




import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


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

from .logging import get_logger
from .state import PartialState


__lowerCAmelCase		:    Tuple	      =		get_logger(__name__)




def       __magic_name__    (			A		: str,  A		: List[dict],  A		: list,  A		: list,  A		: int,  A		: GradScaler = None,  ):






			'''simple docstring'''







			for i, state in enumerate(A       ):
						a   =      F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
						a   =      os.path.join(A,  A       )
						save(A,  A       )
						logger.info(F"""Model weights saved in {output_model_file}"""       )
			# Optimizer states
			for i, opt in enumerate(A       ):
						a   =      opt.state_dict()
						a   =      F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
						a   =      os.path.join(A,  A       )
						save(A,  A       )
						logger.info(F"""Optimizer state saved in {output_optimizer_file}"""       )
			# Scheduler states
			for i, scheduler in enumerate(A       ):
						a   =      scheduler.state_dict()
						a   =      F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
						a   =      os.path.join(A,  A       )
						save(A,  A       )
						logger.info(F"""Scheduler state saved in {output_scheduler_file}"""       )
			# GradScaler state
			if scaler is not None:
						a   =      scaler.state_dict()
						a   =      os.path.join(A,  A       )
						torch.save(A,  A       )
						logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""       )
			# Random number generator states
			a   =      {}
			a   =      F"""{RNG_STATE_NAME}_{process_index}.pkl"""
			a   =      random.getstate()
			a   =      np.random.get_state()
			a   =      torch.get_rng_state()
			if is_xpu_available():
						a   =      torch.xpu.get_rng_state_all()
			else:
						a   =      torch.cuda.get_rng_state_all()
			if is_tpu_available():
						a   =      xm.get_rng_state()
			a   =      os.path.join(A,  A       )
			torch.save(A,  A       )
			logger.info(F"""Random states saved in {output_states_file}"""       )
			return output_dir




def       __magic_name__    (			A		: List[Any],  A		: List[str],  A		: int,  A		: Union[str, Any],  A		: int,  A		: List[Any]=None,  A		: Any=None,  **A		: Any,  ):






			'''simple docstring'''







			if map_location not in [None, "cpu", "on_device"]:
						raise TypeError(
						    "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"       )
			if map_location is None:
						a   =      "cpu"
			elif map_location == "on_device":
						a   =      PartialState().device
			# Model states
			for i, model in enumerate(A       ):
						a   =      F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
						a   =      os.path.join(A,  A       )
						models[i].load_state_dict(torch.load(A,  map_location=A       ),  **A       )
			logger.info("All model weights loaded successfully"       )

			# Optimizer states
			for i, opt in enumerate(A       ):
						a   =      F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
						a   =      os.path.join(A,  A       )
						a   =      torch.load(A,  map_location=A       )
						optimizers[i].load_state_dict(A       )
			logger.info("All optimizer states loaded successfully"       )

			# Scheduler states
			for i, scheduler in enumerate(A       ):
						a   =      F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
						a   =      os.path.join(A,  A       )
						scheduler.load_state_dict(torch.load(A       )       )
			logger.info("All scheduler states loaded successfully"       )

			# GradScaler state
			if scaler is not None:
						a   =      os.path.join(A,  A       )
						scaler.load_state_dict(torch.load(A       )       )
						logger.info("GradScaler state loaded successfully"       )

			# Random states
			try:
						a   =      torch.load(os.path.join(A,  F"""{RNG_STATE_NAME}_{process_index}.pkl"""       )       )
						random.setstate(states["random_state"]       )
						np.random.set_state(states["numpy_random_seed"]       )
						torch.set_rng_state(states["torch_manual_seed"]       )
						if is_xpu_available():
									torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"]       )
						else:
									torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"]       )
						if is_tpu_available():
									xm.set_rng_state(states["xm_seed"]       )
						logger.info("All random states loaded successfully"       )
			except Exception:
						logger.info("Could not load random states"       )




def       __magic_name__    (			A		: Optional[Any],  A		: List[Any],  A		: int = 0       ):






			'''simple docstring'''







			a   =      Path(A       ) / F"""custom_checkpoint_{index}.pkl"""
			logger.info(F"""Saving the state of {get_pretty_name(A       )} to {save_location}"""       )
			torch.save(obj.state_dict(),  A       )






def       __magic_name__    (			A		: Tuple,  A		: Optional[Any],  A		: int = 0       ):






			'''simple docstring'''







			a   =      F"""{path}/custom_checkpoint_{index}.pkl"""
			logger.info(F"""Loading the state of {get_pretty_name(A       )} from {load_location}"""       )
			obj.load_state_dict(torch.load(A,  map_location="cpu"       )       )












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		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Tuple	      =		{
    '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		:    List[Any]	      =		[
    'lm_head',
]




def       __magic_name__    (			A		: int,  A		: List[Any],  A		: Tuple,  A		: Dict,  A		: List[str]       ):






			'''simple docstring'''







			for attribute in key.split("."       ):
						a   =      getattr(A,  A       )

			if weight_type is not None:
						a   =      getattr(A,  A       ).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       __magic_name__    (			A		: Optional[Any],  A		: int,  A		: Dict       ):






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

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




def       __magic_name__    (			A		: str,  A		: Dict       ):






			'''simple docstring'''







			a   =      path.split("."       )
			return reduce(A,  A,  A       )




def       __magic_name__    (			A		: Optional[int],  A		: Tuple,  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      access_by_string(A,  A       )
			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       __magic_name__    (			A		: Optional[Any],  A		: List[str],  A		: Optional[Any],  A		: Optional[int]       ):






			'''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(A       )
						return

			set_weights(A,  A,  A,  F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )




def       __magic_name__    (			A		: int,  A		: Dict,  A		: List[Any],  A		: Tuple       ):






			'''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(A       )
						return
			else:
						a   =      "conv"

			set_weights(A,  A,  A,  F"""layers.{layer_id}.{layer_type}.{weight_type}"""       )




@torch.no_grad()
def       __magic_name__    (			A		: str,  A		: Dict,  A		: int=None,  A		: Tuple=None,  A		: List[str]=True       ):






			'''simple docstring'''







			if config_path is not None:
						a   =      DataaVecAudioConfig.from_pretrained(A       )
			else:
						a   =      DataaVecAudioConfig()

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

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

			def load_dataavec(A		: Optional[Any]       ):
						a   ,		a   ,		a   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
						return model[0].eval()

			a   =      load_dataavec(A       )

			recursively_load_weights(A,  A,  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(A,  return_tensors="pt",  padding=A       )

			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=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "encoder_out"
						].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["logits"]

						a   =      torch.argmax(A,  dim=-1       )
						a   =      processor.batch_decode(A       )

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

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


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






