





import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
    raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
    raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
_UpperCAmelCase       : Any			     =       logging.get_logger(__name__)

_UpperCAmelCase       : str			     =       """Hello, World!"""
_UpperCAmelCase       : Optional[int]			     =       """en_XX"""


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   Any      				=	Path('data_bin'				)
    lowerCamelCase__					:   Optional[int]      				=	FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(_UpperCAmelCase				).parent				)		,			checkpoint_file=Path(_UpperCAmelCase				).name		,			_name='xmod_base'		,			arch='xmod_base'		,			task='multilingual_masked_lm'		,			data_name_or_path=str(_UpperCAmelCase				)		,			bpe='sentencepiece'		,			sentencepiece_model=str(Path(_UpperCAmelCase				).parent / 'sentencepiece.bpe.model'				)		,			src_dict=str(data_dir / 'dict.txt'				)		,			)
    xmod.eval()  # disable dropout
    print(_UpperCAmelCase				)

    lowerCamelCase__					:   Optional[int]      				=	xmod.model.encoder.sentence_encoder
    lowerCamelCase__					:   int      				=	XmodConfig(
        vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings		,			hidden_size=xmod.cfg.model.encoder_embed_dim		,			num_hidden_layers=xmod.cfg.model.encoder_layers		,			num_attention_heads=xmod.cfg.model.encoder_attention_heads		,			intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim		,			max_position_embeddings=514		,			type_vocab_size=1		,			layer_norm_eps=1e-5		,			pre_norm=xmod.cfg.model.encoder_normalize_before		,			adapter_reduction_factor=getattr(xmod.cfg.model		,			'bottleneck'		,			2				)		,			adapter_layer_norm=xmod.cfg.model.adapter_layer_norm		,			adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm		,			ln_before_adapter=xmod.cfg.model.ln_before_adapter		,			languages=xmod.cfg.model.languages		,			)
    if classification_head:
        lowerCamelCase__					:   int      				=	xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

    print('Our X-MOD config:'		,			_UpperCAmelCase				)

    lowerCamelCase__					:   Union[str, Any]      				=	XmodForSequenceClassification(_UpperCAmelCase				) if classification_head else XmodForMaskedLM(_UpperCAmelCase				)
    model.eval()

    # Now let's copy all the weights.
    # Embeddings
    lowerCamelCase__					:   List[str]      				=	xmod_sent_encoder.embed_tokens.weight
    lowerCamelCase__					:   Any      				=	xmod_sent_encoder.embed_positions.weight
    lowerCamelCase__					:   List[str]      				=	torch.zeros_like(
        model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

    lowerCamelCase__					:   Dict      				=	xmod_sent_encoder.layernorm_embedding.weight
    lowerCamelCase__					:   Tuple      				=	xmod_sent_encoder.layernorm_embedding.bias

    for i in range(config.num_hidden_layers				):
        # Encoder: start of layer
        lowerCamelCase__					:   Dict      				=	model.roberta.encoder.layer[i]
        lowerCamelCase__					:   List[Any]      				=	xmod_sent_encoder.layers[i]

        # self attention
        lowerCamelCase__					:   Optional[Any]      				=	layer.attention.self
        if not (
            xmod_layer.self_attn.k_proj.weight.data.shape
            == xmod_layer.self_attn.q_proj.weight.data.shape
            == xmod_layer.self_attn.v_proj.weight.data.shape
            == torch.Size((config.hidden_size, config.hidden_size)				)
        ):
            raise AssertionError('Dimensions of self-attention weights do not match.'				)

        lowerCamelCase__					:   Any      				=	xmod_layer.self_attn.q_proj.weight
        lowerCamelCase__					:   str      				=	xmod_layer.self_attn.q_proj.bias
        lowerCamelCase__					:   Union[str, Any]      				=	xmod_layer.self_attn.k_proj.weight
        lowerCamelCase__					:   Tuple      				=	xmod_layer.self_attn.k_proj.bias
        lowerCamelCase__					:   Dict      				=	xmod_layer.self_attn.v_proj.weight
        lowerCamelCase__					:   List[Any]      				=	xmod_layer.self_attn.v_proj.bias

        # self-attention output
        lowerCamelCase__					:   List[str]      				=	layer.attention.output
        if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
            raise AssertionError('Dimensions of self-attention output weights do not match.'				)
        lowerCamelCase__					:   str      				=	xmod_layer.self_attn.out_proj.weight
        lowerCamelCase__					:   Optional[int]      				=	xmod_layer.self_attn.out_proj.bias
        lowerCamelCase__					:   List[Any]      				=	xmod_layer.self_attn_layer_norm.weight
        lowerCamelCase__					:   Dict      				=	xmod_layer.self_attn_layer_norm.bias

