import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   ="<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "       )
		_lowercase   =dict(zip(lowerCAmelCase_       ,  range(len(lowerCAmelCase_       )       )       )       )

		_lowercase   ={
		    "pad_token": "<pad>",
		    "unk_token": "<unk>",
		    "bos_token": "<s>",
		    "eos_token": "</s>",
		}
		_lowercase   ={
		    "feature_size": 1,
		    "padding_value": 0.0,
		    "sampling_rate": 16000,
		    "return_attention_mask": False,
		    "do_normalize": True,
		}

		_lowercase   =tempfile.mkdtemp()
		_lowercase   =os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["vocab_file"]       )
		_lowercase   =os.path.join(self.tmpdirname       ,  lowerCAmelCase_       )
		with open(self.vocab_file       ,  "w"       ,  encoding="utf-8"       ) as fp:
			fp.write(json.dumps(lowerCAmelCase_       ) + "\n"       )

		with open(self.feature_extraction_file       ,  "w"       ,  encoding="utf-8"       ) as fp:
			fp.write(json.dumps(lowerCAmelCase_       ) + "\n"       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.add_kwargs_tokens_map.copy()
		kwargs.update(lowerCAmelCase_       )
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		shutil.rmtree(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_feature_extractor()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )
		_lowercase   =WavaVecaProcessor.from_pretrained(self.tmpdirname       )

		self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer.get_vocab()       )
		self.assertIsInstance(processor.tokenizer       ,  lowerCAmelCase_       )

		self.assertEqual(processor.feature_extractor.to_json_string()       ,  feature_extractor.to_json_string()       )
		self.assertIsInstance(processor.feature_extractor       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,  feature_extractor=self.get_feature_extractor()       )
		processor.save_pretrained(self.tmpdirname       )

		_lowercase   =self.get_tokenizer(bos_token="(BOS)"       ,  eos_token="(EOS)"       )
		_lowercase   =self.get_feature_extractor(do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

		_lowercase   =WavaVecaProcessor.from_pretrained(
		    self.tmpdirname       ,  bos_token="(BOS)"       ,  eos_token="(EOS)"       ,  do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

		self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer_add_kwargs.get_vocab()       )
		self.assertIsInstance(processor.tokenizer       ,  lowerCAmelCase_       )

		self.assertEqual(processor.feature_extractor.to_json_string()       ,  feature_extractor_add_kwargs.to_json_string()       )
		self.assertIsInstance(processor.feature_extractor       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(lowerCAmelCase_       ,  return_tensors="np"       )

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1e-2       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text=lowerCAmelCase_       )

		_lowercase   =tokenizer(lowerCAmelCase_       )

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]       ,  encoded_processor[key]       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		_lowercase   =processor.batch_decode(lowerCAmelCase_       )
		_lowercase   =tokenizer.batch_decode(lowerCAmelCase_       )

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		self.assertListEqual(
		    processor.model_input_names       ,  feature_extractor.model_input_names       ,  msg="`processor` and `feature_extractor` model input names do not match"       ,  )












import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a 			=							logging.get_logger(__name__)

a 			=							{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    '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',
    'mask_emb': 'masked_spec_embed',
}
def    a__    (	A		,					A		,					A		,					A		,					A       ):
   for attribute in key.split("""."""       ):
      lowercase__    :						Dict       =			getattr(A		,					A       )

   if weight_type is not None:
      lowercase__    :						List[Any]       =			getattr(A		,					A       ).shape
   else:
      lowercase__    :						Tuple       =			hf_pointer.shape

   assert hf_shape == value.shape, (
       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__    :						Dict       =			value
   elif weight_type == "weight_g":
      lowercase__    :						Any       =			value
   elif weight_type == "weight_v":
      lowercase__    :						Dict       =			value
   elif weight_type == "bias":
      lowercase__    :						Any       =			value
   else:
      lowercase__    :						Tuple       =			value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			[]
   lowercase__    :						int       =			fairseq_model.state_dict()

   lowercase__    :						Tuple       =			hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase__    :						Optional[int]       =			False
      if "conv_layers" in name:
         load_conv_layer(
             A		,					A		,					A		,					A		,					hf_model.config.feat_extract_norm == """group"""		,					)
         lowercase__    :						Optional[int]       =			True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase__    :						Dict       =			mapped_key

            if key in name:
               lowercase__    :						int       =			True
               if "*" in mapped_key:
                  lowercase__    :						Any       =			name.split(A       )[0].split("""."""       )[-2]
                  lowercase__    :						int       =			mapped_key.replace("""*"""		,					A       )
               if "weight_g" in name:
                  lowercase__    :						Dict       =			"""weight_g"""
               elif "weight_v" in name:
                  lowercase__    :						Dict       =			"""weight_v"""
               elif "weight" in name:
                  lowercase__    :						Union[str, Any]       =			"""weight"""
               elif "bias" in name:
                  lowercase__    :						Optional[int]       =			"""bias"""
               else:
                  lowercase__    :						List[str]       =			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       ):
   lowercase__    :						Optional[int]       =			full_name.split("""conv_layers."""       )[-1]
   lowercase__    :						int       =			name.split("""."""       )
   lowercase__    :						Optional[Any]       =			int(items[0]       )
   lowercase__    :						Optional[int]       =			int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         lowercase__    :						str       =			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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         lowercase__    :						Any       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         lowercase__    :						int       =			value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
   else:
      unused_weights.append(A       )
def    a__    (	A       ):
   lowercase__    :						Optional[Any]       =			HubertConfig()
   lowercase__    :						List[str]       =			model.config

   lowercase__    :						int       =			fs_config.activation_dropout
   lowercase__    :						Tuple       =			False
   lowercase__    :						Optional[Any]       =			fs_config.attention_dropout
   lowercase__    :						str       =			False
   lowercase__    :						List[Any]       =			eval(fs_config.extractor_conv_feature_layers       )
   lowercase__    :						Union[str, Any]       =			[x[0] for x in conv_layers]
   lowercase__    :						str       =			[x[1] for x in conv_layers]
   lowercase__    :						str       =			[x[2] for x in conv_layers]
   lowercase__    :						Any       =			"""gelu"""
   lowercase__    :						Optional[int]       =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   lowercase__    :						Optional[int]       =			False
   lowercase__    :						Tuple       =			0.0
   lowercase__    :						Optional[Any]       =			0.0
   lowercase__    :						Optional[Any]       =			fs_config.activation_fn
   lowercase__    :						Optional[Any]       =			fs_config.dropout
   lowercase__    :						Any       =			fs_config.encoder_embed_dim
   lowercase__    :						List[Any]       =			0.0_2
   lowercase__    :						List[Any]       =			fs_config.encoder_ffn_embed_dim
   lowercase__    :						List[Any]       =			1e-5
   lowercase__    :						Dict       =			0.0
   lowercase__    :						List[Any]       =			fs_config.encoder_attention_heads
   lowercase__    :						List[str]       =			fs_config.conv_pos_groups
   lowercase__    :						List[Any]       =			fs_config.conv_pos
   lowercase__    :						Union[str, Any]       =			len(A       )
   lowercase__    :						List[str]       =			fs_config.encoder_layers

   return config
@torch.no_grad()
def    a__    (	A		,					A=None       ):
   lowercase__    :						List[str]       =			distilhubert().model.model

   if config_path is not None:
      lowercase__    :						List[str]       =			HubertConfig.from_pretrained(A       )
   else:
      lowercase__    :						List[str]       =			convert_config(A       )
   lowercase__    :						List[Any]       =			model.eval()

   lowercase__    :						Optional[Any]       =			WavaVecaFeatureExtractor(
       feature_size=1		,					sampling_rate=16_000		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
   lowercase__    :						Union[str, Any]       =			HubertModel(A       )

   recursively_load_weights(A		,					A       )

   feature_extractor.save_pretrained(A       )
   hf_model.save_pretrained(A       )


if __name__ == "__main__":
 a 			=							argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 a 			=							parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


