




"""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()
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''',
    '''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''',
}
A_						=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					Any		,   snake_case__							:					str		,   snake_case__							:					str    ):
  """simple docstring"""

  for attribute in key.split("""."""    ):
    _snake_case							:		Optional[Any]								=  getattr(snake_case__		,   snake_case__    )

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

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


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  []
  _snake_case							:		Optional[Any]								=  fairseq_model.state_dict()

  _snake_case							:		str								=  hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  _snake_case							:		Optional[Any]								=  None

  for name, value in fairseq_dict.items():
    _snake_case							:		Optional[Any]								=  False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   hf_model.config.feat_extract_norm == """group"""		,   )
      _snake_case							:		Dict								=  True
    elif name.split("""."""    )[0] == "proj":
      _snake_case							:		Dict								=  fairseq_model.proj
      _snake_case							:		Optional[int]								=  True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
          _snake_case							:		Dict								=  True
          if "*" in mapped_key:
            _snake_case							:		Optional[int]								=  name.split(snake_case__    )[0].split("""."""    )[-2]
            _snake_case							:		Union[str, Any]								=  mapped_key.replace("""*"""		,   snake_case__    )
          if "weight_g" in name:
            _snake_case							:		str								=  """weight_g"""
          elif "weight_v" in name:
            _snake_case							:		Optional[Any]								=  """weight_v"""
          elif "bias" in name:
            _snake_case							:		Union[str, Any]								=  """bias"""
          elif "weight" in name:
            _snake_case							:		int								=  """weight"""
          else:
            _snake_case							:		Optional[int]								=  None
          set_recursively(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )
        continue
    if not is_used:
      unused_weights.append(snake_case__    )

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

  return proj_weight


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Dict		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Any								=  full_name.split("""conv_layers."""    )[-1]
  _snake_case							:		Optional[int]								=  name.split("""."""    )
  _snake_case							:		List[str]								=  int(items[0]    )
  _snake_case							:		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."
      )
      _snake_case							:		Tuple								=  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."
      )
      _snake_case							:		List[Any]								=  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."
      )
      _snake_case							:		int								=  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."
      )
      _snake_case							:		List[str]								=  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
  else:
    unused_weights.append(snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  _snake_case		,					_snake_case							:		Optional[Any]								=  emb.weight.shape
  _snake_case							:		Optional[int]								=  nn.Linear(snake_case__		,   snake_case__		,   bias=snake_case__    )
  _snake_case							:		Union[str, Any]								=  emb.weight.data
  return lin_layer


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  with open(snake_case__		,   """r"""		,   encoding="""utf-8"""    ) as f:
    _snake_case							:		Any								=  f.readlines()
    _snake_case							:		Optional[Any]								=  [line.split(""" """    )[0] for line in lines]

  _snake_case							:		str								=  len(snake_case__    )

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

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


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					List[str]		,   snake_case__							:					int		,   snake_case__							:					Dict		,   snake_case__							:					List[Any]		,   snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		List[str]								=  SpeechaTextaConfig.from_pretrained(
      snake_case__		,   vocab_size=snake_case__		,   decoder_layers=snake_case__		,   do_stable_layer_norm=snake_case__    )

  _snake_case							:		Dict								=  WavaVecaFeatureExtractor(
      feature_size=1		,   sampling_rate=1_60_00		,   padding_value=0		,   do_normalize=snake_case__		,   return_attention_mask=snake_case__		,   )

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

  # set weights for wav2vec2 encoder
  _snake_case							:		Any								=  WavaVecaModel(snake_case__    )
  _snake_case							:		Optional[Any]								=  recursively_load_weights_wavaveca(model.encoder		,   snake_case__    )

  _snake_case							:		Optional[Any]								=  SpeechaTextaForCausalLM(snake_case__    )
  _snake_case		,					_snake_case							:		List[str]								=  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,   strict=snake_case__    )

  # set output linear layer
  unexpected_keys.remove("""embed_out"""    )
  _snake_case							:		Any								=  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}"    )

  _snake_case							:		Any								=  SpeechEncoderDecoderModel(encoder=snake_case__		,   decoder=snake_case__    )
  _snake_case							:		Any								=  False

  # add projection layer
  _snake_case							:		int								=  nn.Parameter(projection_layer.weight    )
  _snake_case							:		Any								=  nn.Parameter(projection_layer.bias    )

  _snake_case							:		Any								=  create_vocab_dict(snake_case__    )

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

  _snake_case							:		Dict								=  SpeechaTextaTokenizer(os.path.join(snake_case__		,   """vocab.json"""    )    )
  tokenizer.save_pretrained(snake_case__    )

  _snake_case							:		str								=  hf_wavavec.config.to_dict()
  _snake_case							:		List[str]								=  tokenizer.pad_token_id
  _snake_case							:		Union[str, Any]								=  tokenizer.bos_token_id
  _snake_case							:		Union[str, Any]								=  tokenizer.eos_token_id
  _snake_case							:		Optional[Any]								=  """speech_to_text_2"""
  _snake_case							:		Optional[int]								=  """wav2vec2"""

  _snake_case							:		Tuple								=  SpeechEncoderDecoderConfig.from_dict(snake_case__    )

  hf_wavavec.save_pretrained(snake_case__    )
  feature_extractor.save_pretrained(snake_case__    )


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('''--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_02_24, type=int, help='''Vocab size of decoder''')
     parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

     A_						=				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,
     )






import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard







@slow
@require_flax
class       lowerCamelCase__   (       unittest.TestCase):





     def      __A					(self       )     ->   List[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''       )
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )

      _lowercase							      ='''xvjiarui/stable-diffusion-2-inpainting'''
      _lowercase      ,    _lowercase							      =FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase		,     safety_checker=UpperCAmelCase       )

      _lowercase							      ='''Face of a yellow cat, high resolution, sitting on a park bench'''

      _lowercase							      =jax.random.PRNGKey(0       )
      _lowercase							      =5_0

      _lowercase							      =jax.device_count()
      _lowercase							      =num_samples * [prompt]
      _lowercase							      =num_samples * [init_image]
      _lowercase							      =num_samples * [mask_image]
      _lowercase      ,    _lowercase      ,    _lowercase							      =pipeline.prepare_inputs(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )

      # shard inputs and rng
      _lowercase							      =replicate(UpperCAmelCase       )
      _lowercase							      =jax.random.split(UpperCAmelCase		,     jax.device_count()       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )

      _lowercase							      =pipeline(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     jit=UpperCAmelCase       )

      _lowercase							      =output.images.reshape(UpperCAmelCase		,     5_1_2		,     5_1_2		,     3       )

      _lowercase							      =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      _lowercase							      =jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      _lowercase							      =jnp.array(
          [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]       )
      print(f"output_slice: {output_slice}"       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2

