"""simple docstring"""


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       :    int         =		logging.get_logger(__name__)

__UpperCamelCase       :    List[str]         =		{
    "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       :    Union[str, Any]         =		[
    "lm_head",
]


def   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       Optional[Any],				_snake_case		:       List[Any],				_snake_case		:       Dict,				_snake_case		:       Optional[int]       ):
   for attribute in key.split("."       ):
      _lowercase							     =       getattr(_snake_case,				_snake_case       )

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

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


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       List[str],				_snake_case		:       Union[str, Any]       ):
   _lowercase							     =       []
   _lowercase							     =       fairseq_model.state_dict()

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

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

   for name, value in fairseq_dict.items():
      _lowercase							     =       False
      if "conv_layers" in name:
         load_conv_layer(
             _snake_case,				_snake_case,				_snake_case,				_snake_case,				)
         _lowercase							     =       True
      elif "pos_conv" in name:
         load_pos_conv_layer(
             _snake_case,				_snake_case,				_snake_case,				_snake_case,				)
         _lowercase							     =       True
      else:
         for key, mapped_key in MAPPING.items():
            if not is_headless:
               _lowercase							     =       "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]:
               _lowercase							     =       True
               if "*" in mapped_key:
                  _lowercase							     =       name.split(_snake_case       )[0].split("."       )[-2]
                  _lowercase							     =       mapped_key.replace("*",				_snake_case       )
               if "weight_g" in name:
                  _lowercase							     =       "weight_g"
               elif "weight_v" in name:
                  _lowercase							     =       "weight_v"
               elif "bias" in name:
                  _lowercase							     =       "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  _lowercase							     =       "weight"
               else:
                  _lowercase							     =       None
               set_recursively(_snake_case,				_snake_case,				_snake_case,				_snake_case,				_snake_case       )
            continue
      if not is_used:
         unused_weights.append(_snake_case       )

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


def   __UpperCAmelCase     (	_snake_case		:       Dict,				_snake_case		:       str       ):
   _lowercase							     =       path.split("."       )
   return reduce(_snake_case,				_snake_case,				_snake_case       )


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       Dict,				_snake_case		:       Optional[int],				_snake_case		:       Any       ):
   _lowercase							     =       access_by_string(_snake_case,				_snake_case       )
   _lowercase							     =       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."""       )
   _lowercase							     =       fsq_value
   logger.info(f"""{full_name} was correctly initialized from {hf_weight_path}."""       )


def   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       Optional[Any],				_snake_case		:       Union[str, Any],				_snake_case		:       List[str]       ):
   _lowercase							     =       full_name.split("conv_layers."       )[-1]
   _lowercase							     =       name.split("."       )
   _lowercase							     =       int(items[0]       )
   _lowercase							     =       int(items[1]       )

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

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


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       List[Any],				_snake_case		:       Optional[Any],				_snake_case		:       List[Any]       ):
   _lowercase							     =       full_name.split("pos_conv."       )[-1]
   _lowercase							     =       name.split("."       )
   _lowercase							     =       int(items[0]       )
   _lowercase							     =       int(items[1]       )

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

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


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       List[str],				_snake_case		:       Optional[int],				_snake_case		:       List[str]=None,				_snake_case		:       Tuple=None,				_snake_case		:       Dict=True       ):
   if config_path is not None:
      _lowercase							     =       DataaVecAudioConfig.from_pretrained(_snake_case       )
   else:
      _lowercase							     =       DataaVecAudioConfig()

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

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

   def load_dataavec(_snake_case		:       Any       ):
      _lowercase     ,  _lowercase     ,  _lowercase							     =       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
      return model[0].eval()

   _lowercase							     =       load_dataavec(_snake_case       )

   recursively_load_weights(_snake_case,				_snake_case,				not is_finetuned       )

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

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

   _lowercase							     =       processor(_snake_case,				return_tensors="pt",				padding=_snake_case       )

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

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

      _lowercase							     =       torch.argmax(_snake_case,				dim=-1       )
      _lowercase							     =       processor.batch_decode(_snake_case       )

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

   print(our_output.shape,				their_output.shape       )
   _lowercase							     =       torch.max(torch.abs(our_output - their_output       )       ).item()
   print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
   _lowercase							     =       torch.allclose(_snake_case,				_snake_case,				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(_snake_case       )

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


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		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       :    Tuple         =		parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )
"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()