import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :	List[str]							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	int							     =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
SCREAMING_SNAKE_CASE :	Any							     =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A					):



 """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
 elif weight_type == "running_mean":
  __a       =  value
 elif weight_type == "running_var":
  __a       =  value
 elif weight_type == "num_batches_tracked":
  __a       =  value
 elif weight_type == "inv_freq":
  __a       =  value
 else:
  __a       =  value

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



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  []
 __a       =  fairseq_model.state_dict()

 __a       =  hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  __a       =  False
  if "conv_layers" in name:
   load_conv_layer(
       _A							,				_A							,				_A							,				_A							,				hf_model.config.feat_extract_norm == "group"							,				)
   __a       =  True
  else:
   for key, mapped_key in MAPPING.items():
    __a       =  "wav2vec2_conformer." + 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 "pos_bias_u" in name:
      __a       =  None
     elif "pos_bias_v" in name:
      __a       =  None
     elif "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"
     elif "running_mean" in name:
      __a       =  "running_mean"
     elif "inv_freq" in name:
      __a       =  "inv_freq"
     elif "running_var" in name:
      __a       =  "running_var"
     elif "num_batches_tracked" in name:
      __a       =  "num_batches_tracked"
     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   __A		(			_A							,				_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


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

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""					)
   __a       =  value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""					)
   __a       =  value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""					)
   __a       =  value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        f"""{full_name} has size {value.shape}, but"""
        f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""					)
   __a       =  value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
 else:
  unused_weights.append(_A					)



@torch.no_grad()
def   __A		(			_A							,				_A							,				_A=None							,				_A=None							,				_A=True					):



 """simple docstring"""


 if config_path is not None:
  __a       =  WavaVecaConformerConfig.from_pretrained(_A							,				hidden_act="swish"					)
 else:
  __a       =  WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  __a       =  "rotary"

 if is_finetuned:
  if dict_path:
   __a       =  Dictionary.load(_A					)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   __a       =  target_dict.pad_index
   __a       =  target_dict.bos_index
   __a       =  target_dict.eos_index
   __a       =  len(target_dict.symbols					)
   __a       =  os.path.join(_A							,				"vocab.json"					)
   if not os.path.isdir(_A					):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_A					)					)
    return
   os.makedirs(_A							,				exist_ok=_A					)
   __a       =  target_dict.indices

   # fairseq has the <pad> and <s> switched
   __a       =  0
   __a       =  1
   with open(_A							,				"w"							,				encoding="utf-8"					) as vocab_handle:
    json.dump(_A							,				_A					)
   __a       =  WavaVecaCTCTokenizer(
       _A							,				unk_token=target_dict.unk_word							,				pad_token=target_dict.pad_word							,				bos_token=target_dict.bos_word							,				eos_token=target_dict.eos_word							,				word_delimiter_token="|"							,				do_lower_case=_A							,				)
   __a       =  True if config.feat_extract_norm == "layer" else False
   __a       =  WavaVecaFeatureExtractor(
       feature_size=1							,				sampling_rate=1_6000							,				padding_value=0							,				do_normalize=_A							,				return_attention_mask=_A							,				)
   __a       =  WavaVecaProcessor(feature_extractor=_A							,				tokenizer=_A					)
   processor.save_pretrained(_A					)

  __a       =  WavaVecaConformerForCTC(_A					)
 else:
  __a       =  WavaVecaConformerForPreTraining(_A					)

 if is_finetuned:
  __a       ,     __a       ,     __a       =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]							,				arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
 else:
  __a       =  argparse.Namespace(task="audio_pretraining"					)
  __a       =  fairseq.tasks.setup_task(_A					)

  __a       ,     __a       ,     __a       =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,				task=_A					)

 __a       =  model[0].eval()

 recursively_load_weights(_A							,				_A							,				not is_finetuned					)

 hf_wavavec.save_pretrained(_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	List[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"""
  )
  SCREAMING_SNAKE_CASE :	str							     =		parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


