






import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__A          = logging.get_logger(__name__)

__A          = {
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    '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',
}
__A          = [
    'lm_head',
]




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					for attribute in key.split('''.'''       ):
										_A									=       getattr(_lowercase    ,	_lowercase       )

					if weight_type is not None:
										_A									=       getattr(_lowercase    ,	_lowercase       ).shape
					else:
										_A									=       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									=       value
					elif weight_type == "weight_g":
										_A									=       value
					elif weight_type == "weight_v":
										_A									=       value
					elif weight_type == "bias":
										_A									=       value
					else:
										_A									=       value

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




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       []
					_A									=       fairseq_model.state_dict()

					if not is_headless:
										_A									=       hf_model.dataavec_audio.feature_extractor
										_A									=       hf_model.dataavec_audio.encoder.pos_conv_embed

					else:
										_A									=       hf_model.feature_extractor
										_A									=       hf_model.encoder.pos_conv_embed

					for name, value in fairseq_dict.items():
										_A									=       False
										if "conv_layers" in name:
															load_conv_layer(
															    _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	)
															_A									=       True
										elif "pos_conv" in name:
															load_pos_conv_layer(
															    _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	)
															_A									=       True
										else:
															for key, mapped_key in MAPPING.items():
																				if not is_headless:
																									_A									=       '''data2vec_audio.''' + 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									=       True
																									if "*" in mapped_key:
																														_A									=       name.split(_lowercase       )[0].split('''.'''       )[-2]
																														_A									=       mapped_key.replace('''*'''    ,	_lowercase       )
																									if "weight_g" in name:
																														_A									=       '''weight_g'''
																									elif "weight_v" in name:
																														_A									=       '''weight_v'''
																									elif "bias" in name:
																														_A									=       '''bias'''
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														_A									=       '''weight'''
																									else:
																														_A									=       None
																									set_recursively(_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       )
																				continue
										if not is_used:
															unused_weights.append(_lowercase       )

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




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       path.split('''.'''       )
					return reduce(_lowercase    ,	_lowercase    ,	_lowercase       )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       access_by_string(_lowercase    ,	_lowercase       )
					_A									=       hf_weight.data

					if fsq_value.shape != hf_value.shape:
										raise ValueError(f"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""       )
					_A									=       fsq_value
					logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""       )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       full_name.split('''conv_layers.'''       )[-1]
					_A									=       name.split('''.'''       )
					_A									=       int(items[0]       )
					_A									=       int(items[1]       )

					_A									=       name.split('''.'''       )[-1]
					if type_id == 0:
										_A									=       '''conv'''
					elif type_id == 2:
										_A									=       '''layer_norm'''
					else:
										unused_weights.append(_lowercase       )
										return

					set_weights(_lowercase    ,	_lowercase    ,	_lowercase    ,	f"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       full_name.split('''pos_conv.'''       )[-1]
					_A									=       name.split('''.'''       )
					_A									=       int(items[0]       )
					_A									=       int(items[1]       )

					_A									=       name.split('''.'''       )[-1]
					if type_id != 0:
										unused_weights.append(_lowercase       )
										return
					else:
										_A									=       '''conv'''

					set_weights(_lowercase    ,	_lowercase    ,	_lowercase    ,	f"""layers.{layer_id}.{layer_type}.{weight_type}"""       )




@torch.no_grad()
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase=None    ,	_lowercase=None    ,	_lowercase=True       ):

					'''simple docstring'''







					if config_path is not None:
										_A									=       DataaVecAudioConfig.from_pretrained(_lowercase       )
					else:
										_A									=       DataaVecAudioConfig()

					if not is_finetuned:
										# Modify final_proj layer name
										_A									=       DataaVecAudioModel(_lowercase       )
										_A									=       os.path.dirname(_lowercase       )

										_A									=       torch.load(_lowercase       )
										_A									=       state_dict['''model'''].pop('''final_proj.0.weight'''       )
										_A									=       state_dict['''model'''].pop('''final_proj.0.bias'''       )
										_A									=       os.path.join(_lowercase    ,	'''converted.pt'''       )
										torch.save(_lowercase    ,	_lowercase       )
					else:
										_A									=       DataaVecAudioForCTC(_lowercase       )
										_A									=       checkpoint_path

					def load_dataavec(_lowercase       ):
										_A							,_A							,_A									=       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
										return model[0].eval()

					_A									=       load_dataavec(_lowercase       )

					recursively_load_weights(_lowercase    ,	_lowercase    ,	not is_finetuned       )

					_A									=       WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''       )

					_A									=       load_dataset('''patrickvonplaten/librispeech_asr_dummy'''    ,	'''clean'''    ,	split='''validation'''       )
					_A									=       [x['''array'''] for x in ds[:4]['''audio''']]

					_A									=       processor(_lowercase    ,	return_tensors='''pt'''    ,	padding=_lowercase       )

