




"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

SCREAMING_SNAKE_CASE    =	{
    "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",
    "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    =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Optional[Any]:
 for attribute in key.split("."						):
  A__	=    getattr(lowercase_      ,			lowercase_						)

 if weight_type is not None:
  A__	=    getattr(lowercase_      ,			lowercase_						).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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Union[str, Any]:
 A__	=    []
 A__	=    fairseq_model.state_dict()

 A__	=    hf_model.feature_extractor

 # if encoder has different dim to decoder -> use proj_weight
 A__	=    None

 for name, value in fairseq_dict.items():
  A__	=    False
  if "conv_layers" in name:
   load_conv_layer(
       lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			hf_model.config.feat_extract_norm == "group"      ,			)
   A__	=    True
  elif name.split("."						)[0] == "proj":
   A__	=    fairseq_model.proj
   A__	=    True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
     A__	=    True
     if "*" in mapped_key:
      A__	=    name.split(lowercase_						)[0].split("."						)[-2]
      A__	=    mapped_key.replace("*"      ,			lowercase_						)
     if "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:
      A__	=    "weight"
     else:
      A__	=    None
     set_recursively(lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)
    continue
  if not is_used:
   unused_weights.append(lowercase_						)

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

 return proj_weight







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Dict:
 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(lowercase_						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 A__,				A__	=    emb.weight.shape
 A__	=    nn.Linear(lowercase_      ,			lowercase_      ,			bias=lowercase_						)
 A__	=    emb.weight.data
 return lin_layer







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Dict:
 with open(lowercase_      ,			"r"      ,			encoding="utf-8"						) as f:
  A__	=    f.readlines()
  A__	=    [line.split(" "						)[0] for line in lines]

 A__	=    len(lowercase_						)

 A__	=    {
     "<s>": 0,
     "<pad>": 1,
     "</s>": 2,
     "<unk>": 3,
 }

 vocab_dict.update(dict(zip(lowercase_      ,			range(4      ,			num_words + 4						)						)						)						)
 return vocab_dict







@torch.no_grad()
def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_      ,			)						->		List[Any]:
 A__	=    WavaVecaConfig.from_pretrained(lowercase_						)
 A__	=    SpeechaTextaConfig.from_pretrained(
     lowercase_      ,			vocab_size=lowercase_      ,			decoder_layers=lowercase_      ,			do_stable_layer_norm=lowercase_						)

 A__	=    WavaVecaFeatureExtractor(
     feature_size=1      ,			sampling_rate=1_60_00      ,			padding_value=0      ,			do_normalize=lowercase_      ,			return_attention_mask=lowercase_      ,			)

 A__,				A__,				A__	=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]      ,			arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
 A__	=    model[0].eval()

 # set weights for wav2vec2 encoder
 A__	=    WavaVecaModel(lowercase_						)
 A__	=    recursively_load_weights_wavaveca(model.encoder      ,			lowercase_						)

 A__	=    SpeechaTextaForCausalLM(lowercase_						)
 A__,				A__	=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,			strict=lowercase_						)

 # set output linear layer
 unexpected_keys.remove("embed_out"						)
 A__	=    nn.Parameter(model.decoder.embed_out.detach()						)

 # layer norm is init to identity matrix so leaving it is fine
 logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""						)
 logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""						)

 A__	=    SpeechEncoderDecoderModel(encoder=lowercase_      ,			decoder=lowercase_						)
 A__	=    False

 # add projection layer
 A__	=    nn.Parameter(projection_layer.weight						)
 A__	=    nn.Parameter(projection_layer.bias						)

 A__	=    create_vocab_dict(lowercase_						)

 with open(os.path.join(lowercase_      ,			"vocab.json"						)      ,			"w"						) as fp:
  json.dump(lowercase_      ,			lowercase_						)

 A__	=    SpeechaTextaTokenizer(os.path.join(lowercase_      ,			"vocab.json"						)						)
 tokenizer.save_pretrained(lowercase_						)

 A__	=    hf_wavavec.config.to_dict()
 A__	=    tokenizer.pad_token_id
 A__	=    tokenizer.bos_token_id
 A__	=    tokenizer.eos_token_id
 A__	=    "speech_to_text_2"
 A__	=    "wav2vec2"

 A__	=    SpeechEncoderDecoderConfig.from_dict(lowercase_						)

 hf_wavavec.save_pretrained(lowercase_						)
 feature_extractor.save_pretrained(lowercase_						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	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(
          "--encoder_config_path",
          default="facebook/wav2vec2-large-lv60",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/s2t-small-mustc-en-fr-st",
          type=str,
          help="Path to hf decoder s2t checkpoint config",
      )
      parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          vocab_size=args.vocab_size,
          num_decoder_layers=args.num_decoder_layers,
      )











"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    "ZinengTang/tvlt-base"
     A__	=    tempfile.mkdtemp()




    def 							__magic_name__							(   self						:	int     ,			**snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     return TvltImageProcessor.from_pretrained(self.checkpoint     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			**snake_case_						:	str				)				->							List[str]:




     '''simple docstring'''

     return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)
     processor.save_pretrained(self.tmpdirname				)
     A__	=    TvltProcessor.from_pretrained(self.tmpdirname				)

     self.assertIsInstance(processor.feature_extractor     ,			snake_case_				)
     self.assertIsInstance(processor.image_processor     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([12_000]				)

     A__	=    feature_extractor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(audio=snake_case_     ,			return_tensors="np"				)

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




    def 							__magic_name__							(   self						:	Tuple				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([3, 224, 224]				)

     A__	=    image_processor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(images=snake_case_     ,			return_tensors="np"				)

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




    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([12_000]				)
     A__	=    np.ones([3, 224, 224]				)

     A__	=    processor(audio=snake_case_     ,			images=snake_case_				)

     self.assertListEqual(list(inputs.keys()				)     ,			["audio_values", "audio_mask", "pixel_values", "pixel_mask"]				)

     # test if it raises when no input is passed
     with pytest.raises(snake_case_				):
      processor()




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

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





