






'''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 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			@patch("""sys.argv"""							,					["""fakeprogrampath""", """env"""]						)
			def UpperCamelCase__ (							self					:			Tuple						):





					"""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					:			Tuple						):





					"""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 UpperCamelCase__ (							self					:			Optional[int]						):





					"""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 UpperCamelCase__ (							self					:			Tuple						):





					"""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 __future__ import annotations


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int | float] ):
		if len(_lowerCamelCase ) == 0:
				raise ValueError("""find_min() arg is an empty sequence""" )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		nums[0]
		for num in nums:
				__SCREAMING_SNAKE_CASE	:		int   	=		min(_lowerCamelCase	,				_lowerCamelCase )
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)