        # intermediate
        lowerCamelCase__					:   int      				=	layer.intermediate
        if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('Dimensions of intermediate weights do not match.'				)
        lowerCamelCase__					:   int      				=	xmod_layer.fca.weight
        lowerCamelCase__					:   int      				=	xmod_layer.fca.bias

        # output
        lowerCamelCase__					:   List[str]      				=	layer.output
        if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
            raise AssertionError('Dimensions of feed-forward weights do not match.'				)
        lowerCamelCase__					:   str      				=	xmod_layer.fca.weight
        lowerCamelCase__					:   Optional[Any]      				=	xmod_layer.fca.bias
        lowerCamelCase__					:   List[Any]      				=	xmod_layer.final_layer_norm.weight
        lowerCamelCase__					:   Optional[int]      				=	xmod_layer.final_layer_norm.bias
        if bert_output.adapter_layer_norm is not None:
            lowerCamelCase__					:   Optional[int]      				=	xmod_layer.adapter_layer_norm.weight
            lowerCamelCase__					:   Union[str, Any]      				=	xmod_layer.adapter_layer_norm.bias

        if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
            raise AssertionError('Lists of language adapters do not match.'				)
        for lang_code, adapter in xmod_layer.adapter_modules.items():
            lowerCamelCase__					:   Dict      				=	bert_output.adapter_modules[lang_code]
            lowerCamelCase__					:   str      				=	xmod_layer.adapter_modules[lang_code]
            lowerCamelCase__					:   List[Any]      				=	from_adapter.fca.weight
            lowerCamelCase__					:   Union[str, Any]      				=	from_adapter.fca.bias
            lowerCamelCase__					:   Optional[Any]      				=	from_adapter.fca.weight
            lowerCamelCase__					:   Any      				=	from_adapter.fca.bias

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        lowerCamelCase__					:   List[Any]      				=	xmod_sent_encoder.layer_norm.weight
        lowerCamelCase__					:   List[str]      				=	xmod_sent_encoder.layer_norm.bias

    if classification_head:
        lowerCamelCase__					:   Optional[Any]      				=	xmod.model.classification_heads['mnli'].dense.weight
        lowerCamelCase__					:   Any      				=	xmod.model.classification_heads['mnli'].dense.bias
        lowerCamelCase__					:   Optional[int]      				=	xmod.model.classification_heads['mnli'].out_proj.weight
        lowerCamelCase__					:   Union[str, Any]      				=	xmod.model.classification_heads['mnli'].out_proj.bias
    else:
        # LM Head
        lowerCamelCase__					:   int      				=	xmod.model.encoder.lm_head.dense.weight
        lowerCamelCase__					:   Union[str, Any]      				=	xmod.model.encoder.lm_head.dense.bias
        lowerCamelCase__					:   Optional[int]      				=	xmod.model.encoder.lm_head.layer_norm.weight
        lowerCamelCase__					:   int      				=	xmod.model.encoder.lm_head.layer_norm.bias
        lowerCamelCase__					:   Optional[int]      				=	xmod.model.encoder.lm_head.weight
        lowerCamelCase__					:   str      				=	xmod.model.encoder.lm_head.bias

    # Let's check that we get the same results.
    lowerCamelCase__					:   str      				=	xmod.encode(_UpperCAmelCase				).unsqueeze(0				)  # batch of size 1
    model.roberta.set_default_language(_UpperCAmelCase				)

    lowerCamelCase__					:   Union[str, Any]      				=	model(_UpperCAmelCase				)[0]
    if classification_head:
        lowerCamelCase__					:   Tuple      				=	xmod.model.classification_heads['mnli'](xmod.extract_features(_UpperCAmelCase				)				)
    else:
        lowerCamelCase__					:   Tuple      				=	xmod.model(_UpperCAmelCase		,			lang_id=[SAMPLE_LANGUAGE]				)[0]
    print(our_output.shape		,			their_output.shape				)
    lowerCamelCase__					:   Any      				=	torch.max(torch.abs(our_output - their_output				)				).item()
    print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
    lowerCamelCase__					:   Optional[Any]      				=	torch.allclose(_UpperCAmelCase		,			_UpperCAmelCase		,			atol=1e-3				)
    print('Do both models output the same tensors?'		,			'🔥' if success else '💩'				)
    if not success:
        raise Exception('Something went wRoNg'				)

    Path(_UpperCAmelCase				).mkdir(parents=_UpperCAmelCase		,			exist_ok=_UpperCAmelCase				)
    print(F"""Saving model to {pytorch_dump_folder_path}"""				)
    model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
    _UpperCAmelCase       : str			     =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
    )
    _UpperCAmelCase       : List[str]			     =       parser.parse_args()
    convert_xmod_checkpoint_to_pytorch(
        args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )












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'    ,  )





