
import argparse
import os
import re


lowerCamelCase_						: int         =  """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCamelCase_						: str         =  re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
lowerCamelCase_						: Optional[Any]         =  re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase = False						)  ->	List[str]:
				with open(lowerCamelCase     ,    """r"""     ,    encoding="""utf-8"""						) as f:
								UpperCamelCase_:							int	=						f.read()

				UpperCamelCase_:							Union[str, Any]	=						content.split("""\n"""						)
				UpperCamelCase_:							Tuple	=						[]
				UpperCamelCase_:							List[str]	=						0
				while line_idx < len(lowerCamelCase						):
								if _re_intro_mapping.search(lines[line_idx]						) is not None:
												UpperCamelCase_:							Union[str, Any]	=						len(re.search(r"""^(\s*)\S"""     ,    lines[line_idx]						).groups()[0]						) + 8
												# Start of a new mapping!
												while not lines[line_idx].startswith(""" """ * indent + """("""						):
																new_lines.append(lines[line_idx]						)
																line_idx += 1

												UpperCamelCase_:							Tuple	=						[]
												while lines[line_idx].strip() != "]":
																# Blocks either fit in one line or not
																if lines[line_idx].strip() == "(":
																				UpperCamelCase_:							Optional[Any]	=						line_idx
																				while not lines[line_idx].startswith(""" """ * indent + """)"""						):
																								line_idx += 1
																				blocks.append("""\n""".join(lines[start_idx : line_idx + 1]						)						)
																else:
																				blocks.append(lines[line_idx]						)
																line_idx += 1

												# Sort blocks by their identifiers
												UpperCamelCase_:							Tuple	=						sorted(lowerCamelCase     ,    key=lambda lowerCamelCase						: _re_identifier.search(lowerCamelCase						).groups()[0]						)
												new_lines += blocks
								else:
												new_lines.append(lines[line_idx]						)
												line_idx += 1

				if overwrite:
								with open(lowerCamelCase     ,    """w"""     ,    encoding="""utf-8"""						) as f:
												f.write("""\n""".join(lowerCamelCase						)						)
				elif "\n".join(lowerCamelCase						) != content:
								return True







def        A__					(						lowerCamelCase = False						)  ->	Union[str, Any]:
				UpperCamelCase_:							Optional[int]	=						[os.path.join(lowerCamelCase     ,    lowerCamelCase						) for f in os.listdir(lowerCamelCase						) if f.endswith(""".py"""						)]
				UpperCamelCase_:							Dict	=						[sort_auto_mapping(lowerCamelCase     ,    overwrite=lowerCamelCase						) for fname in fnames]

				if not overwrite and any(lowerCamelCase						):
								UpperCamelCase_:							Any	=						[f for f, d in zip(lowerCamelCase     ,    lowerCamelCase						) if d]
								raise ValueError(
								    F'''The following files have auto mappings that need sorting: {", ".join(lowerCamelCase						)}. Run `make style` to fix'''
								    """ this."""						)


if __name__ == "__main__":
							lowerCamelCase_						: Optional[Any]         =  argparse.ArgumentParser()
							parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
							lowerCamelCase_						: str         =  parser.parse_args()

							sort_all_auto_mappings(not args.check_only)








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	return_tensors="""np"""      )

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							self.assertListEqual(
							    processor.model_input_names[2:]						,	feature_extractor.model_input_names						,	msg="""`processor` and `feature_extractor` model input names do not match"""						,	)






