones((item["num_nodes"], 1)		,						dtype=np.intaa  )  # same embedding for all

 __SCREAMING_SNAKE_CASE							:     Any							=						np.asarray(item["edge_index"]		,						dtype=np.intaa  )

 __SCREAMING_SNAKE_CASE							:     Any							=						convert_to_single_emb(__lowerCamelCase  ) + 1
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						item["num_nodes"]

 if len(edge_attr.shape  ) == 1:
  __SCREAMING_SNAKE_CASE							:     Any							=						edge_attr[:, None]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		,						dtype=np.intaa  )
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_to_single_emb(__lowerCamelCase  ) + 1

 # node adj matrix [num_nodes, num_nodes] bool
 __SCREAMING_SNAKE_CASE							:     int							=						np.zeros([num_nodes, num_nodes]		,						dtype=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						True

 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.floyd_warshall(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						np.amax(__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     str							=						algos_graphormer.gen_edge_input(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						np.zeros([num_nodes + 1, num_nodes + 1]		,						dtype=np.single  )  # with graph token

 # combine
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						input_nodes + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     Dict							=						attn_bias
 __SCREAMING_SNAKE_CASE							:     Tuple							=						attn_edge_type
 __SCREAMING_SNAKE_CASE							:     Any							=						shortest_path_result.astype(np.intaa  ) + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     int							=						np.sum(__lowerCamelCase		,						axis=1  ).reshape(-1  ) + 1  # we shift all indices by one for padding
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						item["in_degree"]  # for undirected graph
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						input_edges + 1  # we shift all indices by one for padding
 if "labels" not in item:
  __SCREAMING_SNAKE_CASE							:     Any							=						item["y"]

 return item






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				str  ,	UpperCamelCase			:				List[str]=2_0  ,	UpperCamelCase			:				Dict=False					)->Any:
      if not is_cython_available():
       raise ImportError("Graphormer preprocessing needs Cython (pyximport)"					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						spatial_pos_max
      __SCREAMING_SNAKE_CASE							:     Dict							=						on_the_fly_processing






     def __call__(							self			:				Optional[int]  ,	UpperCamelCase			:				List[dict]					)->Dict[str, Any]:
      if self.on_the_fly_processing:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						[preprocess_item(UpperCamelCase					) for i in features]

      if not isinstance(features[0]  ,	UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						[vars(UpperCamelCase					) for f in features]
      __SCREAMING_SNAKE_CASE							:     str							=						{}

      __SCREAMING_SNAKE_CASE							:     int							=						max(len(i["input_nodes"]					) for i in features					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						len(features[0]["input_nodes"][0]					)
      __SCREAMING_SNAKE_CASE							:     Any							=						len(features[0]["attn_edge_type"][0][0]					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						max(len(i["input_edges"][0][0]					) for i in features					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						len(features[0]["input_edges"][0][0][0]					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						len(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.zeros(UpperCamelCase  ,	max_node_num + 1  ,	max_node_num + 1  ,	dtype=torch.float					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Any							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.zeros(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.zeros(
          UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	dtype=torch.long					)

      for ix, f in enumerate(UpperCamelCase					):
       for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
        __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.tensor(f[k]					)

       if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]					) > 0:
        __SCREAMING_SNAKE_CASE							:     int							=						float("-inf"					)

       __SCREAMING_SNAKE_CASE							:     str							=						f["attn_bias"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f[
           "attn_edge_type"
       ]
       __SCREAMING_SNAKE_CASE							:     Dict							=						f["spatial_pos"]
       __SCREAMING_SNAKE_CASE							:     int							=						f["in_degree"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						f["input_nodes"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						f["input_edges"]

      __SCREAMING_SNAKE_CASE							:     Any							=						batch["in_degree"]

      __SCREAMING_SNAKE_CASE							:     int							=						features[0]["labels"]
      if len(UpperCamelCase					) == 1:  # one task
       if isinstance(sample[0]  ,	UpperCamelCase					):  # regression
        __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
       else:  # binary classification
        __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.from_numpy(np.concatenate([i["labels"] for i in features]					)					)
      else:  # multi task classification, left to float to keep the NaNs
       __SCREAMING_SNAKE_CASE							:     int							=						torch.from_numpy(np.stack([i["labels"] for i in features]  ,	axis=0					)					)

      return batch






