


'''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 					__A					( self				:					Optional[Any]  )							->					List[str]:
										# test transformers-cli env
										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				:					int  )							->					Union[str, Any]:
										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				:					Union[str, Any]  )							->					Optional[int]:
										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				:					Tuple  )							->					Any:
										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      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



