

"""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()

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

_SCREAMING_SNAKE_CASE       :  List[str]        = datasets.load_dataset("""xnli""", split="""test+validation""")

_SCREAMING_SNAKE_CASE       :  Optional[int]        = 0
_SCREAMING_SNAKE_CASE       :  List[str]        = 0
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = 0
_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = 0



def        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							str      ,							UpperCAmelCase     :							List[Any]      ,							UpperCAmelCase     :							List[str]				):


     '''simple docstring'''






     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
     UpperCamelCase__		:    Dict         =slow.encode(slow.decode(UpperCAmelCase				)				)
     UpperCamelCase__		:    str         =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        _lowerCAmelCase      (						UpperCAmelCase     :							Dict      ,							UpperCAmelCase     :							Union[str, Any]      ,							UpperCAmelCase     :							List[Any]				):


     '''simple docstring'''






     UpperCamelCase__		:    Optional[int]         =fast.encode_plus(UpperCAmelCase				)[0]
     UpperCamelCase__		:    Dict         =enc.offsets
     UpperCamelCase__		:    str         =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        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							Any      ,							UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Dict				):


     '''simple docstring'''






     for i, (spm_id, tok_id) in enumerate(zip(UpperCAmelCase      ,							UpperCAmelCase				)				):
          if spm_id != tok_id:
               break
     UpperCamelCase__		:    Union[str, Any]         =i
     for i, (spm_id, tok_id) in enumerate(zip(reversed(UpperCAmelCase				)      ,							reversed(UpperCAmelCase				)				)				):
          if spm_id != tok_id:
               break
     UpperCamelCase__		:    Optional[Any]         =len(UpperCAmelCase				) - i

     UpperCamelCase__		:    Tuple         =spm_ids[first:last]
     UpperCamelCase__		:    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
          UpperCamelCase__		:    Optional[Any]         =Counter(spm_ids[first:last]				)
          UpperCamelCase__		:    int         =Counter(tok_ids[first:last]				)

          UpperCamelCase__		:    int         ={spm_ for (spm_, si) in spms.items() if toks.get(spm_      ,							0				) == si}
          UpperCamelCase__		:    Tuple         =3
          for i in range(last - first - min_width				):
               if all(spm_ids[first + i + j] in removable_tokens for j in range(UpperCAmelCase				)				):
                    UpperCamelCase__		:    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

     UpperCamelCase__		:    Optional[Any]         =fast.decode(spm_ids[:first]				)
     UpperCamelCase__		:    int         =fast.decode(spm_ids[last:]				)
     UpperCamelCase__		:    List[str]         =fast.decode(spm_ids[first:last]				)
     print()
     print(UpperCAmelCase				)
     return False



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							List[str]				):


     '''simple docstring'''






     global perfect
     global imperfect
     global wrong
     global total

     UpperCamelCase__		:    str         =slow.encode(UpperCAmelCase				)
     UpperCamelCase__		:    Tuple         =fast.encode(UpperCAmelCase				)

     UpperCamelCase__		:    Optional[int]         =False
     total += 1

     if slow_ids != fast_ids:
          if check_details(UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase				):
               UpperCamelCase__		:    Any         =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(UpperCAmelCase				)}\n{fast.tokenize(UpperCAmelCase				)}'''



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							Union[str, Any]				):


     '''simple docstring'''






     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():
        _SCREAMING_SNAKE_CASE       :  Optional[Any]        = list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            _SCREAMING_SNAKE_CASE       :  List[str]        = 0
            _SCREAMING_SNAKE_CASE       :  Optional[int]        = 0
            _SCREAMING_SNAKE_CASE       :  Union[str, Any]        = 0
            _SCREAMING_SNAKE_CASE       :  List[Any]        = 0

            print(F'''========================== Checking {name}: {checkpoint} ==========================''')
            _SCREAMING_SNAKE_CASE       :  Any        = slow_class.from_pretrained(checkpoint, force_download=True)
            _SCREAMING_SNAKE_CASE       :  Optional[Any]        = fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(F'''Accuracy {perfect * 1_0_0 / total:.2f}''')





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


