



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       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	@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=__UpperCAmelCase						)  # 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=__UpperCAmelCase						)

						# 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=__UpperCAmelCase						)

						# 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"""

UpperCamelCase      :							int          =					1_0**8






def 				A		(							snake_case :int						) ->      bool:
 if n % 1_0 == 0:
  return False
 __UpperCamelCase							    =			str(snake_case						)
 return s == s[::-1]






def 				A		(							) ->      int:
 __UpperCamelCase							    =			set()
 __UpperCamelCase							    =			1
 __UpperCamelCase							    =			5
 while sum_squares < LIMIT:
  __UpperCamelCase							    =			first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(snake_case						):
    answer.add(snake_case						)
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  __UpperCamelCase							    =			first_square**2 + (first_square + 1) ** 2

 return sum(snake_case						)


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



