




import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_snake_case            = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  List[str]    =		test_results.split(""" """  )

      _A							:  Optional[int]    =		0
      _A							:  Tuple    =		0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      _A							:  int    =		expressions[-2] if """=""" in expressions[-1] else expressions[-1]

      for i, expression in enumerate(snake_case_  ):
            if "failed" in expression:
                  failed += int(expressions[i - 1]  )
            if "passed" in expression:
                  success += int(expressions[i - 1]  )

      return failed, success, time_spent







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Dict    =		{}
      _A							:  Optional[Any]    =		None
      _A							:  Union[str, Any]    =		False
      for line in failures_short_lines.split("""\n"""  ):
            if re.search(r"""_ \[doctest\]""",snake_case_  ):
                  _A							:  int    =		True
                  _A							:  Optional[int]    =		line.split(""" """  )[2]
            elif in_error and not line.split(""" """  )[0].isdigit():
                  _A							:  int    =		line
                  _A							:  Dict    =		False

      return failures







class 		lowercase							:

     def __init__(						self	,  _a	,  _a       )		->							str:
           _A							:  Dict    =		title

           _A							:  Dict    =		doc_test_results["""time_spent"""].split(""","""       )[0]
           _A							:  Optional[Any]    =		doc_test_results["""success"""]
           _A							:  List[Any]    =		doc_test_results["""failures"""]
           _A							:  Any    =		self.n_success + self.n_failures

           # Failures and success of the modeling tests
           _A							:  List[str]    =		doc_test_results

     @property
     def       a__			(						self       )		->							str:
           _A							:  str    =		[self._time_spent]
           _A							:  List[Any]    =		0

           for time in time_spent:
                 _A							:  Optional[int]    =		time.split(""":"""       )

                 # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
                 if len(_a       ) == 1:
                       _A							:  List[str]    =		[0, 0, time_parts[0]]

                 _A    ,     _A    ,     _A							:  Any    =		int(time_parts[0]       ), int(time_parts[1]       ), float(time_parts[2]       )
                 total_secs += hours * 3600 + minutes * 60 + seconds

           _A    ,     _A    ,     _A							:  Optional[Any]    =		total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
           return F'''{int(_a       )}h{int(_a       )}m{int(_a       )}s'''

     @property
     def       a__			(						self       )		->							Dict:
           return {"type": "header", "text": {"type": "plain_text", "text": self.title}}

     @property
     def       a__			(						self       )		->							Dict:
           return {
               "type": "section",
               "text": {
                   "type": "plain_text",
                   "text": F'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
                   "emoji": True,
               },
               "accessory": {
                   "type": "button",
                   "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                   "url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
               },
           }

     @property
     def       a__			(						self       )		->							Dict:
           return {
               "type": "section",
               "text": {
                   "type": "plain_text",
                   "text": (
                       F'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
                       F''' {self.time}.'''
                   ),
                   "emoji": True,
               },
               "accessory": {
                   "type": "button",
                   "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                   "url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
               },
           }

     @property
     def       a__			(						self       )		->							Dict:
           _A							:  Tuple    =		40
           _A							:  Optional[Any]    =		{k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(_a	,  _a       )}

           _A							:  Tuple    =		""""""
           for category, failures in category_failures.items():
                 if len(_a       ) == 0:
                       continue

                 if report != "":
                       report += "\n\n"

                 report += F'''*{category} failures*:'''.ljust(line_length // 2       ).rjust(line_length // 2       ) + "\n"
                 report += "`"
                 report += "`\n`".join(_a       )
                 report += "`"

           return {
               "type": "section",
               "text": {
                   "type": "mrkdwn",
                   "text": F'''The following examples had failures:\n\n\n{report}\n''',
               },
           }

     @property
     def       a__			(						self       )		->							str:
           _A							:  Tuple    =		[self.header]

           if self.n_failures > 0:
                 blocks.append(self.failures       )

           if self.n_failures > 0:
                 blocks.extend([self.category_failures]       )

           if self.n_failures == 0:
                 blocks.append(self.no_failures       )

           return json.dumps(_a       )

     @staticmethod
     def       a__			(						)		->							List[str]:
           _A							:  Tuple    =		[
               {
                   """type""": """section""",
                   """text""": {
                       """type""": """plain_text""",
                       """text""": """There was an issue running the tests.""",
                   },
                   """accessory""": {
                       """type""": """button""",
                       """text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
                       """url""": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}''',
                   },
               }
           ]

           print("""Sending the following payload"""       )
           print(json.dumps({"""blocks""": json.loads(_a       )}       )       )

           client.chat_postMessage(
               channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]	,  text="""There was an issue running the tests."""	,  blocks=_a	,  )

     def       a__			(						self       )		->							Dict:
           print("""Sending the following payload"""       )
           print(json.dumps({"""blocks""": json.loads(self.payload       )}       )       )

           _A							:  Tuple    =		F'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else """All tests passed."""

