





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


a__      :     Union[str, Any]         =		logging.get_logger(__name__)

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:Dict				)	->				Union[str, Any]:
     '''simple docstring'''


     try:
          with open(_UpperCAmelCase  ,						'''rb'''				) as flax_state_f:
               A_          =       from_bytes(_UpperCAmelCase  ,						flax_state_f.read()				)
     except UnpicklingError as e:
          try:
               with open(_UpperCAmelCase				) as f:
                    if f.read().startswith('''version'''				):
                         raise OSError(
                             '''You seem to have cloned a repository without having git-lfs installed. Please'''
                             ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                             ''' folder you cloned.'''				)
                    else:
                         raise ValueError from e
          except (UnicodeDecodeError, ValueError):
               raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '				)

     return load_flax_weights_in_pytorch_model(_UpperCAmelCase  ,						_UpperCAmelCase				)


def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:Union[str, Any]				)	->				List[Any]:
     '''simple docstring'''


     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
              ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
              ''' instructions.'''				)
          raise

     # check if we have bf16 weights
     A_          =       flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase				: x.dtype == jnp.bfloataa  ,						_UpperCAmelCase				)				).values()
     if any(_UpperCAmelCase				):
          # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

          # and bf16 is not fully supported in PT yet.
          logger.warning(
              '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
              '''before loading those in PyTorch model.'''				)
          A_          =       jax.tree_util.tree_map(
              lambda _UpperCAmelCase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params  ,						_UpperCAmelCase				)

     A_          =       ''''''

     A_          =       flatten_dict(_UpperCAmelCase  ,						sep='''.'''				)
     A_          =       pt_model.state_dict()

     # keep track of unexpected & missing keys
     A_          =       []
     A_          =       set(pt_model_dict.keys()				)

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          A_          =       flax_key_tuple.split('''.'''				)

          if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
               A_          =       flax_key_tuple_array[:-1] + ['''weight''']
               A_          =       jnp.transpose(_UpperCAmelCase  ,						(3, 2, 0, 1)				)
          elif flax_key_tuple_array[-1] == "kernel":
               A_          =       flax_key_tuple_array[:-1] + ['''weight''']
               A_          =       flax_tensor.T
          elif flax_key_tuple_array[-1] == "scale":
               A_          =       flax_key_tuple_array[:-1] + ['''weight''']

          if "time_embedding" not in flax_key_tuple_array:
               for i, flax_key_tuple_string in enumerate(_UpperCAmelCase				):
                    A_          =       (
                        flax_key_tuple_string.replace('''_0'''  ,						'''.0'''				)
                        .replace('''_1'''  ,						'''.1'''				)
                        .replace('''_2'''  ,						'''.2'''				)
                        .replace('''_3'''  ,						'''.3'''				)
                        .replace('''_4'''  ,						'''.4'''				)
                        .replace('''_5'''  ,						'''.5'''				)
                        .replace('''_6'''  ,						'''.6'''				)
                        .replace('''_7'''  ,						'''.7'''				)
                        .replace('''_8'''  ,						'''.8'''				)
                        .replace('''_9'''  ,						'''.9'''				)
                    )

          A_          =       '''.'''.join(_UpperCAmelCase				)

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                        f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'				)
               else:
                    # add weight to pytorch dict
                    A_          =       np.asarray(_UpperCAmelCase				) if not isinstance(_UpperCAmelCase  ,						np.ndarray				) else flax_tensor
                    A_          =       torch.from_numpy(_UpperCAmelCase				)
                    # remove from missing keys
                    missing_keys.remove(_UpperCAmelCase				)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(_UpperCAmelCase				)

     pt_model.load_state_dict(_UpperCAmelCase				)

     # re-transform missing_keys to list
     A_          =       list(_UpperCAmelCase				)

     if len(_UpperCAmelCase				) > 0:
          logger.warning(
              '''Some weights of the Flax model were not used when initializing the PyTorch model'''
              f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
              f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
              ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
              f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
              ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
              ''' FlaxBertForSequenceClassification model).'''				)
     if len(_UpperCAmelCase				) > 0:
          logger.warning(
              f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
              f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
              ''' use it for predictions and inference.'''				)

     return pt_model










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 ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       '''laion/clap-htsat-unfused'''
       A_          =       tempfile.mkdtemp()


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return RobertaTokenizer.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       return ClapFeatureExtractor.from_pretrained(self.checkpoint ,**__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_tokenizer()
       A_          =       self.get_feature_extractor()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       processor.save_pretrained(self.tmpdirname )
       A_          =       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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ClapProcessor(tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() )
       processor.save_pretrained(self.tmpdirname )

       A_          =       self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' )
       A_          =       self.get_feature_extractor(do_normalize=__snake_case ,padding_value=1.0 )

       A_          =       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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       floats_list((3, 1_0_0_0) )

       A_          =       feature_extractor(__snake_case ,return_tensors='''np''' )
       A_          =       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 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       '''This is a test string'''

       A_          =       processor(text=__snake_case )

       A_          =       tokenizer(__snake_case )

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


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       ClapProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case )

       A_          =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       A_          =       processor.batch_decode(__snake_case )
       A_          =       tokenizer.batch_decode(__snake_case )

       self.assertListEqual(__snake_case ,__snake_case )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_feature_extractor()
       A_          =       self.get_tokenizer()

       A_          =       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''' ,)



