

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:					List[Any]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					Optional[int]		 =   {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   int			,a__    :   Union[str, Any]			,a__    :   Union[str, Any]       )					->				str:
	for attribute in key.split("""."""       ):
		__A :			Union[str, Any]		    =			getattr(a__			,a__       )

	if weight_type is not None:
		__A :			Optional[int]		    =			getattr(a__			,a__       ).shape
	else:
		__A :			List[Any]		    =			hf_pointer.shape

	assert hf_shape == value.shape, (
	    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
	    f""" {value.shape} for {full_name}"""
	)

	if weight_type == "weight":
		__A :			Optional[Any]		    =			value
	elif weight_type == "weight_g":
		__A :			List[Any]		    =			value
	elif weight_type == "weight_v":
		__A :			Optional[Any]		    =			value
	elif weight_type == "bias":
		__A :			Dict		    =			value
	else:
		__A :			Any		    =			value

	logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   List[Any]			,a__    :   str       )					->				Optional[Any]:
	__A :			List[Any]		    =			[]
	__A :			str		    =			fairseq_model.state_dict()

	__A :			List[Any]		    =			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__A :			Any		    =			False
		if "conv_layers" in name:
			load_conv_layer(
			    a__			,a__			,a__			,a__			,hf_model.config.feat_extract_norm == """group"""			,)
			__A :			int		    =			True
		else:
			for key, mapped_key in MAPPING.items():
				__A :			Union[str, Any]		    =			"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

				if key in name or (key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0] and not is_finetuned):
					__A :			int		    =			True
					if "*" in mapped_key:
						__A :			List[Any]		    =			name.split(a__       )[0].split("""."""       )[-2]
						__A :			int		    =			mapped_key.replace("""*"""			,a__       )
					if "weight_g" in name:
						__A :			Tuple		    =			"""weight_g"""
					elif "weight_v" in name:
						__A :			Dict		    =			"""weight_v"""
					elif "weight" in name:
						__A :			Any		    =			"""weight"""
					elif "bias" in name:
						__A :			Optional[Any]		    =			"""bias"""
					else:
						__A :			Union[str, Any]		    =			None
					set_recursively(a__			,a__			,a__			,a__			,a__       )
				continue
		if not is_used:
			unused_weights.append(a__       )

	logger.warning(f"""Unused weights: {unused_weights}"""       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]			,a__    :   str			,a__    :   List[Any]       )					->				Union[str, Any]:
	__A :			Any		    =			full_name.split("""conv_layers."""       )[-1]
	__A :			List[str]		    =			name.split("""."""       )
	__A :			Optional[int]		    =			int(items[0]       )
	__A :			List[str]		    =			int(items[1]       )

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			__A :			Union[str, Any]		    =			value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			__A :			int		    =			value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			__A :			Union[str, Any]		    =			value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			__A :			int		    =			value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
	else:
		unused_weights.append(a__       )
@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Tuple			,a__    :   Dict=None			,a__    :   str=None			,a__    :   Optional[Any]=True       )					->				int:
	if config_path is not None:
		__A :			str		    =			HubertConfig.from_pretrained(a__       )
	else:
		__A :			List[str]		    =			HubertConfig()

	if is_finetuned:
		if dict_path:
			__A :			Tuple		    =			Dictionary.load(a__       )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__A :			List[str]		    =			target_dict.pad_index
			__A :			int		    =			target_dict.bos_index
			__A :			List[str]		    =			target_dict.eos_index
			__A :			Optional[int]		    =			len(target_dict.symbols       )
			__A :			str		    =			os.path.join(a__			,"""vocab.json"""       )
			if not os.path.isdir(a__       ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(a__       )       )
				return
			os.makedirs(a__			,exist_ok=a__       )
			with open(a__			,"""w"""			,encoding="""utf-8"""       ) as vocab_handle:
				json.dump(target_dict.indices			,a__       )
			__A :			int		    =			WavaVecaCTCTokenizer(
			    a__			,unk_token=target_dict.unk_word			,pad_token=target_dict.pad_word			,bos_token=target_dict.bos_word			,eos_token=target_dict.eos_word			,word_delimiter_token="""|"""			,do_lower_case=a__			,)
			__A :			Dict		    =			True if config.feat_extract_norm == """layer""" else False
			__A :			Optional[int]		    =			WavaVecaFeatureExtractor(
			    feature_size=1			,sampling_rate=16000			,padding_value=0			,do_normalize=a__			,return_attention_mask=a__			,)
			__A :			Optional[Any]		    =			WavaVecaProcessor(feature_extractor=a__			,tokenizer=a__       )
			processor.save_pretrained(a__       )

		__A :			Dict		    =			HubertForCTC(a__       )
	else:
		__A :			Optional[Any]		    =			HubertModel(a__       )

	if is_finetuned:
		__A    ,				__A    ,				__A :			Optional[Any]		    =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]			,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
	else:
		__A    ,				__A    ,				__A :			Optional[int]		    =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

	__A :			int		    =			model[0].eval()

	recursively_load_weights(a__			,a__			,a__       )

	hf_wavavec.save_pretrained(a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					str		 =   argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument(
			    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			UpperCAmelCase_							:					Optional[int]		 =   parser.parse_args()
			convert_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)







"""simple docstring"""



import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece.model')
UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

UpperCamelCase__          =	'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizer
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizerFast
      UpperCamelCase							:   str           =     True
      UpperCamelCase							:   Tuple           =     True



      def  __snake_case							(   self      ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__	:      int     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'<pad>'
          UpperCAmelCase__	:      Tuple     							=					1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_      )     ,	UpperCamelCase_      )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_      )     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					list(self.get_tokenizer().get_vocab().keys()      )

          self.assertEqual(vocab_keys[0]     ,	'<s>NOTUSED'      )
          self.assertEqual(vocab_keys[1]     ,	'<pad>'      )
          self.assertEqual(vocab_keys[-1]     ,	'<mask>'      )
          self.assertEqual(len(UpperCamelCase_      )     ,	1_004      )



      def  __snake_case							(   self      ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,	1_005      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )
          UpperCAmelCase__	:      str     							=					CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

          UpperCAmelCase__	:      Union[str, Any]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          UpperCAmelCase__	:      List[Any]     							=					tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase__	:      Any     							=					self.get_tokenizer()
          UpperCAmelCase__	:      List[str]     							=					self.get_rust_tokenizer()

          UpperCAmelCase__	:      List[str]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      List[str]     							=					tokenizer.tokenize(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					self.get_rust_tokenizer()
          UpperCAmelCase__	:      Union[str, Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          # fmt: off
          UpperCAmelCase__	:      Any     							=					{'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          UpperCAmelCase__	:      Union[str, Any]     							=					[
              'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
              'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
              'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
              'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
              'telles que la traduction et la synthèse de texte.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=UpperCamelCase_     ,	model_name='camembert-base'     ,	revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'     ,	sequences=UpperCamelCase_     ,	)