           _A							:  List[str]    =		client.chat_postMessage(
               channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]	,  blocks=self.payload	,  text=_a	,  )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a       )		->							Tuple:
           _A							:  Dict    =		""""""
           for key, value in failures.items():
                 _A							:  List[str]    =		value[:200] + """ [Truncated]""" if len(_a       ) > 250 else value
                 failures_text += F'''*{key}*\n_{value}_\n\n'''

           _A							:  Any    =		job_name
           _A							:  str    =		{"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

           if job_link is not None:
                 _A							:  List[str]    =		{
                     """type""": """button""",
                     """text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
                     """url""": job_link,
                 }

           return [
               {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
               content,
               {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
           ]



     def       a__			(						self       )		->							str:
           if self.thread_ts is None:
                 raise ValueError("""Can only post reply if a post has been made."""       )

           _A							:  int    =		self.doc_test_results.pop("""job_link"""       )
           self.doc_test_results.pop("""failures"""       )
           self.doc_test_results.pop("""success"""       )
           self.doc_test_results.pop("""time_spent"""       )

           _A							:  Tuple    =		sorted(self.doc_test_results.items()	,  key=lambda _a       : t[0]       )
           for job, job_result in sorted_dict:
                 if len(job_result["""failures"""]       ):
                       _A							:  Tuple    =		F'''*Num failures* :{len(job_result["failed"]       )} \n'''
                       _A							:  Any    =		job_result["""failures"""]
                       _A							:  Tuple    =		self.get_reply_blocks(_a	,  _a	,  _a	,  text=_a       )

                       print("""Sending the following reply"""       )
                       print(json.dumps({"""blocks""": blocks}       )       )

                       client.chat_postMessage(
                           channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""]	,  text=F'''Results for {job}'''	,  blocks=_a	,  thread_ts=self.thread_ts["""ts"""]	,  )

                       time.sleep(1       )







def        lowerCAmelCase_			(  ):
      _A							:  Optional[int]    =		os.environ["""GITHUB_RUN_ID"""]
      _A							:  Tuple    =		f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
      _A							:  Tuple    =		requests.get(snake_case_  ).json()
      _A							:  str    =		{}

      try:
            jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}  )
            _A							:  List[str]    =		math.ceil((result["""total_count"""] - 100) / 100  )

            for i in range(snake_case_  ):
                  _A							:  Optional[Any]    =		requests.get(url + f'''&page={i + 2}'''  ).json()
                  jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}  )

            return jobs
      except Exception as e:
            print("""Unknown error, could not fetch links.""",snake_case_  )

      return {}







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Union[str, Any]    =		{}

      if os.path.exists(snake_case_  ):
            _A							:  Optional[Any]    =		os.listdir(snake_case_  )
            for file in files:
                  try:
                        with open(os.path.join(snake_case_,snake_case_  ),encoding="""utf-8"""  ) as f:
                              _A							:  int    =		f.read()
                  except UnicodeDecodeError as e:
                        raise ValueError(f'''Could not open {os.path.join(snake_case_,snake_case_  )}.'''  ) from e

      return _artifact







def        lowerCAmelCase_			(  ):







      class 		lowercase							:

           def __init__(						self	,  _a       )		->							Optional[Any]:
                 _A							:  str    =		name
                 _A							:  Any    =		[]

           def __str__(						self       )		->							Optional[Any]:
                 return self.name



           def       a__			(						self	,  _a       )		->							Tuple:
                 self.paths.append({"""name""": self.name, """path""": path}       )


      _A							:  Dict[str, Artifact]    =		{}

      _A							:  Optional[int]    =		filter(os.path.isdir,os.listdir()  )
      for directory in directories:
            _A							:  Optional[int]    =		directory
            if artifact_name not in _available_artifacts:
                  _A							:  Optional[int]    =		Artifact(snake_case_  )

                  _available_artifacts[artifact_name].add_path(snake_case_  )

      return _available_artifacts


if __name__ == "__main__":
  _snake_case            = get_job_links()
  _snake_case            = retrieve_available_artifacts()

  _snake_case            = collections.OrderedDict(
      [
          ("*.py", "API Examples"),
          ("*.md", "MD Examples"),
      ]
  )

