

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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        import transformers.commands.transformers_cli

        shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'	,   ignore_errors=_SCREAMING_SNAKE_CASE   )  # 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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        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=_SCREAMING_SNAKE_CASE   )

        # 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 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        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=_SCREAMING_SNAKE_CASE   )

        # 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 math import factorial

def 				A_					(							a   ,				a					):

      """simple docstring"""



      return factorial(a					) / (factorial(a					) * factorial(n - r					))

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       0

      for i in range(1   ,				1_0_1					):
            for j in range(1   ,				i + 1					):
                  if combinations(a   ,				a					) > 1e6:
                        total += 1
      return total


if __name__ == "__main__":
    print(solution())






