
"""simple docstring"""







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	os.path.join(args.tf_model_dir				,	"""parameters.json"""							)
      UpperCAmelCase          =	json.loads(open(_snake_case							).read()							)
      if not params:
            raise ValueError(
                F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''							)
      if not args.output.endswith(""".pt"""							):
            UpperCAmelCase          =	args.output + """.pt"""
      UpperCAmelCase          =	OrderedDict()
      with tf.device("""/CPU:0"""							):
            UpperCAmelCase          =	tf.train.load_checkpoint(args.tf_model_dir							)
            UpperCAmelCase          =	reader.get_variable_to_shape_map()
            for key_name in shapes.keys():
                  UpperCAmelCase          =	reader.get_tensor(_snake_case							).astype(np.floataa							)
                  if key_name.endswith("""/adam_m"""							) or key_name.endswith("""/adam_v"""							):
                        continue
                  if key_name.startswith("""pasts/"""							):
                        if key_name.startswith("""pasts/mlp"""							):
                              UpperCAmelCase          =	int(key_name[9]							)
                        elif key_name.startswith("""pasts/out"""							):
                              UpperCAmelCase          =	8
                        UpperCAmelCase          =	"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                        UpperCAmelCase          =	vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                        UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name.startswith("""model/moe"""							):
                        UpperCAmelCase          =	int(key_name[9:].split("""/"""							)[0]							)
                        if key_name.endswith("""/switch_gating/kernel"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
                              UpperCAmelCase          =	vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/softmlp/kernel"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
                              UpperCAmelCase          =	vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/wo/kernel"""							) or key_name.endswith("""/wi/kernel"""							):
                              UpperCAmelCase          =	key_name[-9:-7]
                              for i in range(16							):
                                    UpperCAmelCase          =	"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
                                    UpperCAmelCase          =	(
                                        vnp[i].transpose([1, 0]							).copy()
                                    )  # In Mesh-Tensorflow, it is one array, so it is divided
                                    UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name.startswith("""model/mlp"""							):
                        UpperCAmelCase          =	int(key_name[9:].split("""/"""							)[0]							)
                        if key_name.endswith("""/p1/kernel"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
                              UpperCAmelCase          =	vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/p1/bias"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
                              UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/p2/kernel"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
                              UpperCAmelCase          =	vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/p2/bias"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
                              UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name.startswith("""model/ln"""							):
                        UpperCAmelCase          =	int(key_name[8:].split("""/"""							)[0]							)
                        if key_name.endswith("""/b"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.norm.bias""" % player
                              UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/g"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.feed_forward.norm.weight""" % player
                              UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name.startswith("""model/att"""							):
                        UpperCAmelCase          =	int(key_name[9:].split("""/"""							)[0]							)
                        if key_name.endswith("""/qkv/kernel"""							):
                              UpperCAmelCase          =	vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                              UpperCAmelCase          =	state[:, 0, :, :]
                              UpperCAmelCase          =	state[:, 1, :, :]
                              UpperCAmelCase          =	state[:, 2, :, :]
                              UpperCAmelCase          =	(
                                  state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]							)
                                  .transpose([1, 0]							)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	(
                                  state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]							)
                                  .transpose([1, 0]							)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	(
                                  state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]							)
                                  .transpose([1, 0]							)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                              UpperCAmelCase          =	"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                              UpperCAmelCase          =	"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/o/kernel"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
                              UpperCAmelCase          =	(
                                  vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]							).transpose([1, 0]							).copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name.startswith("""model/an"""							):
                        UpperCAmelCase          =	int(key_name[8:].split("""/"""							)[0]							)
                        if key_name.endswith("""/b"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.self_attn.norm.bias""" % player
                              UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                        elif key_name.endswith("""/g"""							):
                              UpperCAmelCase          =	"""model.blocks.%d.self_attn.norm.weight""" % player
                              UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif (
                      key_name.startswith("""model/wte"""							)
                      or key_name.startswith("""model/wpe"""							)
                      or key_name.startswith("""model/ete"""							)
                  ):
                        UpperCAmelCase          =	{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
                            key_name[-3:]
                        ]
                        UpperCAmelCase          =	"""model.%s.weight""" % nlayer
                        UpperCAmelCase          =	vnp.copy()  # same in embedded
                        UpperCAmelCase          =	torch.tensor(_snake_case							)
                        if key_name.startswith("""model/wte"""							):
                              UpperCAmelCase          =	"""lm_head.weight"""
                              UpperCAmelCase          =	vnp.copy()  # same in embedded
                              UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name.startswith("""model/wob"""							):
                        UpperCAmelCase          =	"""final_logits_bias"""
                        UpperCAmelCase          =	vnp.copy()  # same in embedded
                        UpperCAmelCase          =	state.reshape((1, -1)							)
                        UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name == "model/dense/kernel":
                        UpperCAmelCase          =	"""model.last_project.weight"""
                        UpperCAmelCase          =	vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                        UpperCAmelCase          =	torch.tensor(_snake_case							)
                  elif key_name == "model/dense_1/bias":
                        UpperCAmelCase          =	"""model.last_project.bias"""
                        UpperCAmelCase          =	vnp.copy()  # same because it is one dimensional
                        UpperCAmelCase          =	torch.tensor(_snake_case							)
      torch.save(_snake_case				,	args.output							)


