





import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch







class 				__lowerCAmelCase       (    UpperCamelCase__):







      @require_torch
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					List[Any]										="\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

        a__	:					Any										="\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

        a__	:					int										="\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n        "

        # Force fetching the files so that we can use the cache
        a__	:					str										="hf-internal-testing/tiny-random-bert"
        BertConfig.from_pretrained(lowerCAmelCase__							)
        BertModel.from_pretrained(lowerCAmelCase__							)
        BertTokenizer.from_pretrained(lowerCAmelCase__							)
        pipeline(task="fill-mask"		,      model=lowerCAmelCase__							)

        # baseline - just load from_pretrained with normal network
        a__	:					List[str]										=[sys.executable, "-c", "\n".join([load, run, mock]							)]

        # should succeed
        a__	:					List[str]										=self.get_env()
        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        a__	:					Union[str, Any]										="1"
        a__	:					List[str]										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      0		,      result.stderr							)
        self.assertIn("success"		,      result.stdout.decode()							)







      @require_torch
      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					List[Any]										="\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

        a__	:					int										="\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

        a__	:					str										="\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

        # Force fetching the files so that we can use the cache
        a__	:					Any										="hf-internal-testing/tiny-random-bert"
        BertConfig.from_pretrained(lowerCAmelCase__							)
        BertModel.from_pretrained(lowerCAmelCase__							)
        BertTokenizer.from_pretrained(lowerCAmelCase__							)
        pipeline(task="fill-mask"		,      model=lowerCAmelCase__							)

        # baseline - just load from_pretrained with normal network
        a__	:					str										=[sys.executable, "-c", "\n".join([load, run, mock]							)]

        # should succeed
        a__	:					Any										=self.get_env()
        a__	:					Dict										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      0		,      result.stderr							)
        self.assertIn("success"		,      result.stdout.decode()							)







      @require_torch
      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										="\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

        a__	:					Optional[Any]										="\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

        a__	:					Optional[int]										="\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

        # baseline - just load from_pretrained with normal network
        a__	:					Any										=[sys.executable, "-c", "\n".join([load, run]							)]

        # should succeed
        a__	:					List[Any]										=self.get_env()
        a__	:					Dict										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      0		,      result.stderr							)
        self.assertIn("success"		,      result.stdout.decode()							)

        # next emulate no network
        a__	:					Optional[int]										=[sys.executable, "-c", "\n".join([load, mock, run]							)]

        # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
        # env["TRANSFORMERS_OFFLINE"] = "0"
        # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
        # self.assertEqual(result.returncode, 1, result.stderr)

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        a__	:					List[Any]										="1"
        a__	:					str										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      0		,      result.stderr							)
        self.assertIn("success"		,      result.stdout.decode()							)







      @require_torch
      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										="\nfrom transformers import pipeline\n        "
        a__	:					Any										="\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

        a__	:					Tuple										="\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
        a__	:					Any										=self.get_env()
        a__	:					List[Any]										="1"
        a__	:					Tuple										=[sys.executable, "-c", "\n".join([load, mock, run]							)]
        a__	:					Any										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      1		,      result.stderr							)
        self.assertIn(
            "You cannot infer task automatically within `pipeline` when using offline mode"		,      result.stderr.decode().replace("\n"		,      ""							)		,      )







      @require_torch
      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Dict										="\nfrom transformers import AutoModel\n        "
        a__	:					str										="\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

        # baseline - just load from_pretrained with normal network
        a__	:					List[str]										=[sys.executable, "-c", "\n".join([load, run]							)]

        # should succeed
        a__	:					str										=self.get_env()
        a__	:					Optional[int]										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      0		,      result.stderr							)
        self.assertIn("success"		,      result.stdout.decode()							)

        # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
        a__	:					int										="1"
        a__	:					List[str]										=subprocess.run(lowerCAmelCase__		,      env=lowerCAmelCase__		,      check=lowerCAmelCase__		,      capture_output=lowerCAmelCase__							)
        self.assertEqual(result.returncode		,      0		,      result.stderr							)
        self.assertIn("success"		,      result.stdout.decode()							)













def      _A       (		SCREAMING_SNAKE_CASE     :   int = 50						):





  """simple docstring"""



  a__	:					Any										=[1] * (length + 1)

  for row_length in range(3       ,       length + 1						):
    for block_length in range(3       ,       row_length + 1						):
      for block_start in range(row_length - block_length						):
        ways_number[row_length] += ways_number[
            row_length - block_start - block_length - 1
        ]

      ways_number[row_length] += 1

  return ways_number[length]


if __name__ == "__main__":
     print(F"""{solution() = }""")






