





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase="attention"					)						->			int:
 UpperCamelCase     : Tuple	     =       np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]					)
 UpperCamelCase     : Dict	     =       k_tmp.reshape(k_tmp.shape[0]							,       k_tmp.shape[1] * k_tmp.shape[2]					)
 UpperCamelCase     : Dict	     =       np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]					)
 UpperCamelCase     : str	     =       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,       o_tmp.shape[2]					)
 UpperCamelCase     : str	     =       np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]					)
 UpperCamelCase     : Union[str, Any]	     =       q_tmp.reshape(q_tmp.shape[0]							,       q_tmp.shape[1] * q_tmp.shape[2]					)
 UpperCamelCase     : Dict	     =       np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]					)
 UpperCamelCase     : Dict	     =       v_tmp.reshape(v_tmp.shape[0]							,       v_tmp.shape[1] * v_tmp.shape[2]					)
 return k, o, q, v



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=False					)						->			Union[str, Any]:
 if split_mlp_wi:
  UpperCamelCase     : Union[str, Any]	     =       params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
  UpperCamelCase     : str	     =       params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
  UpperCamelCase     : Tuple	     =       (wi_a, wi_a)
 else:
  UpperCamelCase     : Dict	     =       params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

 UpperCamelCase     : str	     =       params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
 return wi, wo



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[int]:
 return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 		A_    (					_lowerCAmelCase							,       *, _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase = False					)						->			Dict:
 UpperCamelCase     : int	     =       traverse_util.flatten_dict(variables["target"]					)
 UpperCamelCase     : Union[str, Any]	     =       {"/".join(_lowerCAmelCase					): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 UpperCamelCase     : List[str]	     =       "encoder/encoder/mlp/wi_0/kernel" in old
 print("Split MLP:"							,       _lowerCAmelCase					)

 UpperCamelCase     : Union[str, Any]	     =       collections.OrderedDict()

 # Shared embeddings.
 UpperCamelCase     : Union[str, Any]	     =       old["token_embedder/embedding"]

 # Encoder.
 for i in range(_lowerCAmelCase					):
  # Block i, layer 0 (Self Attention).
  UpperCamelCase     : Tuple	     =       tax_layer_norm_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "encoder"							,       "pre_attention_layer_norm"					)
  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Dict	     =       tax_attention_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "encoder"							,       "attention"					)
  UpperCamelCase     : int	     =       layer_norm
  UpperCamelCase     : Dict	     =       k.T
  UpperCamelCase     : Any	     =       o.T
  UpperCamelCase     : Optional[int]	     =       q.T
  UpperCamelCase     : str	     =       v.T

  # Block i, layer 1 (MLP).
  UpperCamelCase     : int	     =       tax_layer_norm_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "encoder"							,       "pre_mlp_layer_norm"					)
  UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       tax_mlp_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "encoder"							,       _lowerCAmelCase					)
  UpperCamelCase     : Union[str, Any]	     =       layer_norm
  if split_mlp_wi:
   UpperCamelCase     : List[Any]	     =       wi[0].T
   UpperCamelCase     : Optional[Any]	     =       wi[1].T
  else:
   UpperCamelCase     : Any	     =       wi.T
  UpperCamelCase     : Tuple	     =       wo.T
  if scalable_attention:
   # convert the rel_embedding of each layer
   UpperCamelCase     : Optional[Any]	     =       tax_relpos_bias_lookup(
       _lowerCAmelCase							,       _lowerCAmelCase							,       "encoder"					).T

 UpperCamelCase     : Union[str, Any]	     =       old["encoder/encoder_norm/scale"]

 if not scalable_attention:
  UpperCamelCase     : Optional[int]	     =       tax_relpos_bias_lookup(
      _lowerCAmelCase							,       0							,       "encoder"					).T
  UpperCamelCase     : List[str]	     =       tax_relpos_bias_lookup(
      _lowerCAmelCase							,       0							,       "decoder"					).T

 if not is_encoder_only:
  # Decoder.
  for i in range(_lowerCAmelCase					):
   # Block i, layer 0 (Self Attention).
   UpperCamelCase     : Union[str, Any]	     =       tax_layer_norm_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"							,       "pre_self_attention_layer_norm"					)
   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : int	     =       tax_attention_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"							,       "self_attention"					)
   UpperCamelCase     : Any	     =       layer_norm
   UpperCamelCase     : int	     =       k.T
   UpperCamelCase     : Tuple	     =       o.T
   UpperCamelCase     : List[str]	     =       q.T
   UpperCamelCase     : List[str]	     =       v.T