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser(
      description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
  )
  parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
  parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
  _UpperCamelCase					      =  parser.parse_args()
  convert_tf_gptsan_to_pt(args)











"""simple docstring"""







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

_UpperCamelCase					      =  {
    """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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
_UpperCamelCase					      =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							)

      if weight_type is not None:
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							).shape
      else:
            UpperCAmelCase          =	hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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          =	value
      elif weight_type == "weight_g":
            UpperCAmelCase          =	value
      elif weight_type == "weight_v":
            UpperCAmelCase          =	value
      elif weight_type == "bias":
            UpperCAmelCase          =	value
      else:
            UpperCAmelCase          =	value

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	fairseq_model.state_dict()

      UpperCAmelCase          =	hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase          =	False
            if "conv_layers" in name:
                  load_conv_layer(
                      _snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	hf_model.config.feat_extract_norm == """group"""				,	)
                  UpperCAmelCase          =	True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase          =	"""unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
                              if "layer_norm_for_extract" in name and (".".join(name.split("""."""							)[:-1]							) != key):
                                    # special case since naming is very similar
                                    continue
                              UpperCAmelCase          =	True
                              if "*" in mapped_key:
                                    UpperCAmelCase          =	name.split(_snake_case							)[0].split("""."""							)[-2]
                                    UpperCAmelCase          =	mapped_key.replace("""*"""				,	_snake_case							)
                              if "weight_g" in name:
                                    UpperCAmelCase          =	"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCAmelCase          =	"""weight_v"""
                              elif "bias" in name:
                                    UpperCAmelCase          =	"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase          =	"""weight"""
                              else:
                                    UpperCAmelCase          =	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}'''							)

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	full_name.split("""conv_layers."""							)[-1]
      UpperCAmelCase          =	name.split("""."""							)
      UpperCAmelCase          =	int(items[0]							)
      UpperCAmelCase          =	int(items[1]							)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            F'''{full_name} has size {value.shape}, but'''
                            F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
                  UpperCAmelCase          =	value
                  logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            F'''{full_name} has size {value.shape}, but'''
                            F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
                  UpperCAmelCase          =	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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F'''{full_name} has size {value.shape}, but'''
                            F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''							)
                  UpperCAmelCase          =	value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F'''{full_name} has size {value.shape}, but'''
                            F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''							)
                  UpperCAmelCase          =	value
                  logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
      else:
            unused_weights.append(_snake_case							)

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None				,	_snake_case=True							):







      """simple docstring"""






      if config_path is not None:
            UpperCAmelCase          =	UniSpeechSatConfig.from_pretrained(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatConfig()

      UpperCAmelCase          =	""""""

      if is_finetuned:
            UpperCAmelCase          =	UniSpeechSatForCTC(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatForPreTraining(_snake_case							)

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

      recursively_load_weights(_snake_case				,	_snake_case							)

      hf_wavavec.save_pretrained(_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(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  _UpperCamelCase					      =  parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )



