
import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:     List[str]          =				FNetConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:     Any          =				FNetForPreTraining(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[str]          =				checkpoint_dict["""target"""]

 # Embeddings
 # Position IDs
 lowerCamelCase		:     Optional[Any]          =				fnet_pretraining_model.state_dict()

 lowerCamelCase		:     str          =				state_dict["""fnet.embeddings.position_ids"""]
 lowerCamelCase		:     List[str]          =				{"""fnet.embeddings.position_ids""": position_ids}
 # Embedding Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     List[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]							).T
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]							)
 lowerCamelCase		:     str          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     Optional[int]          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]							)

 # Encoder Layers
 for layer in range(config.num_hidden_layers							):
  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Any          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""mixing_layer_norm"""]["""bias"""]							)

  lowerCamelCase		:     int          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]							).T
  lowerCamelCase		:     List[str]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]							)

  lowerCamelCase		:     List[Any]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""kernel"""]							).T
  lowerCamelCase		:     str          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''feed_forward_{layer}''']["""output"""]["""bias"""]							)

  lowerCamelCase		:     Tuple          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]							)
  lowerCamelCase		:     Optional[int]          =				torch.tensor(
      pretrained_model_params["""encoder"""][F'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]							)

 # Pooler Layers
 lowerCamelCase		:     Tuple          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]							).T
 lowerCamelCase		:     List[Any]          =				torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]							)

 # Masked LM Layers
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""kernel"""]							).T
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_dense"""]["""bias"""]							)
 lowerCamelCase		:     Tuple          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""scale"""]							)
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""predictions_layer_norm"""]["""bias"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]							)
 lowerCamelCase		:     Dict          =				torch.tensor(
     pretrained_model_params["""predictions_output"""]["""output_bias"""]							)
 lowerCamelCase		:     Any          =				torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]							)

 # Seq Relationship Layers
 lowerCamelCase		:     int          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_kernel"""]							)
 lowerCamelCase		:     Optional[Any]          =				torch.tensor(
     pretrained_model_params["""classification"""]["""output_bias"""]							)

 # Load State Dict
 fnet_pretraining_model.load_state_dict(lowerCamelCase							)

 # Save PreTrained
 print(F'''Saving pretrained model to {save_path}'''							)
 fnet_pretraining_model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--fnet_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained FNet model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      _lowerCamelCase        =parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)






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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 for attribute in key.split("""."""							):
  lowerCamelCase		:     str          =				getattr(lowerCamelCase,     lowerCamelCase							)

 if weight_type is not None:
  lowerCamelCase		:     Union[str, Any]          =				getattr(lowerCamelCase,     lowerCamelCase							).shape
 else:
  lowerCamelCase		:     Optional[int]          =				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		:     Union[str, Any]          =				value
 elif weight_type == "weight_g":
  lowerCamelCase		:     Optional[Any]          =				value
 elif weight_type == "weight_v":
  lowerCamelCase		:     List[str]          =				value
 elif weight_type == "bias":
  lowerCamelCase		:     Any          =				value
 else:
  lowerCamelCase		:     str          =				value

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





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     List[str]          =				[]
 lowerCamelCase		:     Union[str, Any]          =				fairseq_model.state_dict()

 if not is_headless:
  lowerCamelCase		:     Tuple          =				hf_model.dataavec_audio.feature_extractor
  lowerCamelCase		:     Tuple          =				hf_model.dataavec_audio.encoder.pos_conv_embed

 else:
  lowerCamelCase		:     Tuple          =				hf_model.feature_extractor
  lowerCamelCase		:     int          =				hf_model.encoder.pos_conv_embed