  # This dict will contain all the information relative to each doc test category:
  # - failed: list of failed tests
  # - failures: dict in the format 'test': 'error_message'
  _snake_case            = {
      v: {
          "failed": [],
          "failures": {},
      }
      for v in docs.values()
  }

  # Link to the GitHub Action job
  _snake_case            = github_actions_job_links.get("run_doctests")

  _snake_case            = available_artifacts["doc_tests_gpu_test_reports"].paths[0]
  _snake_case            = retrieve_artifact(artifact_path["name"])
  if "stats" in artifact:
    _snake_case      , _snake_case      , _snake_case            = handle_test_results(artifact["stats"])
    _snake_case            = failed
    _snake_case            = success
    _snake_case            = time_spent[1:-1] + ", "

    _snake_case            = extract_first_line_failure(artifact["failures_short"])
    for line in artifact["summary_short"].split("\n"):
      if re.search("FAILED", line):
        _snake_case            = line.replace("FAILED ", "")
        _snake_case            = line.split()[0].replace("\n", "")

        if "::" in line:
          _snake_case      , _snake_case            = line.split("::")
        else:
          _snake_case      , _snake_case            = line, line

        for file_regex in docs.keys():
          if fnmatch(file_path, file_regex):
            _snake_case            = docs[file_regex]
            doc_test_results[category]["failed"].append(test)

            _snake_case            = all_failures[test] if test in all_failures else "N/A"
            _snake_case            = failure
            break

  _snake_case            = Message("🤗 Results of the doc tests.", doc_test_results)
  message.post()
  message.post_reply()







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case            = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							int:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  List[Any]    =		PegasusTokenizer(_a       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							int:
           return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		"""</s>"""
           _A							:  Dict    =		1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a       )	,  _a       )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a       )	,  _a       )

     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]	,  """<pad>"""       )
           self.assertEqual(vocab_keys[1]	,  """</s>"""       )
           self.assertEqual(vocab_keys[-1]	,  """v"""       )
           self.assertEqual(len(_a       )	,  1103       )

     def       a__			(						self       )		->							Optional[int]:
           self.assertEqual(self.get_tokenizer().vocab_size	,  1103       )

     def       a__			(						self       )		->							Tuple:
           _A							:  Any    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Optional[int]    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  int    =		(
               """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
               """ </s> <pad> <pad> <pad>"""
           )
           _A							:  Optional[int]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  List[Any]    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._large_tokenizer
           # <mask_1> masks whole sentence while <mask_2> masks single word
           _A							:  Optional[int]    =		"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
           _A							:  Union[str, Any]    =		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Union[str, Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		self._large_tokenizer
           # The tracebacks for the following asserts are **better** without messages or self.assertEqual
           assert tokenizer.vocab_size == 9_6103
           assert tokenizer.pad_token_id == 0
           assert tokenizer.eos_token_id == 1
           assert tokenizer.offset == 103
           assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
           assert tokenizer.unk_token == "<unk>"
           assert tokenizer.model_max_length == 1024
           _A							:  Any    =		"""To ensure a smooth flow of bank resolutions."""
           _A							:  Optional[int]    =		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Optional[Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )
           assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

     @require_torch
     def       a__			(						self       )		->							List[str]:
           _A							:  Union[str, Any]    =		["""This is going to be way too long.""" * 150, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Union[str, Any]    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  Tuple    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 1024)
           assert batch.attention_mask.shape == (2, 1024)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     @slow
     def       a__			(						self       )		->							Optional[Any]:
           # fmt: off
           _A							:  List[Any]    =		{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=_a	,  model_name="""google/bigbird-pegasus-large-arxiv"""	,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,  )







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  Tuple    =		PegasusTokenizer(_a	,  offset=0	,  mask_token_sent=_a	,  mask_token="""[MASK]"""       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							Optional[Any]:
           return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[str]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							List[Any]:
           _A							:  List[Any]    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		(
               """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
               """ <pad> <pad> <pad>"""
           )
           _A							:  Optional[Any]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  int    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     @require_torch
     def       a__			(						self       )		->							Optional[int]:
           _A							:  Tuple    =		["""This is going to be way too long.""" * 1000, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Tuple    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  str    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 4096)
           assert batch.attention_mask.shape == (2, 4096)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		(
               """This is an example string that is used to test the original TF implementation against the HF"""
               """ implementation"""
           )

           _A							:  Any    =		self._large_tokenizer(_a       ).input_ids

           self.assertListEqual(
               _a	,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]	,  )

