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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "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",
}
UpperCAmelCase							=       [
    "lm_head",
]






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: str ,	lowerCAmelCase_: int ,	lowerCAmelCase_: Dict ,	lowerCAmelCase_: Union[str, Any] ,	lowerCAmelCase_: Union[str, Any]     ):
      for attribute in key.split("."     ):
            snake_case_     :			str       =       getattr(lowerCAmelCase_ ,	lowerCAmelCase_     )

      if weight_type is not None:
            snake_case_     :			Any       =       getattr(lowerCAmelCase_ ,	lowerCAmelCase_     ).shape
      else:
            snake_case_     :			Tuple       =       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":
            snake_case_     :			Dict       =       value
      elif weight_type == "weight_g":
            snake_case_     :			Optional[Any]       =       value
      elif weight_type == "weight_v":
            snake_case_     :			str       =       value
      elif weight_type == "bias":
            snake_case_     :			List[str]       =       value
      else:
            snake_case_     :			Dict       =       value

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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: List[str]     ):
      snake_case_     :			Optional[int]       =       []
      snake_case_     :			List[Any]       =       fairseq_model.state_dict()

      if not is_headless:
            snake_case_     :			int       =       hf_model.dataavec_audio.feature_extractor
            snake_case_     :			str       =       hf_model.dataavec_audio.encoder.pos_conv_embed

      else:
            snake_case_     :			Any       =       hf_model.feature_extractor
            snake_case_     :			List[Any]       =       hf_model.encoder.pos_conv_embed

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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Union[str, Any] ,	lowerCAmelCase_: List[Any]     ):
      snake_case_     :			List[Any]       =       access_by_string(lowerCAmelCase_ ,	lowerCAmelCase_     )
      snake_case_     :			int       =       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."     )
      snake_case_     :			str       =       fsq_value
      logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[str] ,	lowerCAmelCase_: str ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Optional[Any]     ):
      snake_case_     :			Any       =       full_name.split("conv_layers."     )[-1]
      snake_case_     :			Tuple       =       name.split("."     )
      snake_case_     :			Union[str, Any]       =       int(items[0]     )
      snake_case_     :			Optional[int]       =       int(items[1]     )

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

      set_weights(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	f"conv_layers.{layer_id}.{layer_type}.{weight_type}"     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: str ,	lowerCAmelCase_: Any     ):
      snake_case_     :			Any       =       full_name.split("pos_conv."     )[-1]
      snake_case_     :			Tuple       =       name.split("."     )
      snake_case_     :			List[str]       =       int(items[0]     )
      snake_case_     :			List[Any]       =       int(items[1]     )

      snake_case_     :			Any       =       name.split("."     )[-1]
      if type_id != 0:
            unused_weights.append(lowerCAmelCase_     )
            return
      else:
            snake_case_     :			int       =       "conv"

      set_weights(lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_ ,	f"layers.{layer_id}.{layer_type}.{weight_type}"     )






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Any ,	lowerCAmelCase_: Dict=None ,	lowerCAmelCase_: List[str]=None ,	lowerCAmelCase_: Union[str, Any]=True     ):
      if config_path is not None:
            snake_case_     :			Union[str, Any]       =       DataaVecAudioConfig.from_pretrained(lowerCAmelCase_     )
      else:
            snake_case_     :			List[Any]       =       DataaVecAudioConfig()

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

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

      def load_dataavec(lowerCAmelCase_: Optional[Any]     ):
            snake_case_  ,snake_case_  ,snake_case_     :			Union[str, Any]       =       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]     )
            return model[0].eval()

      snake_case_     :			Optional[Any]       =       load_dataavec(lowerCAmelCase_     )

      recursively_load_weights(lowerCAmelCase_ ,	lowerCAmelCase_ ,	not is_finetuned     )

      snake_case_     :			str       =       WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"     )

