
"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase_							=					{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase			)		->Any:

   """simple docstring"""



   for attribute in key.split("."			):
      a_							=			getattr(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__			)

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

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase			)		->int:

   """simple docstring"""



   a_							=			[]
   a_							=			fairseq_model.state_dict()

   a_							=			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      a_							=			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"      ,					)
         a_							=			True
      else:
         for key, mapped_key in MAPPING.items():
            a_							=			"sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") 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(SCREAMING_SNAKE_CASE__			)[0].split("."			)[-2]
                  a_							=			mapped_key.replace("*"      ,					SCREAMING_SNAKE_CASE__			)
               if "weight_g" in name:
                  a_							=			"weight_g"
               elif "weight_v" in name:
                  a_							=			"weight_v"
               elif "weight" in name:
                  a_							=			"weight"
               elif "bias" in name:
                  a_							=			"bias"
               else:
                  a_							=			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 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase			)		->Dict:

   """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:
         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.'''
         )
         a_							=			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.'''
         )
         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:
         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."
         )
         a_							=			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.'''
         )
         a_							=			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__			)
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->Optional[Any]:

   """simple docstring"""



   a_							=			SEWConfig()
   if is_finetuned:
      a_							=			model.wav_encoder.wav_model.cfg
   else:
      a_							=			model.cfg

   a_							=			fs_config.conv_bias
   a_							=			eval(fs_config.conv_feature_layers			)
   a_							=			[x[0] for x in conv_layers]
   a_							=			[x[1] for x in conv_layers]
   a_							=			[x[2] for x in conv_layers]
   a_							=			"gelu"
   a_							=			"layer" if fs_config.extractor_mode == "layer_norm" else "group"
   a_							=			0.0
   a_							=			fs_config.activation_fn.name
   a_							=			fs_config.encoder_embed_dim
   a_							=			0.02
   a_							=			fs_config.encoder_ffn_embed_dim
   a_							=			1E-5
   a_							=			fs_config.encoder_layerdrop
   a_							=			fs_config.encoder_attention_heads
   a_							=			fs_config.conv_pos_groups
   a_							=			fs_config.conv_pos
   a_							=			len(SCREAMING_SNAKE_CASE__			)
   a_							=			fs_config.encoder_layers
   a_							=			fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      a_							=			model.cfg
      a_							=			fs_config.final_dropout
      a_							=			fs_config.layerdrop
   a_							=			fs_config.activation_dropout
   a_							=			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   a_							=			fs_config.attention_dropout
   a_							=			fs_config.dropout_input
   a_							=			fs_config.dropout
   a_							=			fs_config.mask_channel_length
   a_							=			fs_config.mask_channel_prob
   a_							=			fs_config.mask_length
   a_							=			fs_config.mask_prob

   a_							=			"Wav2Vec2FeatureExtractor"
   a_							=			"Wav2Vec2CTCTokenizer"

   return config
@torch.no_grad()
def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase      ,					UpperCAmelCase=None      ,					UpperCAmelCase=None      ,					UpperCAmelCase=True			)		->Optional[Any]:

   """simple docstring"""



   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_					,       a_					,       a_							=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

   if config_path is not None:
      a_							=			SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
   else:
      a_							=			convert_config(model[0]      ,					SCREAMING_SNAKE_CASE__			)
   a_							=			model[0].eval()

   a_							=			True if config.feat_extract_norm == "layer" else False
   a_							=			WavaVecaFeatureExtractor(
       feature_size=1      ,					sampling_rate=16_000      ,					padding_value=0      ,					do_normalize=SCREAMING_SNAKE_CASE__      ,					return_attention_mask=SCREAMING_SNAKE_CASE__      ,					)

   if is_finetuned:
      if dict_path:
         a_							=			Dictionary.load(SCREAMING_SNAKE_CASE__			)

         # 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.pad_index
         a_							=			target_dict.bos_index
         a_							=			target_dict.eos_index
         a_							=			len(target_dict.symbols			)
         a_							=			os.path.join(SCREAMING_SNAKE_CASE__      ,					"vocab.json"			)
         if not os.path.isdir(SCREAMING_SNAKE_CASE__			):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(SCREAMING_SNAKE_CASE__			)			)
            return
         os.makedirs(SCREAMING_SNAKE_CASE__      ,					exist_ok=SCREAMING_SNAKE_CASE__			)
         with open(SCREAMING_SNAKE_CASE__      ,					"w"      ,					encoding="utf-8"			) as vocab_handle:
            json.dump(target_dict.indices      ,					SCREAMING_SNAKE_CASE__			)
         a_							=			WavaVecaCTCTokenizer(
             SCREAMING_SNAKE_CASE__      ,					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=SCREAMING_SNAKE_CASE__      ,					)
         a_							=			WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__      ,					tokenizer=SCREAMING_SNAKE_CASE__			)
         processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

      a_							=			SEWForCTC(SCREAMING_SNAKE_CASE__			)
   else:
      a_							=			SEWModel(SCREAMING_SNAKE_CASE__			)
      feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__			)

   recursively_load_weights(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__			)

   hf_model.save_pretrained(SCREAMING_SNAKE_CASE__			)


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(
          '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
      )
      UpperCamelCase_							=					parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )


import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return x + 2





class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''x = 3'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result == 3
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3}							)

									snake_case_   =	'''x = y'''
									snake_case_   =	{'''y''': 5}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 5, '''y''': 5}							)







			def 				snake_case__(     self :				Dict							)      ->Optional[int]:
									snake_case_   =	'''y = add_two(x)'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 5}							)

									# Won't work without the tool
									with CaptureStdout() as out:
															snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result is None
									assert "tried to execute add_two" in out.out







			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									snake_case_   =	'''x = 3'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result == 3
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3}							)







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[int]:
									snake_case_   =	'''test_dict = {\'x\': x, \'y\': add_two(x)}'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 5}							)
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}							)







			def 				snake_case__(     self :				Dict							)      ->str:
									snake_case_   =	'''x = 3\ny = 5'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 5}							)







			def 				snake_case__(     self :				str							)      ->Tuple:
									snake_case_   =	'''text = f\'This is x: {x}.\''''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == "This is x: 3."
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''text''': '''This is x: 3.'''}							)







			def 				snake_case__(     self :				Optional[Any]							)      ->List[str]:
									snake_case_   =	'''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 2
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 2}							)

									snake_case_   =	{'''x''': 8}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									# evaluate returns the value of the last assignment.
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 8, '''y''': 5}							)







			def 				snake_case__(     self :				str							)      ->str:
									snake_case_   =	'''test_list = [x, add_two(x)]'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   [3, 5]							)
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_list''': [3, 5]}							)







			def 				snake_case__(     self :				Any							)      ->List[Any]:
									snake_case_   =	'''y = x'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {}							,   state=_UpperCamelCase							)
									assert result == 3
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''y''': 3}							)







			def 				snake_case__(     self :				Optional[int]							)      ->Dict:
									snake_case_   =	'''test_list = [x, add_two(x)]\ntest_list[1]'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_list''': [3, 5]}							)

									snake_case_   =	'''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
									snake_case_   =	{'''x''': 3}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''add_two''': add_two}							,   state=_UpperCamelCase							)
									assert result == 5
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}							)







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''x = 0\nfor i in range(3):\n    x = i'''
									snake_case_   =	{}
									snake_case_   =	evaluate(_UpperCamelCase							,   {'''range''': range}							,   state=_UpperCamelCase							)
									assert result == 2
									self.assertDictEqual(_UpperCamelCase							,   {'''x''': 2, '''i''': 2}							)