





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):

      """simple docstring"""


      @patch("sys.argv"					,  ["fakeprogrampath", "env"]			)
      def 	UpperCAmelCase_   (						self:	str			):
          '''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:	Optional[Any]			):
          '''simple docstring'''


          import transformers.commands.transformers_cli

          shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"					,  ignore_errors=__lowerCamelCase			)  # 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:	int			):
          '''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=__lowerCamelCase			)

          # 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:	int			):
          '''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=__lowerCamelCase			)

          # 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"			)			)








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

