

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()
lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "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",
}
lowercase_									=    [
    "lm_head",
]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      Any       ,     SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Dict )				-> List[str]:





    '''simple docstring'''






    for attribute in key.split('.' ):
        A__        =  getattr(_lowerCAmelCase       ,     _lowerCAmelCase )

    if weight_type is not None:
        A__        =  getattr(_lowerCAmelCase       ,     _lowerCAmelCase ).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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Union[str, Any] )				-> Union[str, Any]:





    '''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(
                _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     )
            A__        =  True
        elif "pos_conv" in name:
            load_pos_conv_layer(
                _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     )
            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(_lowerCAmelCase )[0].split('.' )[-2]
                        A__        =  mapped_key.replace('*'       ,     _lowerCAmelCase )
                    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(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase )
                continue
        if not is_used:
            unused_weights.append(_lowerCAmelCase )

    logger.warning(f'Unused weights: {unused_weights}' )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      int )				-> Optional[int]:





    '''simple docstring'''






    A__        =  path.split('.' )
    return reduce(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      Union[str, Any]       ,     SCREAMING_SNAKE_CASE__     :      Dict )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  access_by_string(_lowerCAmelCase       ,     _lowerCAmelCase )
    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 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Dict:





    '''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(_lowerCAmelCase )
        return

    set_weights(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     f'conv_layers.{layer_id}.{layer_type}.{weight_type}' )
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      List[str] )				-> Optional[int]:





    '''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(_lowerCAmelCase )
        return
    else:
        A__        =  'conv'

    set_weights(_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     f'layers.{layer_id}.{layer_type}.{weight_type}' )
@torch.no_grad()
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      List[str]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__     :      Dict=None       ,     SCREAMING_SNAKE_CASE__     :      Tuple=True )				-> Optional[Any]:





    '''simple docstring'''






    if config_path is not None:
        A__        =  DataaVecAudioConfig.from_pretrained(_lowerCAmelCase )
    else:
        A__        =  DataaVecAudioConfig()

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

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

    def load_dataavec(SCREAMING_SNAKE_CASE__     :      str ):
        A__ ,		A__ ,		A__        =  fairseq.checkpoint_utils.load_model_ensemble_and_task([path] )
        return model[0].eval()

    A__        =  load_dataavec(_lowerCAmelCase )

    recursively_load_weights(_lowerCAmelCase       ,     _lowerCAmelCase       ,     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(_lowerCAmelCase       ,     return_tensors='pt'       ,     padding=_lowerCAmelCase )

    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=_lowerCAmelCase       ,     padding_mask=(1 - attention_mask)       ,     mask=_lowerCAmelCase       ,     features_only=_lowerCAmelCase )[
            'encoder_out'
        ].transpose(0       ,     1 )
        A__        =  hf_wavavec(_lowerCAmelCase       ,     attention_mask=_lowerCAmelCase )['logits']

        A__        =  torch.argmax(_lowerCAmelCase       ,     dim=-1 )
        A__        =  processor.batch_decode(_lowerCAmelCase )

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

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


if __name__ == "__main__":
     lowercase_									=    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"
     )
     lowercase_									=    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 random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: bool = False						):




 """simple docstring"""







 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(_lowerCAmelCase						)
 np.random.seed(_lowerCAmelCase						)
 torch.manual_seed(_lowerCAmelCase						)
 if is_xpu_available():
  torch.xpu.manual_seed_all(_lowerCAmelCase						)
 elif is_npu_available():
  torch.npu.manual_seed_all(_lowerCAmelCase						)
 else:
  torch.cuda.manual_seed_all(_lowerCAmelCase						)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[RNGType] = None    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 if rng_type == RNGType.TORCH:
  UpperCAmelCase__							=    torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  UpperCAmelCase__							=    torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  UpperCAmelCase__							=    torch.tensor(xm.get_rng_state()						)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  UpperCAmelCase__							=    torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  UpperCAmelCase__							=    torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  UpperCAmelCase__							=    generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 UpperCAmelCase__							=    AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  UpperCAmelCase__							=    rng_state.to(xm.xla_device()						)
  xm.collective_broadcast([rng_state]						)
  xm.mark_step()
  UpperCAmelCase__							=    rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  UpperCAmelCase__							=    rng_state.to(state.device						)
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)
  UpperCAmelCase__							=    rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()						)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Union[str, RNGType]]    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 for rng_type in rng_types:
  synchronize_rng_state(RNGType(_lowerCAmelCase						)    ,				generator=_lowerCAmelCase						)





