






'''simple docstring'''


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     SCREAMING_SNAKE_CASE						(	unittest.TestCase							):






      @patch("""sys.argv"""	,	["""fakeprogrampath""", """env"""]					)
      def        lowercase_	(   self      :     Optional[Any]					):


           '''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        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           import transformers.commands.transformers_cli

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


           '''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=lowercase__					)

           # 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        lowercase_	(   self      :     Optional[Any]					):


           '''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=lowercase__					)

           # 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"""					)					)






'''simple docstring'''


from .stack import Stack



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       Stack[str]    	=     Stack()
     a_    :       Tuple    	=     {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(UpperCamelCase__							)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 lowerCAmelCase_			:     Any							    =     ['((()))', '((())', '(()))']
 print('Balanced parentheses demonstration:\n')
 for example in examples:
  lowerCAmelCase_			:     Optional[Any]							    =     '' if balanced_parentheses(example) else 'not '
  print(f"{example} is {not_str}balanced")
