


'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__lowercase     :						Dict	=     logging.get_logger(__name__)

__lowercase     :						Optional[Any]	=     {
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
__lowercase     :						str	=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]       ):
					__a      :	List[str]    =  {}
					with open(_SCREAMING_SNAKE_CASE						,				'r'       ) as file:
										for line_number, line in enumerate(_SCREAMING_SNAKE_CASE       ):
															__a      :	Tuple    =  line.strip()
															if line:
																				__a      :	List[str]    =  line.split()
																				__a      :	Tuple    =  line_number
																				__a      :	Tuple    =  words[0]
																				__a      :	List[Any]    =  value
					return result






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[Any]       ):
					for attribute in key.split('.'       ):
										__a      :	Tuple    =  getattr(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					__a      :	Optional[Any]    =  None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(_SCREAMING_SNAKE_CASE       ):
															__a      :	List[Any]    =  PARAM_MAPPING[full_name.split('.'       )[-1]]
															__a      :	int    =  'param'

					if weight_type is not None and weight_type != "param":
										__a      :	Optional[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ).shape
					elif weight_type is not None and weight_type == "param":
										__a      :	Union[str, Any]    =  hf_pointer
										for attribute in hf_param_name.split('.'       ):
															__a      :	Optional[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										__a      :	Optional[int]    =  shape_pointer.shape

										# let's reduce dimension
										__a      :	str    =  value[0]
					else:
										__a      :	str    =  hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    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      :	Dict    =  value
					elif weight_type == "weight_g":
										__a      :	int    =  value
					elif weight_type == "weight_v":
										__a      :	Tuple    =  value
					elif weight_type == "bias":
										__a      :	Tuple    =  value
					elif weight_type == "param":
										for attribute in hf_param_name.split('.'       ):
															__a      :	List[str]    =  getattr(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										__a      :	Any    =  value
					else:
										__a      :	List[Any]    =  value

					logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					__a      :	Any    =  None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(_SCREAMING_SNAKE_CASE       ):
															__a      :	List[Any]    =  PARAM_MAPPING[full_name.split('.'       )[-1]]
															__a      :	Any    =  'param'

					if weight_type is not None and weight_type != "param":
										__a      :	str    =  '.'.join([key, weight_type]       )
					elif weight_type is not None and weight_type == "param":
										__a      :	Tuple    =  '.'.join([key, hf_param_name]       )
					else:
										__a      :	Optional[Any]    =  key

					__a      :	str    =  value if 'lm_head' in full_key else value[0]


__lowercase     :						Dict	=     {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       List[str]=None						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]=None       ):
					__a      :	Dict    =  False
					for key, mapped_key in MAPPING.items():
										__a      :	Optional[int]    =  'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
										if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
															__a      :	str    =  True
															if "*" in mapped_key:
																				__a      :	Optional[Any]    =  name.split(_SCREAMING_SNAKE_CASE       )[0].split('.'       )[-2]
																				__a      :	List[str]    =  mapped_key.replace('*'						,				_SCREAMING_SNAKE_CASE       )
															if "weight_g" in name:
																				__a      :	int    =  'weight_g'
															elif "weight_v" in name:
																				__a      :	Tuple    =  'weight_v'
															elif "bias" in name:
																				__a      :	Optional[Any]    =  'bias'
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				__a      :	Optional[Any]    =  'weight'
															else:
																				__a      :	List[Any]    =  None
															if hf_dict is not None:
																				rename_dict(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
															else:
																				set_recursively(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
															return is_used
					return is_used






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       Any       ):
					__a      :	Tuple    =  []
					__a      :	Any    =  fairseq_model.state_dict()

					__a      :	str    =  hf_model.wavaveca.feature_extractor

					for name, value in fairseq_dict.items():
										__a      :	List[str]    =  False
										if "conv_layers" in name:
															load_conv_layer(
															    _SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				hf_model.config.feat_extract_norm == 'group'						,				)
															__a      :	Optional[Any]    =  True
										else:
															__a      :	Dict    =  load_wavaveca_layer(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										if not is_used:
															unused_weights.append(_SCREAMING_SNAKE_CASE       )

					logger.warning(F"""Unused weights: {unused_weights}"""       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       Optional[int]						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					__a      :	Dict    =  full_name.split('conv_layers.'       )[-1]
					__a      :	Optional[Any]    =  name.split('.'       )
					__a      :	Optional[int]    =  int(items[0]       )
					__a      :	Union[str, Any]    =  int(items[1]       )

