




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()
_lowerCamelCase			 =						logging.get_logger(__name__)

_lowerCamelCase			 =						{
    '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',
}
_lowerCamelCase			 =						[
    'lm_head',
]




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						Dict       ,     __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						Optional[int]       ,     __UpperCamelCase			:						int      )  ->							Tuple:


    for attribute in key.split('''.'''      ):
        UpperCAmelCase_							 =     getattr(lowercase_       ,     lowercase_      )

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

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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Optional[int]       ,     __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						Optional[int]      )  ->							Dict:
    UpperCAmelCase_							 =     []
    UpperCAmelCase_							 =     fairseq_model.state_dict()

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

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

    for name, value in fairseq_dict.items():
        UpperCAmelCase_							 =     False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     )
            UpperCAmelCase_							 =     True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     )
            UpperCAmelCase_							 =     True
        else:
            for key, mapped_key in MAPPING.items():
                if not is_headless:
                    UpperCAmelCase_							 =     '''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]:
                    UpperCAmelCase_							 =     True
                    if "*" in mapped_key:
                        UpperCAmelCase_							 =     name.split(lowercase_      )[0].split('''.'''      )[-2]
                        UpperCAmelCase_							 =     mapped_key.replace('''*'''       ,     lowercase_      )
                    if "weight_g" in name:
                        UpperCAmelCase_							 =     '''weight_g'''
                    elif "weight_v" in name:
                        UpperCAmelCase_							 =     '''weight_v'''
                    elif "bias" in name:
                        UpperCAmelCase_							 =     '''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCAmelCase_							 =     '''weight'''
                    else:
                        UpperCAmelCase_							 =     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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Any       ,     __UpperCamelCase			:						Dict      )  ->							Tuple:
    UpperCAmelCase_							 =     path.split('''.'''      )
    return reduce(lowercase_       ,     lowercase_       ,     lowercase_      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[str]       ,     __UpperCamelCase			:						Optional[int]       ,     __UpperCamelCase			:						Optional[Any]       ,     __UpperCamelCase			:						str      )  ->							Dict:
    UpperCAmelCase_							 =     access_by_string(lowercase_       ,     lowercase_      )
    UpperCAmelCase_							 =     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.'      )
    UpperCAmelCase_							 =     fsq_value
    logger.info(f'{full_name} was correctly initialized from {hf_weight_path}.'      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						int       ,     __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						Optional[int]      )  ->							Optional[Any]:
    UpperCAmelCase_							 =     full_name.split('''conv_layers.'''      )[-1]
    UpperCAmelCase_							 =     name.split('''.'''      )
    UpperCAmelCase_							 =     int(items[0]      )
    UpperCAmelCase_							 =     int(items[1]      )

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

    set_weights(lowercase_       ,     lowercase_       ,     lowercase_       ,     f'conv_layers.{layer_id}.{layer_type}.{weight_type}'      )




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						Dict       ,     __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						Any      )  ->							Dict:
    UpperCAmelCase_							 =     full_name.split('''pos_conv.'''      )[-1]
    UpperCAmelCase_							 =     name.split('''.'''      )
    UpperCAmelCase_							 =     int(items[0]      )
    UpperCAmelCase_							 =     int(items[1]      )

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

    set_weights(lowercase_       ,     lowercase_       ,     lowercase_       ,     f'layers.{layer_id}.{layer_type}.{weight_type}'      )




@torch.no_grad()
def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str       ,     __UpperCamelCase			:						str       ,     __UpperCamelCase			:						Union[str, Any]=None       ,     __UpperCamelCase			:						Union[str, Any]=None       ,     __UpperCamelCase			:						Any=True      )  ->							List[Any]:


    if config_path is not None:
        UpperCAmelCase_							 =     DataaVecAudioConfig.from_pretrained(lowercase_      )
    else:
        UpperCAmelCase_							 =     DataaVecAudioConfig()

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

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

    def load_dataavec(__UpperCamelCase			:						List[Any]      ):
        UpperCAmelCase_ ,							UpperCAmelCase_ ,							UpperCAmelCase_							 =     fairseq.checkpoint_utils.load_model_ensemble_and_task([path]      )
        return model[0].eval()

    UpperCAmelCase_							 =     load_dataavec(lowercase_      )

    recursively_load_weights(lowercase_       ,     lowercase_       ,     not is_finetuned      )

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

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

    UpperCAmelCase_							 =     processor(lowercase_       ,     return_tensors='''pt'''       ,     padding=lowercase_      )

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

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

        UpperCAmelCase_							 =     torch.argmax(lowercase_       ,     dim=-1      )
        UpperCAmelCase_							 =     processor.batch_decode(lowercase_      )

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

    print(our_output.shape       ,     their_output.shape      )
    UpperCAmelCase_							 =     torch.max(torch.abs(our_output - their_output      )      ).item()
    print(f'max_absolute_diff = {max_absolute_diff}'      )  # ~ 1e-7
    UpperCAmelCase_							 =     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__":
 _lowerCamelCase			 =						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'
 )
 _lowerCamelCase			 =						parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )











def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  0
    for ele in arr:
        total_sum += product_sum(lowercase_    ,   depth + 1					) if isinstance(lowercase_    ,   lowercase_					) else ele
    return total_sum * depth




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return product_sum(lowercase_    ,   1					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






