


import os
from collections.abc import Iterator






def   _lowercase(					__a				:						str = "."     ):
							for dir_path, dir_names, filenames in os.walk(__a     ):
														a__      =[d for d in dir_names if d != 'scripts' and d[0] not in '._']
														for filename in filenames:
																					if filename == "__init__.py":
																												continue
																					if os.path.splitext(__a     )[1] in (".py", ".ipynb"):
																												yield os.path.join(__a    ,     __a     ).lstrip('./'     )






def   _lowercase(					__a				:						Dict     ):
							return f"""{i * '  '}*""" if i else "\n##"






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							a__      =old_path.split(os.sep     )
							for i, new_part in enumerate(new_path.split(os.sep     )     ):
														if (i + 1 > len(__a     ) or old_parts[i] != new_part) and new_part:
																					print(f"""{md_prefix(__a     )} {new_part.replace('_'    ,     ' '     ).title()}"""     )
							return new_path






def   _lowercase(					__a				:						str = "."     ):
							a__      =''
							for filepath in sorted(good_file_paths(__a     )     ):
														a__    ,     a__      =os.path.split(__a     )
														if filepath != old_path:
																					a__      =print_path(__a    ,     __a     )
														a__      =(filepath.count(os.sep     ) + 1) if filepath else 0
														a__      =f"""{filepath}/{filename}""".replace(' '    ,     '%20'     )
														a__      =os.path.splitext(filename.replace('_'    ,     ' '     ).title()     )[0]
														print(f"""{md_prefix(__a     )} [{filename}]({url})"""     )


if __name__ == "__main__":
	print_directory_md('.')





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

