



"""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()

UpperCAmelCase:  Tuple			     =				{
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

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

UpperCAmelCase:  Union[str, Any]			     =				0
UpperCAmelCase:  Optional[Any]			     =				0
UpperCAmelCase:  Optional[int]			     =				0
UpperCAmelCase:  Any			     =				0


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		if spm_diff == list(reversed(__UpperCAmelCase      )      ):
				# AAA -> AA+A vs A+AA case.
				return True
		elif len(__UpperCAmelCase      ) == len(__UpperCAmelCase      ) and fast.decode(__UpperCAmelCase      ) == fast.decode(__UpperCAmelCase      ):
				# Second order OK
				# Barrich -> Barr + ich vs Bar + rich
				return True
		_lowercase				: List[str]     =   slow.encode(slow.decode(__UpperCAmelCase      )      )
		_lowercase				: int     =   fast.encode(fast.decode(__UpperCAmelCase      )      )
		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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: int     =   fast.encode_plus(__UpperCAmelCase      )[0]
		_lowercase				: Optional[int]     =   enc.offsets
		_lowercase     ,  _lowercase				: Optional[Any]     =   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  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		# Encoding can be the same with same result AAA -> A + AA vs AA + A
		# We can check that we use at least exactly the same number of tokens.
		for i, (spm_id, tok_id) in enumerate(zip(__UpperCAmelCase   ,				__UpperCAmelCase      )      ):
				if spm_id != tok_id:
						break
		_lowercase				: Optional[Any]     =   i
		for i, (spm_id, tok_id) in enumerate(zip(reversed(__UpperCAmelCase      )   ,				reversed(__UpperCAmelCase      )      )      ):
				if spm_id != tok_id:
						break
		_lowercase				: Tuple     =   len(__UpperCAmelCase      ) - i

		_lowercase				: List[Any]     =   spm_ids[first:last]
		_lowercase				: Optional[Any]     =   tok_ids[first:last]

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

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

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

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

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

		_lowercase				: Tuple     =   fast.decode(spm_ids[:first]      )
		_lowercase				: Tuple     =   fast.decode(spm_ids[last:]      )
		_lowercase				: List[Any]     =   fast.decode(spm_ids[first:last]      )
		print()
		print(__UpperCAmelCase      )
		return False


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		global perfect
		global imperfect
		global wrong
		global total

		_lowercase				: int     =   slow.encode(__UpperCAmelCase      )
		_lowercase				: Dict     =   fast.encode(__UpperCAmelCase      )

		_lowercase				: int     =   False
		total += 1

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

		if total % 10000 == 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(__UpperCAmelCase      )}\n{fast.tokenize(__UpperCAmelCase      )}"""


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

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


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

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






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






