





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    __snake_case	(							unittest.TestCase      ):





							'''simple docstring'''







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




							def 							UpperCAmelCase__			(  self			:   Optional[int]							,    **A			:   Optional[int]		):
									return RobertaTokenizer.from_pretrained(self.checkpoint							,    **A		)




							def 							UpperCAmelCase__			(  self			:   List[str]							,    **A			:   Optional[int]		):
									return ClapFeatureExtractor.from_pretrained(self.checkpoint							,    **A		)




							def 							UpperCAmelCase__			(  self			:   Optional[int]		):
									shutil.rmtree(self.tmpdirname		)




							def 							UpperCAmelCase__			(  self			:   Optional[int]		):
									__snake_case:						int			  =						self.get_tokenizer()
									__snake_case:						Tuple			  =						self.get_feature_extractor()

									__snake_case:						Union[str, Any]			  =						ClapProcessor(tokenizer=A							,    feature_extractor=A		)

									processor.save_pretrained(self.tmpdirname		)
									__snake_case:						int			  =						ClapProcessor.from_pretrained(self.tmpdirname		)

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

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




							def 							UpperCAmelCase__			(  self			:   Tuple		):
									__snake_case:						Union[str, Any]			  =						ClapProcessor(tokenizer=self.get_tokenizer()							,    feature_extractor=self.get_feature_extractor()		)
									processor.save_pretrained(self.tmpdirname		)

									__snake_case:						List[Any]			  =						self.get_tokenizer(bos_token="""(BOS)"""							,    eos_token="""(EOS)"""		)
									__snake_case:						Any			  =						self.get_feature_extractor(do_normalize=A							,    padding_value=1.0		)

									__snake_case:						Optional[Any]			  =						ClapProcessor.from_pretrained(
									    self.tmpdirname							,    bos_token="""(BOS)"""							,    eos_token="""(EOS)"""							,    do_normalize=A							,    padding_value=1.0		)

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

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




							def 							UpperCAmelCase__			(  self			:   List[Any]		):
									__snake_case:						Dict			  =						self.get_feature_extractor()
									__snake_case:						int			  =						self.get_tokenizer()

									__snake_case:						Any			  =						ClapProcessor(tokenizer=A							,    feature_extractor=A		)

									__snake_case:						Optional[int]			  =						floats_list((3, 1_000)		)

									__snake_case:						Tuple			  =						feature_extractor(A							,    return_tensors="""np"""		)
									__snake_case:						List[str]			  =						processor(audios=A							,    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 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									__snake_case:						List[str]			  =						self.get_feature_extractor()
									__snake_case:						List[Any]			  =						self.get_tokenizer()

									__snake_case:						Optional[Any]			  =						ClapProcessor(tokenizer=A							,    feature_extractor=A		)

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

									__snake_case:						Tuple			  =						processor(text=A		)

									__snake_case:						Optional[int]			  =						tokenizer(A		)

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




							def 							UpperCAmelCase__			(  self			:   List[str]		):
									__snake_case:						str			  =						self.get_feature_extractor()
									__snake_case:						int			  =						self.get_tokenizer()

									__snake_case:						Dict			  =						ClapProcessor(tokenizer=A							,    feature_extractor=A		)

									__snake_case:						Any			  =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									__snake_case:						List[str]			  =						processor.batch_decode(A		)
									__snake_case:						Any			  =						tokenizer.batch_decode(A		)

									self.assertListEqual(A							,    A		)






							def 							UpperCAmelCase__			(  self			:   List[str]		):
									__snake_case:						Union[str, Any]			  =						self.get_feature_extractor()
									__snake_case:						Optional[Any]			  =						self.get_tokenizer()

									__snake_case:						List[str]			  =						ClapProcessor(tokenizer=A							,    feature_extractor=A		)

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













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						float(
        d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creates a copy of the matrix with swapped positions of the elements
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 3
      and len(matrix[0]) == 3
      and len(matrix[1]) == 3
      and len(matrix[2]) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    __snake_case:						Any			  =						float(
        (
            (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
            + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
            + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
        )
        - (
            (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
            + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
            + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
        ))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creating cofactor matrix
    __snake_case:						Tuple			  =						[
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")






