





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 		__lowerCamelCase    (   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 		__lowerCamelCase    (   self							):



				'''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 		__lowerCamelCase    (   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=__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 		__lowerCamelCase    (   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=__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'''							)							)










from __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



