



'''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 _A  (    unittest.TestCase    ):





		@patch("""sys.argv""" ,       ["""fakeprogrampath""", """env"""]				)
		def 							__A       (     self				)       ->     int:

					'''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 							__A       (     self				)       ->     List[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 							__A       (     self				)       ->     Dict:

					'''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 							__A       (     self				)       ->     str:

					'''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"""				)				)









'''simple docstring'''
from itertools import permutations


def   lowercase_	(   lowerCAmelCase__	:  int = 5 ):

			"""simple docstring"""





			if gon_side < 3 or gon_side > 5:
						raise ValueError("""gon_side must be in the range [3, 5]""" )

			# Since it's 16, we know 10 is on the outer ring
			# Put the big numbers at the end so that they are never the first number
			__UpperCAmelCase   :      int										=     list(range(gon_side + 1			,							0			,							-1 ) )
			__UpperCAmelCase   :      int										=     list(range(gon_side + 2			,							gon_side * 2 + 1 ) )

			for perm in permutations(small_numbers + big_numbers ):
						__UpperCAmelCase   :      Optional[int]										=     generate_gon_ring(lowerCAmelCase__			,							list(lowerCAmelCase__ ) )
						if is_magic_gon(lowerCAmelCase__ ):
									return int("""""".join(str(lowerCAmelCase__ ) for n in numbers ) )

			__UpperCAmelCase   :      Optional[int]										=     f'Magic {gon_side}-gon ring is impossible'
			raise ValueError(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  int			,							lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     [0] * (gon_side * 3)
			__UpperCAmelCase   :      Optional[Any]										=     perm[0:3]
			perm.append(perm[1] )

			__UpperCAmelCase   :      Union[str, Any]										=     1 if gon_side < 5 else 2

			for i in range(1			,							len(lowerCAmelCase__ ) // 3 + magic_number ):
						__UpperCAmelCase   :      List[str]										=     perm[2 * i + 1]
						__UpperCAmelCase   :      Optional[int]										=     result[3 * i - 1]
						__UpperCAmelCase   :      Optional[int]										=     perm[2 * i + 2]

			return result


def   lowercase_	(   lowerCAmelCase__	:  list[int] ):

			"""simple docstring"""





			if len(lowerCAmelCase__ ) % 3 != 0:
						raise ValueError("""a gon ring should have a length that is a multiple of 3""" )

			if min(numbers[::3] ) != numbers[0]:
						return False

			__UpperCAmelCase   :      Optional[int]										=     sum(numbers[:3] )

			return all(sum(numbers[i : i + 3] ) == total for i in range(3			,							len(lowerCAmelCase__ )			,							3 ) )


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




