
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				):
       # 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 						UpperCamelCase__							(				self				):
       import transformers.commands.transformers_cli

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

       # 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				):
       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=__magic_name__				)

       # 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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				[1 / first_term]
 lowerCamelCase		:     Any          =				1 / first_term
 for _ in range(number_of_terms - 1							):
  first_term += common_difference
  arithmetic_progression.append(lowerCamelCase							)
 lowerCamelCase		:     Optional[Any]          =				[1 / step for step in arithmetic_progression]
 return sum(lowerCamelCase							)


if __name__ == "__main__":
      import doctest

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




