
"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_A						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


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

		lowerCamelCase					:      int          =   tf.shape(__lowerCAmelCase      )

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

		lowerCamelCase					:      Optional[Any]          =   tensor.shape.as_list()

		return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCAmelCase      )]
def 		UpperCAmelCase (		a_,						a_ = None,						a_ = None      ):





		'''simple docstring'''


		return tf.nn.softmax(logits=logits + 1E-9,						axis=__lowerCAmelCase,						name=__lowerCAmelCase      )
def 		UpperCAmelCase (		a_,						a_,						a_,						a_=1E-5,						a_=-1      ):





		'''simple docstring'''


		if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCAmelCase,						__lowerCAmelCase      ):
				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
		lowerCamelCase					:      Optional[Any]          =   tf.nn.moments(__lowerCAmelCase,						axes=[axis],						keepdims=__lowerCAmelCase      )

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

		# Compute layer normalization using the batch_normalization
		# function.
		lowerCamelCase					:      Dict          =   tf.nn.batch_normalization(
		    __lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase,						offset=__lowerCAmelCase,						scale=__lowerCAmelCase,						variance_epsilon=__lowerCAmelCase,						)
		return outputs
def 		UpperCAmelCase (		a_,						a_=0,						a_=-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

		lowerCamelCase					:      Optional[Any]          =   tf.shape(__lowerCAmelCase      )
		lowerCamelCase					:      Union[str, Any]          =   tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]      )
		lowerCamelCase					:      List[Any]          =   tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]],						axis=0      )
		return tf.reshape(__lowerCAmelCase,						__lowerCAmelCase      )
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		if not isinstance(__lowerCAmelCase,						tf.Tensor      ):
				lowerCamelCase					:      List[Any]          =   tf.convert_to_tensor(__lowerCAmelCase      )  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				lowerCamelCase					:      List[Any]          =   encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				lowerCamelCase					:      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))
		lowerCamelCase					:      List[str]          =   (
		    tf.cast(1,						encoder_attention_mask.dtype      ) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask
def 		UpperCAmelCase (		a_,						a_,						a_ = "input_ids"      ):





		'''simple docstring'''


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





		'''simple docstring'''


		lowerCamelCase					:      str          =   6_4512
		# 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.
		lowerCamelCase					:      List[str]          =   [x for x in data if len(__lowerCAmelCase      ) > 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}"""      )

		lowerCamelCase					:      Optional[int]          =   np.asarray(__lowerCAmelCase      )

		lowerCamelCase					:      List[str]          =   1
		lowerCamelCase					:      List[Any]          =   np.array_split(__lowerCAmelCase,						__lowerCAmelCase      )

		# 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
				lowerCamelCase					:      Any          =   np.array_split(__lowerCAmelCase,						__lowerCAmelCase      )

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(__lowerCAmelCase      ):
						lowerCamelCase					:      int          =   chunk_data
		else:
				lowerCamelCase					:      Any          =   data
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


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





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


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

		return tf.nest.map_structure(_expand_single_ad_tensor,						__lowerCAmelCase      )








"""simple docstring"""

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_A						=			logging.get_logger('transformers.models.speecht5')
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		hf_model.apply_weight_norm()

		lowerCamelCase					:      str          =   checkpoint['input_conv.weight_g']
		lowerCamelCase					:      int          =   checkpoint['input_conv.weight_v']
		lowerCamelCase					:      Optional[Any]          =   checkpoint['input_conv.bias']

		for i in range(len(config.upsample_rates      )      ):
				lowerCamelCase					:      Tuple          =   checkpoint[F"""upsamples.{i}.1.weight_g"""]
				lowerCamelCase					:      Any          =   checkpoint[F"""upsamples.{i}.1.weight_v"""]
				lowerCamelCase					:      List[str]          =   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      )      ):
						lowerCamelCase					:      Optional[Any]          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
						lowerCamelCase					:      Tuple          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
						lowerCamelCase					:      Union[str, Any]          =   checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

						lowerCamelCase					:      Dict          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
						lowerCamelCase					:      str          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
						lowerCamelCase					:      Optional[Any]          =   checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

		lowerCamelCase					:      Any          =   checkpoint['output_conv.1.weight_g']
		lowerCamelCase					:      Tuple          =   checkpoint['output_conv.1.weight_v']
		lowerCamelCase					:      int          =   checkpoint['output_conv.1.bias']

		hf_model.remove_weight_norm()
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_=None,						a_=None,						):





		'''simple docstring'''


		if config_path is not None:
				lowerCamelCase					:      str          =   SpeechTaHifiGanConfig.from_pretrained(a_      )
		else:
				lowerCamelCase					:      Dict          =   SpeechTaHifiGanConfig()

		lowerCamelCase					:      int          =   SpeechTaHifiGan(a_      )

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

		lowerCamelCase					:      Tuple          =   np.load(a_      )
		lowerCamelCase					:      str          =   stats[0].reshape(-1      )
		lowerCamelCase					:      Optional[int]          =   stats[1].reshape(-1      )
		lowerCamelCase					:      Dict          =   torch.from_numpy(a_      ).float()
		lowerCamelCase					:      Optional[int]          =   torch.from_numpy(a_      ).float()

		model.save_pretrained(a_      )

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


if __name__ == "__main__":
					_A						=			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.'
					)

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