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

      snake_case_     :			Tuple       =       processor(lowerCAmelCase_ ,	return_tensors="pt" ,	padding=lowerCAmelCase_     )

      snake_case_     :			str       =       inputs.input_values
      snake_case_     :			Optional[int]       =       inputs.attention_mask
      #    input_values = inputs.input_values[:, :-1]
      #    attention_mask = inputs.attention_mask[:, :-1]

      hf_wavavec.eval()
      model.eval()
      if is_finetuned:
            snake_case_     :			Union[str, Any]       =       model(source=lowerCAmelCase_ ,	padding_mask=(1 - attention_mask) ,	mask=lowerCAmelCase_ ,	features_only=lowerCAmelCase_     )[
                "encoder_out"
            ].transpose(0 ,	1     )
            snake_case_     :			Any       =       hf_wavavec(lowerCAmelCase_ ,	attention_mask=lowerCAmelCase_     )["logits"]

            snake_case_     :			Optional[int]       =       torch.argmax(lowerCAmelCase_ ,	dim=-1     )
            snake_case_     :			List[str]       =       processor.batch_decode(lowerCAmelCase_     )

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

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




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}



class 					snake_case__       (				_UpperCamelCase							):
   _SCREAMING_SNAKE_CASE	: List[str]						    =      "plbart"
   _SCREAMING_SNAKE_CASE	: Union[str, Any]						    =      ["past_key_values"]
   _SCREAMING_SNAKE_CASE	: Dict						    =      {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(				self   :   Dict			,	A__   :   Any=5_00_05			,	A__   :   Optional[int]=10_24			,	A__   :   Tuple=6			,	A__   :   Optional[Any]=30_72			,	A__   :   Dict=12			,	A__   :   Union[str, Any]=6			,	A__   :   Union[str, Any]=30_72			,	A__   :   Tuple=12			,	A__   :   List[str]=0.0			,	A__   :   Dict=0.0			,	A__   :   int=True			,	A__   :   Tuple=True			,	A__   :   List[str]="gelu"			,	A__   :   List[Any]=7_68			,	A__   :   Optional[Any]=0.1			,	A__   :   List[Any]=0.1			,	A__   :   Any=0.0			,	A__   :   int=0.02			,	A__   :   Optional[Any]=0.0			,	A__   :   List[str]=True			,	A__   :   Any=1			,	A__   :   Tuple=0			,	A__   :   Any=2			,	A__   :   int=2			,	**A__   :   Dict			,	)							->     int:




         '''simple docstring'''




         snake_case_     :			int       =       vocab_size
         snake_case_     :			List[Any]       =       max_position_embeddings
         snake_case_     :			str       =       d_model
         snake_case_     :			Tuple       =       encoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       encoder_layers
         snake_case_     :			Dict       =       encoder_attention_heads
         snake_case_     :			Union[str, Any]       =       decoder_ffn_dim
         snake_case_     :			Union[str, Any]       =       decoder_layers
         snake_case_     :			List[str]       =       decoder_attention_heads
         snake_case_     :			List[Any]       =       dropout
         snake_case_     :			Union[str, Any]       =       attention_dropout
         snake_case_     :			Optional[int]       =       activation_dropout
         snake_case_     :			List[str]       =       activation_function
         snake_case_     :			Tuple       =       init_std
         snake_case_     :			str       =       encoder_layerdrop
         snake_case_     :			int       =       decoder_layerdrop
         snake_case_     :			Optional[int]       =       classifier_dropout
         snake_case_     :			List[str]       =       use_cache
         snake_case_     :			List[Any]       =       encoder_layers
         snake_case_     :			Tuple       =       scale_embedding  # scale factor will be sqrt(d_model) if True
         super().__init__(
             pad_token_id=A__			,	bos_token_id=A__			,	eos_token_id=A__			,	is_encoder_decoder=A__			,	forced_eos_token_id=A__			,	**A__			,	)



class 					snake_case__       (				_UpperCamelCase							):



   @property
   def 					UpperCAmelCase__       (				self   :   str   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         return OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "sequence"}),
                 ("attention_mask", {0: "batch", 1: "sequence"}),
             ]   )



   @property
   def 					UpperCAmelCase__       (				self   :   List[str]   )							->     Mapping[str, Mapping[int, str]]:




         '''simple docstring'''




         if self.use_past:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("past_keys", {0: "batch", 2: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )
         else:
               return OrderedDict(
                   [
                       ("last_hidden_state", {0: "batch", 1: "sequence"}),
                       ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
                   ]   )



