




'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)




def   lowercase__(			__UpperCamelCase:					Union[tf.Tensor, np.ndarray]    ):

      """simple docstring"""






      if isinstance(__UpperCamelCase      ,np.ndarray    ):
            return list(tensor.shape    )

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tf.shape(__UpperCamelCase    )

      if tensor.shape == tf.TensorShape(__UpperCamelCase    ):
            return dynamic

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tensor.shape.as_list()

      return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCamelCase    )]




def   lowercase__(			__UpperCamelCase:					tf.Tensor      ,__UpperCamelCase:					Optional[int] = None      ,__UpperCamelCase:					Optional[str] = None    ):

      """simple docstring"""






      return tf.nn.softmax(logits=logits + 1e-9      ,axis=__UpperCamelCase      ,name=__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[Any]=1e-5      ,__UpperCamelCase:					Dict=-1    ):

      """simple docstring"""






      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCamelCase      ,__UpperCamelCase    ):
            raise NotImplementedError('Only 1D weight and bias tensors are supported for now, with only a single axis.'    )

      # Get mean and variance on the axis to be normalized
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						tf.nn.moments(__UpperCamelCase      ,axes=[axis]      ,keepdims=__UpperCamelCase    )

      if axis != -1:
            # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
            # on every dimension except axis
            SCREAMING_SNAKE_CASE   :      Any    					=						[1] * inputs.shape.rank
            SCREAMING_SNAKE_CASE   :      str    					=						shape_list(__UpperCamelCase    )[axis]
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tf.reshape(__UpperCamelCase      ,__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Any    					=						tf.reshape(__UpperCamelCase      ,__UpperCamelCase    )

      # Compute layer normalization using the batch_normalization
      # function.
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tf.nn.batch_normalization(
          __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,offset=__UpperCamelCase      ,scale=__UpperCamelCase      ,variance_epsilon=__UpperCamelCase      ,)
      return outputs




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str=0      ,__UpperCamelCase:					Optional[Any]=-1    ):

      """simple docstring"""






      if end_dim < 0:
            end_dim += input.shape.rank
      if start_dim < 0:
            start_dim += input.shape.rank

      if start_dim == end_dim:
            return input

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tf.shape(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,axis=0    )
      return tf.reshape(__UpperCamelCase      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					tf.Tensor    ):

      """simple docstring"""






      if not isinstance(__UpperCamelCase      ,tf.Tensor    ):
            SCREAMING_SNAKE_CASE   :      Any    					=						tf.convert_to_tensor(__UpperCamelCase    )  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            SCREAMING_SNAKE_CASE   :      Tuple    					=						encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            SCREAMING_SNAKE_CASE   :      Dict    					=						encoder_attention_mask[:, None, None, :]
      # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
      # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
      # /transformer/transformer_layers.py#L270
      # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
      # encoder_extended_attention_mask.transpose(-1, -2))
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						(
          tf.cast(1      ,encoder_attention_mask.dtype    ) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask




def   lowercase__(			__UpperCamelCase:					tf.Tensor      ,__UpperCamelCase:					int      ,__UpperCamelCase:					str = "input_ids"    ):

      """simple docstring"""






      tf.debugging.assert_less(
          __UpperCamelCase      ,tf.cast(__UpperCamelCase      ,dtype=tensor.dtype    )      ,message=(
              f"The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCamelCase    )}) must be smaller than the embedding "
              f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
          )      ,)




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						6_45_12
      # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
      # because in that case even chunking the array would not make the saving
      # possible.
      SCREAMING_SNAKE_CASE   :      int    					=						[x for x in data if len(__UpperCamelCase    ) > HDF5_OBJECT_HEADER_LIMIT]

      # Expecting this to never be true.
      if bad_attributes:
            raise RuntimeError(
                'The following attributes cannot be saved to HDF5 file because '
                f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
                f"bytes: {bad_attributes}"    )

      SCREAMING_SNAKE_CASE   :      List[str]    					=						np.asarray(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						1
      SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array_split(__UpperCamelCase      ,__UpperCamelCase    )

      # This will never loop forever thanks to the test above.
      while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data    ):
            num_chunks += 1
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						np.array_split(__UpperCamelCase      ,__UpperCamelCase    )

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						chunk_data
      else:
            SCREAMING_SNAKE_CASE   :      str    					=						data




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if name in group.attrs:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[n.decode('utf8'    ) if hasattr(__UpperCamelCase      ,'decode'    ) else n for n in group.attrs[name]]
      else:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
            SCREAMING_SNAKE_CASE   :      str    					=						0
            while "%s%d" % (name, chunk_id) in group.attrs:
                  data.extend(
                      [n.decode('utf8'    ) if hasattr(__UpperCamelCase      ,'decode'    ) else n for n in group.attrs['%s%d' % (name, chunk_id)]]    )
                  chunk_id += 1
      return data





def   lowercase__(			__UpperCamelCase:					Optional[int]    ):

      """simple docstring"""






      def _expand_single_ad_tensor(__UpperCamelCase:					int    ):
            if isinstance(__UpperCamelCase      ,tf.Tensor    ) and t.shape.rank == 1:
                  return tf.expand_dims(__UpperCamelCase      ,axis=-1    )
            return t

      return tf.nest.map_structure(_expand_single_ad_tensor      ,__UpperCamelCase    )








'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
UpperCamelCase_    	=				logging.get_logger("transformers.models.speecht5")




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any    ):

      """simple docstring"""






      hf_model.apply_weight_norm()

      SCREAMING_SNAKE_CASE   :      Any    					=						checkpoint['input_conv.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['input_conv.weight_v']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['input_conv.bias']

      for i in range(len(config.upsample_rates    )    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[f"upsamples.{i}.1.weight_g"]
            SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"upsamples.{i}.1.weight_v"]
            SCREAMING_SNAKE_CASE   :      Union[str, 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    )    ):
                  SCREAMING_SNAKE_CASE   :      int    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      str    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['output_conv.1.weight_g']
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						checkpoint['output_conv.1.weight_v']
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						checkpoint['output_conv.1.bias']

      hf_model.remove_weight_norm()




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					int      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					str=None      ,__UpperCamelCase:					Tuple=None      ,):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						SpeechTaHifiGanConfig()

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						SpeechTaHifiGan(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.load(__UpperCamelCase    )
      load_weights(orig_checkpoint['model']['generator']      ,__UpperCamelCase      ,__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      int    					=						np.load(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						stats[0].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						stats[1].reshape(-1    )
      SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.from_numpy(__UpperCamelCase    ).float()
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.from_numpy(__UpperCamelCase    ).float()

      model.save_pretrained(__UpperCamelCase    )

      if repo_id:
            print('Pushing to the hub...'    )
            model.push_to_hub(__UpperCamelCase    )


if __name__ == "__main__":
      UpperCamelCase_    	=				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."
      )

      UpperCamelCase_    	=				parser.parse_args()
      convert_hifigan_checkpoint(
          args.checkpoint_path,
          args.stats_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )


