



"""simple docstring"""




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
_lowerCAmelCase    : Optional[int]							=							logging.get_logger(__name__)

_lowerCAmelCase    : List[Any]							=							{
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase    : Union[str, Any]							=							[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						List[Any]    )  ->       Optional[int]:





     '''simple docstring'''

     for attribute in key.split("."    ):
          _UpperCAmelCase      :     List[str]        =   getattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

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

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   []
     _UpperCAmelCase      :     Optional[Any]        =   fairseq_model.state_dict()

     _UpperCAmelCase      :     Tuple        =   hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          _UpperCAmelCase      :     List[str]        =   False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  hf_model.config.feat_extract_norm == "group"						,  )
               _UpperCAmelCase      :     Tuple        =   True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
                         _UpperCAmelCase      :     Dict        =   True
                         if "*" in mapped_key:
                              _UpperCAmelCase      :     Dict        =   name.split(SCREAMING_SNAKE_CASE__    )[0].split("."    )[-2]
                              _UpperCAmelCase      :     List[str]        =   mapped_key.replace("*"						,  SCREAMING_SNAKE_CASE__    )
                         if "weight_g" in name:
                              _UpperCAmelCase      :     Optional[int]        =   "weight_g"
                         elif "weight_v" in name:
                              _UpperCAmelCase      :     Optional[Any]        =   "weight_v"
                         elif "bias" in name and "relative_attention_bias" not in name:
                              _UpperCAmelCase      :     Optional[Any]        =   "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _UpperCAmelCase      :     int        =   "weight"
                         else:
                              _UpperCAmelCase      :     Tuple        =   None

                         set_recursively(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE__    )

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   full_name.split("conv_layers."    )[-1]
     _UpperCAmelCase      :     str        =   name.split("."    )
     _UpperCAmelCase      :     Dict        =   int(items[0]    )
     _UpperCAmelCase      :     Dict        =   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.'
               )
               _UpperCAmelCase      :     List[Any]        =   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.'
               )
               _UpperCAmelCase      :     Dict        =   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."
               )
               _UpperCAmelCase      :     Union[str, 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.'
               )
               _UpperCAmelCase      :     List[str]        =   value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE__    )

@torch.no_grad()
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Dict=None    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   torch.load(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[str]        =   WavLMConfigOrig(checkpoint["cfg"]    )
     _UpperCAmelCase      :     str        =   WavLMOrig(SCREAMING_SNAKE_CASE__    )
     model.load_state_dict(checkpoint["model"]    )
     model.eval()

     if config_path is not None:
          _UpperCAmelCase      :     Optional[int]        =   WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE__    )
     else:
          _UpperCAmelCase      :     Tuple        =   WavLMConfig()

     _UpperCAmelCase      :     Optional[Any]        =   WavLMModel(SCREAMING_SNAKE_CASE__    )

     recursively_load_weights(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 _lowerCAmelCase    : 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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 _lowerCAmelCase    : List[str]							=							parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			GPTSanJapaneseTokenizer
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       List[str]								=			{'do_clean_text': False, 'add_prefix_space': False}



      def 						snake_case_ (					self					:     Any						):
           super().setUp()

           # fmt: off
           _UpperCAmelCase      :     Any        =   ["こん", "こんに", "にちは", "ばんは", "世界,㔺界", "、", "。", "<BR>", "<SP>", "<TAB>", "<URL>", "<EMAIL>", "<TEL>", "<DATE>", "<PRICE>", "<BLOCK>", "<KIGOU>", "<U2000U2BFF>", "<|emoji1|>", "<unk>", "<|bagoftoken|>", "<|endoftext|>"]
           # fmt: on
           _UpperCAmelCase      :     Optional[int]        =   {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
           _UpperCAmelCase      :     List[Any]        =   {"unk_token": "<unk>"}

           _UpperCAmelCase      :     List[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Any        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["emoji_file"]						)
           with open(self.vocab_file					,     "w"					,     encoding="utf-8"						) as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in vocab_tokens]						)						)
           with open(self.emoji_file					,     "w"						) as emoji_writer:
                emoji_writer.write(json.dumps(A						)						)



      def 						snake_case_ (					self					:     int					,     **A					:     List[str]						):
           kwargs.update(self.special_tokens_map						)
           return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     Any						):
           _UpperCAmelCase      :     Optional[Any]        =   "こんにちは、世界。 \nこんばんは、㔺界。😀"
           _UpperCAmelCase      :     List[Any]        =   "こんにちは、世界。 \nこんばんは、世界。😀"
           return input_text, output_text



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     str						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     str        =   self.get_input_output_texts(A						)
           _UpperCAmelCase      :     List[Any]        =   tokenizer.encode(A					,     add_special_tokens=A						)
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.decode(A					,     clean_up_tokenization_spaces=A						)
           return text, ids



      def 						snake_case_ (					self					:     Any						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     int						):
           pass  # TODO add if relevant



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[Any]        =   self.get_tokenizer()

           # Testing tokenization
           _UpperCAmelCase      :     Optional[int]        =   "こんにちは、世界。　こんばんは、㔺界。"
           _UpperCAmelCase      :     Dict        =   ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
           _UpperCAmelCase      :     List[Any]        =   tokenizer.tokenize(A						)
           self.assertListEqual(A					,     A						)

           # Testing conversion to ids without special tokens
           _UpperCAmelCase      :     Optional[int]        =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
           _UpperCAmelCase      :     List[str]        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(A					,     A						)

           # Testing conversion to ids with special tokens
           _UpperCAmelCase      :     str        =   tokens + [tokenizer.unk_token]
           _UpperCAmelCase      :     Any        =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
           _UpperCAmelCase      :     str        =   tokenizer.convert_tokens_to_ids(A						)
           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_tokenizer()

           # Testing tokenization
           _UpperCAmelCase      :     Dict        =   "こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
           _UpperCAmelCase      :     Tuple        =   "こんにちは、、、、世界。こんばんは、、、、世界。"
           _UpperCAmelCase      :     int        =   tokenizer.encode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   tokenizer.decode(A						)
           self.assertEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     List[Any]        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           _UpperCAmelCase      :     List[Any]        =   "こんにちは、世界。"
           _UpperCAmelCase      :     List[str]        =   "こんばんは、㔺界。😀"
           _UpperCAmelCase      :     Any        =   "こんにちは、世界。こんばんは、世界。😀"
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.encode(prefix_text + input_text						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.encode(""					,     prefix_text=prefix_text + input_text						)
           _UpperCAmelCase      :     Optional[int]        =   tokenizer.encode(A					,     prefix_text=A						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.decode(A						)
           _UpperCAmelCase      :     Optional[Any]        =   tokenizer.decode(A						)
           _UpperCAmelCase      :     Tuple        =   tokenizer.decode(A						)
           self.assertEqual(A					,     A						)
           self.assertEqual(A					,     A						)
           self.assertEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase      :     Dict        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           # Testing tokenization
           _UpperCAmelCase      :     Any        =   "こんにちは、世界。"
           _UpperCAmelCase      :     List[Any]        =   "こんばんは、㔺界。😀"

           _UpperCAmelCase      :     Optional[Any]        =   len(tokenizer.encode(A						)						) - 2
           _UpperCAmelCase      :     List[Any]        =   len(tokenizer.encode(A						)						) - 2

           _UpperCAmelCase      :     List[str]        =   [1] + [0] * (len_prefix + len_text + 1)
           _UpperCAmelCase      :     str        =   [1] * (len_prefix + len_text + 1) + [0]
           _UpperCAmelCase      :     int        =   [1] + [1] * (len_prefix) + [0] * (len_text + 1)

           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer(prefix_text + input_text						).token_type_ids
           _UpperCAmelCase      :     Any        =   tokenizer(""					,     prefix_text=prefix_text + input_text						).token_type_ids
           _UpperCAmelCase      :     List[Any]        =   tokenizer(A					,     prefix_text=A						).token_type_ids
           self.assertListEqual(A					,     A						)
           self.assertListEqual(A					,     A						)
           self.assertListEqual(A					,     A						)



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     str        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           _UpperCAmelCase      :     Dict        =   tokenizer.encode("あンいワ"						)
           _UpperCAmelCase      :     str        =   tokenizer.encode(""					,     prefix_text="あンいワ"						)
           _UpperCAmelCase      :     Dict        =   tokenizer.encode("いワ"					,     prefix_text="あン"						)

           self.assertEqual(tokenizer.decode(A						)					,     tokenizer.decode(A						)						)
           self.assertEqual(tokenizer.decode(A						)					,     tokenizer.decode(A						)						)
           self.assertNotEqual(A					,     A						)
           self.assertNotEqual(A					,     A						)
           self.assertEqual(x_token_a[1]					,     x_token_a[-1]						)  # SEG token
           self.assertEqual(x_token_a[1]					,     x_token_a[3]						)  # SEG token



      @slow
      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"						)

           _UpperCAmelCase      :     Tuple        =   [["武田信玄", "は、"], ["織田信長", "の配下の、"]]
           _UpperCAmelCase      :     Tuple        =   tokenizer(A					,     padding=A						)
           _UpperCAmelCase      :     str        =   tokenizer.batch_encode_plus(A					,     padding=A						)

           # fmt: off
           _UpperCAmelCase      :     str        =   [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
           _UpperCAmelCase      :     str        =   [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
           _UpperCAmelCase      :     int        =   [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
           # fmt: on
           self.assertListEqual(x_token.input_ids					,     A						)
           self.assertListEqual(x_token.token_type_ids					,     A						)
           self.assertListEqual(x_token.attention_mask					,     A						)
           self.assertListEqual(x_token_a.input_ids					,     A						)
           self.assertListEqual(x_token_a.token_type_ids					,     A						)
           self.assertListEqual(x_token_a.attention_mask					,     A						)



      def 						snake_case_ (					self					:     List[Any]						):
           # Intentionally convert some words to accommodate character fluctuations unique to Japanese
           pass



      def 						snake_case_ (					self					:     int						):
           # tokenizer has no padding token
           pass