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""       )
															__a      :	Optional[Any]    =  value
															logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""       )
															__a      :	str    =  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:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""       )
															__a      :	Tuple    =  value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    F"""{full_name} has size {value.shape}, but"""
																				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""       )
															__a      :	Any    =  value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
					else:
										unused_weights.append(_SCREAMING_SNAKE_CASE       )






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       Tuple						,				_SCREAMING_SNAKE_CASE				:       Dict=None						,				_SCREAMING_SNAKE_CASE				:       Dict=None						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]=True						,				_SCREAMING_SNAKE_CASE				:       Any=False       ):
					if config_path is not None:
										__a      :	Union[str, Any]    =  WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
					else:
										__a      :	List[str]    =  WavaVecaConfig()

					if is_seq_class:
										__a      :	List[Any]    =  read_txt_into_dict(_SCREAMING_SNAKE_CASE       )
										__a      :	List[str]    =  idalabel
										__a      :	int    =  WavaVecaForSequenceClassification(_SCREAMING_SNAKE_CASE       )
										__a      :	Any    =  WavaVecaFeatureExtractor(
										    feature_size=1						,				sampling_rate=16_000						,				padding_value=0						,				do_normalize=_SCREAMING_SNAKE_CASE						,				return_attention_mask=_SCREAMING_SNAKE_CASE						,				)
										feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE       )

					elif is_finetuned:
										if dict_path:
															__a      :	Optional[Any]    =  Dictionary.load(_SCREAMING_SNAKE_CASE       )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__a      :	Optional[int]    =  target_dict.pad_index
															__a      :	Dict    =  target_dict.bos_index
															__a      :	str    =  target_dict.eos_index
															__a      :	Union[str, Any]    =  len(target_dict.symbols       )
															__a      :	Optional[Any]    =  os.path.join(_SCREAMING_SNAKE_CASE						,				'vocab.json'       )
															if not os.path.isdir(_SCREAMING_SNAKE_CASE       ):
																				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE       )       )
																				return
															os.makedirs(_SCREAMING_SNAKE_CASE						,				exist_ok=_SCREAMING_SNAKE_CASE       )
															__a      :	int    =  target_dict.indices

															# fairseq has the <pad> and <s> switched
															__a      :	Tuple    =  0
															__a      :	Any    =  1
															with open(_SCREAMING_SNAKE_CASE						,				'w'						,				encoding='utf-8'       ) as vocab_handle:
																				json.dump(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
															__a      :	Optional[int]    =  WavaVecaCTCTokenizer(
															    _SCREAMING_SNAKE_CASE						,				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=_SCREAMING_SNAKE_CASE						,				)
															__a      :	Any    =  True if config.feat_extract_norm == 'layer' else False
															__a      :	Optional[Any]    =  WavaVecaFeatureExtractor(
															    feature_size=1						,				sampling_rate=16_000						,				padding_value=0						,				do_normalize=_SCREAMING_SNAKE_CASE						,				return_attention_mask=_SCREAMING_SNAKE_CASE						,				)
															__a      :	Dict    =  WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE						,				tokenizer=_SCREAMING_SNAKE_CASE       )
															processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

										__a      :	Any    =  WavaVecaForCTC(_SCREAMING_SNAKE_CASE       )
					else:
										__a      :	Any    =  WavaVecaForPreTraining(_SCREAMING_SNAKE_CASE       )

					if is_finetuned or is_seq_class:
										__a   ,    __a   ,    __a      :	List[str]    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]						,				arg_overrides={'data': '/'.join(dict_path.split('/'       )[:-1]       )}       )
					else:
										__a      :	Tuple    =  argparse.Namespace(task='audio_pretraining'       )
										__a      :	Optional[Any]    =  fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE       )

										__a   ,    __a   ,    __a      :	List[Any]    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,				task=_SCREAMING_SNAKE_CASE       )

					__a      :	Optional[int]    =  model[0].eval()

					recursively_load_weights(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				not is_finetuned       )

					hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	__lowercase     :						Tuple	=     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'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	__lowercase     :						List[Any]	=     parser.parse_args()

	__lowercase     :						Tuple	=     not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)







'''simple docstring'''



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      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  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							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  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							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  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							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

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

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

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

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

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  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'						,    )



