

from typing import Dict, Optional, Tuple

import torch




def        __UpperCamelCase     (      _A					:							torch.Tensor		)				->torch.Tensor:



							"""simple docstring"""
							lowerCamelCase_								=boundaries[1] - boundaries[0]
							lowerCamelCase_								=boundaries + step / 2
							lowerCamelCase_								=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1		)]      ,  dim=0		)
							return bin_centers




def        __UpperCamelCase     (      _A					:							torch.Tensor      ,  _A					:							torch.Tensor      ,  )				->Tuple[torch.Tensor, torch.Tensor]:



							"""simple docstring"""
							lowerCamelCase_								=_calculate_bin_centers(_A		)
							return (
							    torch.sum(aligned_distance_error_probs * bin_centers      ,  dim=-1		),
							    bin_centers[-1],
							)




def        __UpperCamelCase     (      _A					:							torch.Tensor      ,  _A					:							int = 31      ,  _A					:							int = 64      ,  **_A					:							Dict      ,  )				->Dict[str, torch.Tensor]:



							"""simple docstring"""
							lowerCamelCase_								=torch.linspace(0      ,  _A      ,  steps=(no_bins - 1)      ,  device=logits.device		)

							lowerCamelCase_								=torch.nn.functional.softmax(_A      ,  dim=-1		)
							lowerCamelCase_	,   lowerCamelCase_								=_calculate_expected_aligned_error(
							    alignment_confidence_breaks=_A      ,  aligned_distance_error_probs=_A      ,  )

							return {
							    "aligned_confidence_probs": aligned_confidence_probs,
							    "predicted_aligned_error": predicted_aligned_error,
							    "max_predicted_aligned_error": max_predicted_aligned_error,
							}






def        __UpperCamelCase     (      _A					:							torch.Tensor      ,  _A					:							Optional[torch.Tensor] = None      ,  _A					:							int = 31      ,  _A					:							int = 64      ,  _A					:							float = 1E-8      ,  **_A					:							Tuple      ,  )				->torch.Tensor:



							"""simple docstring"""
							if residue_weights is None:
														lowerCamelCase_								=logits.new_ones(logits.shape[-2]		)

							lowerCamelCase_								=torch.linspace(0      ,  _A      ,  steps=(no_bins - 1)      ,  device=logits.device		)

							lowerCamelCase_								=_calculate_bin_centers(_A		)
							torch.sum(_A		)
							lowerCamelCase_								=logits.shape[-2]
							lowerCamelCase_								=max(_A      ,  19		)

							lowerCamelCase_								=1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

							lowerCamelCase_								=torch.nn.functional.softmax(_A      ,  dim=-1		)

							lowerCamelCase_								=1.0 / (1 + (bin_centers**2) / (da**2))
							lowerCamelCase_								=torch.sum(probs * tm_per_bin      ,  dim=-1		)

							lowerCamelCase_								=residue_weights / (eps + residue_weights.sum())
							lowerCamelCase_								=torch.sum(predicted_tm_term * normed_residue_mask      ,  dim=-1		)

							lowerCamelCase_								=per_alignment * residue_weights

							lowerCamelCase_								=(weighted == torch.max(_A		)).nonzero()[0]
							return per_alignment[tuple(_A		)]



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	:   Optional[Any]	=					logging.get_logger(__name__)

__A	:   Optional[int]	=					{
    '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	:   Optional[int]	=					[
    'lm_head',
]




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



							"""simple docstring"""
							for attribute in key.split("""."""		):
														lowerCamelCase_								=getattr(_A      ,  _A		)

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

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




def        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							Dict      ,  _A					:							Optional[Any]		)				->Any:



							"""simple docstring"""
							lowerCamelCase_								=[]
							lowerCamelCase_								=fairseq_model.state_dict()

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

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

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



							"""simple docstring"""
							lowerCamelCase_								=path.split("""."""		)
							return reduce(_A      ,  _A      ,  _A		)




def        __UpperCamelCase     (      _A					:							str      ,  _A					:							List[Any]      ,  _A					:							str      ,  _A					:							Dict		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=access_by_string(_A      ,  _A		)
							lowerCamelCase_								=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.'		)
							lowerCamelCase_								=fsq_value
							logger.info(f'{full_name} was correctly initialized from {hf_weight_path}.'		)




def        __UpperCamelCase     (      _A					:							Union[str, Any]      ,  _A					:							Any      ,  _A					:							List[str]      ,  _A					:							List[Any]		)				->Union[str, Any]:



							"""simple docstring"""
							lowerCamelCase_								=full_name.split("""conv_layers."""		)[-1]
							lowerCamelCase_								=name.split("""."""		)
							lowerCamelCase_								=int(items[0]		)
							lowerCamelCase_								=int(items[1]		)

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

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




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



							"""simple docstring"""
							lowerCamelCase_								=full_name.split("""pos_conv."""		)[-1]
							lowerCamelCase_								=name.split("""."""		)
							lowerCamelCase_								=int(items[0]		)
							lowerCamelCase_								=int(items[1]		)

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

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




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



							"""simple docstring"""
							if config_path is not None:
														lowerCamelCase_								=DataaVecAudioConfig.from_pretrained(_A		)
							else:
														lowerCamelCase_								=DataaVecAudioConfig()

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

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

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

							lowerCamelCase_								=load_dataavec(_A		)

							recursively_load_weights(_A      ,  _A      ,  not is_finetuned		)

							lowerCamelCase_								=WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""		)

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

							lowerCamelCase_								=processor(_A      ,  return_tensors="""pt"""      ,  padding=_A		)

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

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

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

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

							print(our_output.shape      ,  their_output.shape		)
							lowerCamelCase_								=torch.max(torch.abs(our_output - their_output		)		).item()
							print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
							lowerCamelCase_								=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__":
	__A	:   Optional[Any]	=					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	:   List[str]	=					parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)
