





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


_UpperCAmelCase       : Tuple			     =       logging.get_logger(__name__)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  str:
    try:
        with open(_UpperCAmelCase		,			'rb'				) as flax_state_f:
            lowerCamelCase__					:   List[str]      				=	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  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Dict:

    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
    lowerCamelCase__					:   List[str]      				=	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.'				)
        lowerCamelCase__					:   List[Any]      				=	jax.tree_util.tree_map(
            lambda _UpperCAmelCase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params		,			_UpperCAmelCase				)

    lowerCamelCase__					:   Optional[Any]      				=	''

    lowerCamelCase__					:   List[str]      				=	flatten_dict(_UpperCAmelCase		,			sep='.'				)
    lowerCamelCase__					:   Union[str, Any]      				=	pt_model.state_dict()

    # keep track of unexpected & missing keys
    lowerCamelCase__					:   Dict      				=	[]
    lowerCamelCase__					:   Tuple      				=	set(pt_model_dict.keys()				)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        lowerCamelCase__					:   Union[str, Any]      				=	flax_key_tuple.split('.'				)

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

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(_UpperCAmelCase				):
                lowerCamelCase__					:   int      				=	(
                    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'				)
                )

        lowerCamelCase__					:   Any      				=	'.'.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
                lowerCamelCase__					:   Union[str, Any]      				=	np.asarray(_UpperCAmelCase				) if not isinstance(_UpperCAmelCase		,			np.ndarray				) else flax_tensor
                lowerCamelCase__					:   Optional[Any]      				=	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
    lowerCamelCase__					:   Union[str, Any]      				=	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      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Optional[int]      				=	'laion/clap-htsat-unfused'
     lowerCamelCase__					:   List[Any]      				=	tempfile.mkdtemp()
 def   A_			(   self						:   Optional[int]    ,  **UpperCAmelCase						:   int			)    ->			Optional[Any]:
     return RobertaTokenizer.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  **UpperCAmelCase						:   Any			)    ->			List[str]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]			)    ->			Dict:
     shutil.rmtree(self.tmpdirname			)
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     lowerCamelCase__					:   Tuple      				=	self.get_tokenizer()
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     processor.save_pretrained(self.tmpdirname			)
     lowerCamelCase__					:   List[Any]      				=	ClapProcessor.from_pretrained(self.tmpdirname			)

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=self.get_tokenizer()    ,  feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase__					:   Dict      				=	self.get_tokenizer(bos_token='(BOS)'    ,  eos_token='(EOS)'			)
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor(do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

     lowerCamelCase__					:   str      				=	ClapProcessor.from_pretrained(
         self.tmpdirname    ,  bos_token='(BOS)'    ,  eos_token='(EOS)'    ,  do_normalize=UpperCAmelCase    ,  padding_value=1.0			)

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,  feature_extractor_add_kwargs.to_json_string()			)
     self.assertIsInstance(processor.feature_extractor    ,  UpperCAmelCase			)
 def   A_			(   self						:   List[Any]			)    ->			int:
     lowerCamelCase__					:   Any      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[Any]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

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

     lowerCamelCase__					:   Tuple      				=	feature_extractor(UpperCAmelCase    ,  return_tensors='np'			)
     lowerCamelCase__					:   Tuple      				=	processor(audios=UpperCAmelCase    ,  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   A_			(   self						:   List[str]			)    ->			Tuple:
     lowerCamelCase__					:   Dict      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[Any]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Any      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   Union[str, Any]      				=	'This is a test string'

     lowerCamelCase__					:   Union[str, Any]      				=	processor(text=UpperCAmelCase			)

     lowerCamelCase__					:   Any      				=	tokenizer(UpperCAmelCase			)

     for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]			)
 def   A_			(   self						:   Optional[int]			)    ->			Tuple:
     lowerCamelCase__					:   Tuple      				=	self.get_feature_extractor()
     lowerCamelCase__					:   List[str]      				=	self.get_tokenizer()

     lowerCamelCase__					:   Optional[int]      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

     lowerCamelCase__					:   int      				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase__					:   List[Any]      				=	processor.batch_decode(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	tokenizer.batch_decode(UpperCAmelCase			)

     self.assertListEqual(UpperCAmelCase    ,  UpperCAmelCase			)







 def   A_			(   self						:   Optional[Any]			)    ->			int:
     lowerCamelCase__					:   str      				=	self.get_feature_extractor()
     lowerCamelCase__					:   str      				=	self.get_tokenizer()

     lowerCamelCase__					:   Dict      				=	ClapProcessor(tokenizer=UpperCAmelCase    ,  feature_extractor=UpperCAmelCase			)

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