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





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Tuple          =				access_by_string(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[Any]          =				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		:     Dict          =				fsq_value
 logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				full_name.split("""conv_layers."""							)[-1]
 lowerCamelCase		:     int          =				name.split("""."""							)
 lowerCamelCase		:     Any          =				int(items[0]							)
 lowerCamelCase		:     Union[str, Any]          =				int(items[1]							)

 lowerCamelCase		:     Dict          =				name.split("""."""							)[-1]
 if type_id == 0:
  lowerCamelCase		:     Optional[int]          =				"""conv"""
 elif type_id == 2:
  lowerCamelCase		:     Dict          =				"""layer_norm"""
 else:
  unused_weights.append(lowerCamelCase							)
  return

 set_weights(lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Dict          =				full_name.split("""pos_conv."""							)[-1]
 lowerCamelCase		:     Any          =				name.split("""."""							)
 lowerCamelCase		:     List[str]          =				int(items[0]							)
 lowerCamelCase		:     List[str]          =				int(items[1]							)

 lowerCamelCase		:     List[Any]          =				name.split("""."""							)[-1]
 if type_id != 0:
  unused_weights.append(lowerCamelCase							)
  return
 else:
  lowerCamelCase		:     Union[str, Any]          =				"""conv"""

 set_weights(lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     F'''layers.{layer_id}.{layer_type}.{weight_type}'''							)





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase=None,     lowerCamelCase=None,     lowerCamelCase=True							):
 if config_path is not None:
  lowerCamelCase		:     Optional[Any]          =				DataaVecAudioConfig.from_pretrained(lowerCamelCase							)
 else:
  lowerCamelCase		:     Union[str, Any]          =				DataaVecAudioConfig()

 if not is_finetuned:
  # Modify final_proj layer name
  lowerCamelCase		:     Optional[Any]          =				DataaVecAudioModel(lowerCamelCase							)
  lowerCamelCase		:     List[Any]          =				os.path.dirname(lowerCamelCase							)

  lowerCamelCase		:     Union[str, Any]          =				torch.load(lowerCamelCase							)
  lowerCamelCase		:     Optional[int]          =				state_dict["""model"""].pop("""final_proj.0.weight"""							)
  lowerCamelCase		:     Optional[int]          =				state_dict["""model"""].pop("""final_proj.0.bias"""							)
  lowerCamelCase		:     Tuple          =				os.path.join(lowerCamelCase,     """converted.pt"""							)
  torch.save(lowerCamelCase,     lowerCamelCase							)
 else:
  lowerCamelCase		:     Optional[Any]          =				DataaVecAudioForCTC(lowerCamelCase							)
  lowerCamelCase		:     Union[str, Any]          =				checkpoint_path

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

 lowerCamelCase		:     Optional[int]          =				load_dataavec(lowerCamelCase							)

 recursively_load_weights(lowerCamelCase,     lowerCamelCase,     not is_finetuned							)

 lowerCamelCase		:     int          =				WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""							)

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

 lowerCamelCase		:     List[Any]          =				processor(lowerCamelCase,     return_tensors="""pt""",     padding=lowerCamelCase							)

 lowerCamelCase		:     Optional[Any]          =				inputs.input_values
 lowerCamelCase		:     int          =				inputs.attention_mask
 #    input_values = inputs.input_values[:, :-1]
 #    attention_mask = inputs.attention_mask[:, :-1]

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

  lowerCamelCase		:     Any          =				torch.argmax(lowerCamelCase,     dim=-1							)
  lowerCamelCase		:     List[str]          =				processor.batch_decode(lowerCamelCase							)

  print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''							)
 else:
  lowerCamelCase		:     Union[str, Any]          =				model(source=lowerCamelCase,     padding_mask=(1 - attention_mask),     mask=lowerCamelCase,     features_only=lowerCamelCase							)[
      """layer_results"""
  ][-1][0].transpose(0,     1							)
  lowerCamelCase		:     Optional[Any]          =				hf_wavavec(lowerCamelCase,     attention_mask=lowerCamelCase							)["""last_hidden_state"""]

 print(our_output.shape,     their_output.shape							)
 lowerCamelCase		:     str          =				torch.max(torch.abs(our_output - their_output							)							).item()
 print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
 lowerCamelCase		:     int          =				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__":
      _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
      )