					_A									=       inputs.input_values
					_A									=       inputs.attention_mask
					#    input_values = inputs.input_values[:, :-1]
					#    attention_mask = inputs.attention_mask[:, :-1]

					hf_wavavec.eval()
					model.eval()
					if is_finetuned:
										_A									=       model(source=_lowercase    ,	padding_mask=(1 - attention_mask)    ,	mask=_lowercase    ,	features_only=_lowercase       )[
										    '''encoder_out'''
										].transpose(0    ,	1       )
										_A									=       hf_wavavec(_lowercase    ,	attention_mask=_lowercase       )['''logits''']

										_A									=       torch.argmax(_lowercase    ,	dim=-1       )
										_A									=       processor.batch_decode(_lowercase       )

										print(f"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""       )
					else:
										_A									=       model(source=_lowercase    ,	padding_mask=(1 - attention_mask)    ,	mask=_lowercase    ,	features_only=_lowercase       )[
										    '''layer_results'''
										][-1][0].transpose(0    ,	1       )
										_A									=       hf_wavavec(_lowercase    ,	attention_mask=_lowercase       )['''last_hidden_state''']

					print(our_output.shape    ,	their_output.shape       )
					_A									=       torch.max(torch.abs(our_output - their_output       )       ).item()
					print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
					_A									=       torch.allclose(_lowercase    ,	_lowercase    ,	atol=1e-3       )
					print('''Do both models output the same tensors?'''    ,	'''🔥''' if success else '''💩'''       )
					if not success:
										raise Exception('''Something went wRoNg'''       )

					hf_wavavec.save_pretrained(_lowercase       )

					if is_finetuned:
										processor.save_pretrained(_lowercase       )
					else:
										processor.feature_extractor.save_pretrained(_lowercase       )


if __name__ == "__main__":
				__A          = 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'
				)
				__A          = parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_  :	Tuple							  =							logging.get_logger(__name__)

snake_case_  :	int							  =							{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_  :	Dict							  =							{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Optional[Any] ,			_a    :					Tuple=None ,			_a    :					Union[str, Any]=None ,			_a    :					Dict=False ,			_a    :					int=True ,			_a    :					str=False ,			_a    :					int="<s>" ,			_a    :					int="</s>" ,			_a    :					Optional[int]="<unk>" ,			_a    :					Dict="<sep>" ,			_a    :					Optional[int]="<pad>" ,			_a    :					str="<cls>" ,			_a    :					List[Any]="<mask>" ,			_a    :					Optional[Any]=["<eop>", "<eod>"] ,			**_a    :					Any ,			)							->   Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =AddedToken(_a ,			lstrip=_a ,			rstrip=_a     ) if isinstance(_a ,			_a     ) else mask_token

										super().__init__(
										    vocab_file=_a ,			tokenizer_file=_a ,			do_lower_case=_a ,			remove_space=_a ,			keep_accents=_a ,			bos_token=_a ,			eos_token=_a ,			unk_token=_a ,			sep_token=_a ,			pad_token=_a ,			cls_token=_a ,			mask_token=_a ,			additional_special_tokens=_a ,			**_a ,			)

										_SCREAMING_SNAKE_CASE	     =3
										_SCREAMING_SNAKE_CASE	     =do_lower_case
										_SCREAMING_SNAKE_CASE	     =remove_space
										_SCREAMING_SNAKE_CASE	     =keep_accents
										_SCREAMING_SNAKE_CASE	     =vocab_file
										_SCREAMING_SNAKE_CASE	     =False if not self.vocab_file else True

										try:
														import jieba
										except ModuleNotFoundError as error:
														raise error.__class__(
														    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
														    '''See https://pypi.org/project/jieba/ for installation.'''     )
										_SCREAMING_SNAKE_CASE	     =jieba
										_SCREAMING_SNAKE_CASE	     =str.maketrans(''' \n''' ,			'''\u2582\u2583'''     )




						def 							__UpperCamelCase (				self    :					Any ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[int] ,			_a    :					Optional[List[int]] = None     )							->   List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[self.sep_token_id]
										_SCREAMING_SNAKE_CASE	     =[2]

										if token_ids_a is None:
														return len(token_ids_a + sep     ) * [0] + cls_segment_id
										return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id




						def 							__UpperCamelCase (				self    :					str ,			_a    :					str ,			_a    :					Optional[str] = None     )							->   Tuple[str]:


										"""simple docstring"""





										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''     )

										if not os.path.isdir(_a     ):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
														return
										_SCREAMING_SNAKE_CASE	     =os.path.join(
										    _a ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

										if os.path.abspath(self.vocab_file     ) != os.path.abspath(_a     ):
														copyfile(self.vocab_file ,			_a     )

										return (out_vocab_file,)




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					str ,			*_a    :					Optional[int] ,			**_a    :					Any     )							->   int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =[
										    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_a ,			cut_all=_a     )]     )
										    for text in batch_text_or_text_pairs
										]
										return super()._batch_encode_plus(_a ,			*_a ,			**_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			*_a    :					Union[str, Any] ,			**_a    :					Any     )							->   Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =super()._decode(*_a ,			**_a     )
										_SCREAMING_SNAKE_CASE	     =text.replace(''' ''' ,			''''''     ).replace('''\u2582''' ,			''' '''     ).replace('''\u2583''' ,			'''\n'''     )
										return text