





import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch

class 		__a							(	unittest.TestCase      ):



       @patch("""sys.argv"""					,			["""fakeprogrampath""", """env"""]			)
       def  A							(     self	:   Optional[Any]			):
           # test transformers-cli env
           import transformers.commands.transformers_cli

           with CaptureStd() as cs:
               transformers.commands.transformers_cli.main()
           self.assertIn("""Python version"""					,			cs.out			)
           self.assertIn("""Platform"""					,			cs.out			)
           self.assertIn("""Using distributed or parallel set-up in script?"""					,			cs.out			)



       @is_pt_tf_cross_test
       @patch(
           """sys.argv"""					,			["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]			)
       def  A							(     self	:   Optional[int]			):
           import transformers.commands.transformers_cli

           shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""					,			ignore_errors=UpperCAmelCase			)  # cleans potential past runs
           transformers.commands.transformers_cli.main()

           # The original repo has no TF weights -- if they exist, they were created by the CLI
           self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""			)			)



       @require_torch
       @patch("""sys.argv"""					,			["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]			)
       def  A							(     self	:   str			):
           import transformers.commands.transformers_cli

           # # remove any previously downloaded model to start clean
           shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""					,			ignore_errors=UpperCAmelCase			)

           # run the command
           transformers.commands.transformers_cli.main()

           # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
           self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""			)			)
           self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""			)			)
           self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""			)			)



       @require_torch
       @patch(
           """sys.argv"""					,			[
               """fakeprogrampath""",
               """download""",
               """hf-internal-testing/test_dynamic_model_with_tokenizer""",
               """--trust-remote-code""",
               """--cache-dir""",
               """/tmp""",
           ]					,			)
       def  A							(     self	:   Optional[int]			):
           import transformers.commands.transformers_cli

           # # remove any previously downloaded model to start clean
           shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""					,			ignore_errors=UpperCAmelCase			)

           # run the command
           transformers.commands.transformers_cli.main()

           # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
           self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""			)			)
           self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""			)			)
           self.assertTrue(
               os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""			)			)









from __future__ import annotations

import math
from itertools import permutations
def 			__UpperCamelCase				(			lowercase__	:       int		)	->      bool:
    '''simple docstring'''





    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5		,    int(math.sqrt(lowercase__		) + 1		)		,    6		):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True
def 			__UpperCamelCase				(			lowercase__	:       int = 7		)	->      int:
    '''simple docstring'''





    lowerCAmelCase_					:	str						=   """""".join(str(lowercase__		) for i in range(1		,    n + 1		)		)
    lowerCAmelCase_					:	Any						=   [int("""""".join(lowercase__		)		) for i in permutations(lowercase__		,    lowercase__		)]
    lowerCAmelCase_					:	List[Any]						=   [num for num in perm_list if is_prime(lowercase__		)]
    return max(lowercase__		) if pandigitals else 0


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


