'''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      :				Optional[Any] 				=    {
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

_A      :				List[Any] 				=    datasets.load_dataset('''xnli''', split='''test+validation''')

_A      :				Optional[Any] 				=    0
_A      :				Dict 				=    0
_A      :				List[Any] 				=    0
_A      :				str 				=    0







def 					UpperCamelCase_						( snake_case_	:	Union[str, Any]						,		snake_case_	:	int						,		snake_case_	:	List[str]						,		snake_case_	:	List[str]				) ->					Any:






 '''simple docstring'''

 if spm_diff == list(reversed(snake_case_				)				):
  # AAA -> AA+A vs A+AA case.
  return True
 elif len(snake_case_				) == len(snake_case_				) and fast.decode(snake_case_				) == fast.decode(snake_case_				):
  # Second order OK
  # Barrich -> Barr + ich vs Bar + rich
  return True
 __lowerCAmelCase    							=						slow.encode(slow.decode(snake_case_				)				)
 __lowerCAmelCase    							=						fast.encode(fast.decode(snake_case_				)				)
 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 					UpperCamelCase_						( snake_case_	:	Union[str, Any]						,		snake_case_	:	Optional[Any]						,		snake_case_	:	Optional[Any]				) ->					List[Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						fast.encode_plus(snake_case_				)[0]
 __lowerCAmelCase    							=						enc.offsets
 __lowerCAmelCase							,	__lowerCAmelCase    							=						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 					UpperCamelCase_						( snake_case_	:	Dict						,		snake_case_	:	Dict						,		snake_case_	:	int						,		snake_case_	:	Tuple						,		snake_case_	:	int				) ->					Union[str, Any]:






 '''simple docstring'''

 for i, (spm_id, tok_id) in enumerate(zip(snake_case_						,		snake_case_				)				):
  if spm_id != tok_id:
   break
 __lowerCAmelCase    							=						i
 for i, (spm_id, tok_id) in enumerate(zip(reversed(snake_case_				)						,		reversed(snake_case_				)				)				):
  if spm_id != tok_id:
   break
 __lowerCAmelCase    							=						len(snake_case_				) - i

 __lowerCAmelCase    							=						spm_ids[first:last]
 __lowerCAmelCase    							=						tok_ids[first:last]

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

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

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

  __lowerCAmelCase    							=						{spm_ for (spm_, si) in spms.items() if toks.get(spm_						,		0				) == si}
  __lowerCAmelCase    							=						3
  for i in range(last - first - min_width				):
   if all(spm_ids[first + i + j] in removable_tokens for j in range(snake_case_				)				):
    __lowerCAmelCase    							=						[
        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]						,		snake_case_						,		snake_case_				) and check_details(
         snake_case_						,		spm_ids[first + i : last]						,		tok_ids[first + j : last]						,		snake_case_						,		snake_case_						,		):
      return True

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

 __lowerCAmelCase    							=						fast.decode(spm_ids[:first]				)
 __lowerCAmelCase    							=						fast.decode(spm_ids[last:]				)
 __lowerCAmelCase    							=						fast.decode(spm_ids[first:last]				)
 print()
 print(snake_case_				)
 return False







def 					UpperCamelCase_						( snake_case_	:	Any						,		snake_case_	:	List[str]						,		snake_case_	:	Tuple				) ->					Dict:






 '''simple docstring'''

 global perfect
 global imperfect
 global wrong
 global total

 __lowerCAmelCase    							=						slow.encode(snake_case_				)
 __lowerCAmelCase    							=						fast.encode(snake_case_				)

 __lowerCAmelCase    							=						False
 total += 1

 if slow_ids != fast_ids:
  if check_details(snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_				):
   __lowerCAmelCase    							=						True
   imperfect += 1
  else:
   wrong += 1
 else:
  perfect += 1

 if total % 1_00_00 == 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(snake_case_				)}\n{fast.tokenize(snake_case_				)}"""







def 					UpperCamelCase_						( snake_case_	:	Optional[int]						,		snake_case_	:	Union[str, Any]				) ->					Any:






 '''simple docstring'''

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

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


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

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







'''simple docstring'''

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(snake_case_						,		snake_case_						,		snake_case_				)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to a folder where the PyTorch model will be placed.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






