
"""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}')



"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


_a   :      List[Any]       =				-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class   _UpperCAmelCase					(       unittest.TestCase):
    __lowercase				:			List[str]	=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __lowercase				:			str	=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = SummarizationPipeline(model=snake_case_    ,     tokenizer=snake_case_  )
      return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = summarizer.model

      _snake_case  :    Optional[int]            = summarizer("(CNN)The Palestinian Authority officially became"  )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      _snake_case  :    List[str]            = summarizer(
          "(CNN)The Palestinian Authority officially became "    ,     num_beams=2    ,     min_length=2    ,     max_length=5    ,     )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
      _snake_case  :    Tuple            = [
          "SwitchTransformersConfig",
          "T5Config",
          "LongT5Config",
          "LEDConfig",
          "PegasusXConfig",
          "FSMTConfig",
          "M2M100Config",
          "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
      ]
      if model.config.__class__.__name__ not in model_can_handle_longer_seq:
        # Too long and exception is expected.
        # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
        # the embedding layer.
        if not (
            isinstance(snake_case_    ,     snake_case_  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
          with self.assertRaises(snake_case_  ):
            _snake_case  :    Tuple            = summarizer("This " * 10_00  )
      _snake_case  :    Any            = summarizer("This " * 10_00    ,     truncation=TruncationStrategy.ONLY_FIRST  )
    @require_torch
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="pt"  )
      _snake_case  :    List[Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )
    @require_tf
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="tf"  )
      _snake_case  :    Union[str, Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )


    @require_torch
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = pipeline(task="summarization"    ,     device=snake_case_  )
      _snake_case  :    Any            = (
          " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
          " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
          " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
          " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
          " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
          " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
          " situation in Palestinian territories, paving the way for possible war crimes investigations against"
          " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
          " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
          " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
          " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
          " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
          " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
          " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
          " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
          " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
          " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
          " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
          " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
          " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
          " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
          " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
          " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
          " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
          " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
          " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
          " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
          " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
          " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
          " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
          " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
          " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
          " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
          " will include alleged war crimes committed since June. The International Criminal Court was set up in"
          " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
          " and Faith Karimi contributed to this report."
      )
      _snake_case  :    int            = (
          " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
          " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
          " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
          " says governments seeking to penalize Palestine should end pressure ."
      )
      _snake_case  :    Union[str, Any]            = summarizer(snake_case_  )
      self.assertEqual(result[0]["summary_text"]    ,     snake_case_  )