   # Block i, layer 1 (Cross Attention).
   UpperCamelCase     : Tuple	     =       tax_layer_norm_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"							,       "pre_cross_attention_layer_norm"					)
   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Any	     =       tax_attention_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"							,       "encoder_decoder_attention"					)
   UpperCamelCase     : Optional[Any]	     =       layer_norm
   UpperCamelCase     : List[str]	     =       k.T
   UpperCamelCase     : Tuple	     =       o.T
   UpperCamelCase     : Any	     =       q.T
   UpperCamelCase     : Any	     =       v.T

   # Block i, layer 2 (MLP).
   UpperCamelCase     : Any	     =       tax_layer_norm_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"							,       "pre_mlp_layer_norm"					)
   UpperCamelCase     ,   UpperCamelCase     : Union[str, Any]	     =       tax_mlp_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"							,       _lowerCAmelCase					)
   UpperCamelCase     : Union[str, Any]	     =       layer_norm
   if split_mlp_wi:
    UpperCamelCase     : int	     =       wi[0].T
    UpperCamelCase     : Tuple	     =       wi[1].T
   else:
    UpperCamelCase     : Dict	     =       wi.T
   UpperCamelCase     : Optional[int]	     =       wo.T

   if scalable_attention:
    # convert the rel_embedding of each layer
    UpperCamelCase     : str	     =       tax_relpos_bias_lookup(_lowerCAmelCase							,       _lowerCAmelCase							,       "decoder"					).T

  UpperCamelCase     : List[Any]	     =       old["decoder/decoder_norm/scale"]

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   UpperCamelCase     : Any	     =       old["decoder/logits_dense/kernel"].T

 return new



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Any:
 UpperCamelCase     : Union[str, Any]	     =       collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  UpperCamelCase     : Union[str, Any]	     =       state_dict["shared.weight"]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   UpperCamelCase     : Any	     =       state_dict["shared.weight"]

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print("Using shared word embeddings as lm_head."					)
   UpperCamelCase     : Any	     =       state_dict["shared.weight"]

 return state_dict



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[Any]:
 UpperCamelCase     : Tuple	     =       checkpoints.load_tax_checkpoint(_lowerCAmelCase					)
 UpperCamelCase     : int	     =       convert_tax_to_pytorch(
     _lowerCAmelCase							,       num_layers=config.num_layers							,       is_encoder_only=_lowerCAmelCase							,       scalable_attention=_lowerCAmelCase					)
 UpperCamelCase     : List[Any]	     =       make_state_dict(_lowerCAmelCase							,       _lowerCAmelCase					)
 model.load_state_dict(_lowerCAmelCase							,       strict=_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase = False							,       _lowerCAmelCase = False							,       )						->			Tuple:
 UpperCamelCase     : List[Any]	     =       MTaConfig.from_json_file(_lowerCAmelCase					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  UpperCamelCase     : int	     =       UMTaEncoderModel(_lowerCAmelCase					)
 else:
  UpperCamelCase     : str	     =       UMTaForConditionalGeneration(_lowerCAmelCase					)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(_lowerCAmelCase					)

 # Verify that we can load the checkpoint.
 model.from_pretrained(_lowerCAmelCase					)
 print("Done"					)


if __name__ == "__main__":
    __lowerCamelCase	:					List[Any]			  =					argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
    )
    parser.add_argument(
        """--scalable_attention""",
        action="""store_true""",
        help="""Whether the model uses scaled attention (umt5 model)""",
        default=False,
    )
    __lowerCamelCase	:					Any			  =					parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path,
        args.config_file,
        args.pytorch_dump_path,
        args.is_encoder_only,
        args.scalable_attention,
    )







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates					)					):
  UpperCamelCase     : Any	     =       checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
  for j in range(len(config.resblock_dilation_sizes					)					):
   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCamelCase     : str	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()

 model.save_pretrained(_lowerCAmelCase					)

 if repo_id:
  print("Pushing to the hub..."					)
  model.push_to_hub(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
    parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
