


'''simple docstring'''




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCAmelCase  :		Tuple		     =	logging.getLogger(__name__)



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					str
	_lowerCamelCase   :					List[str]
	_lowerCamelCase   :					Optional[List[str]]



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					List[int]
	_lowerCamelCase   :					List[int]
	_lowerCamelCase   :					Optional[List[int]]			      = None
	_lowerCamelCase   :					Optional[List[int]]			      = None



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					List[str]			      = """train"""
	_lowerCamelCase   :					Optional[int]			      = """dev"""
	_lowerCamelCase   :					Tuple			      = """test"""



class  lowerCAmelCase				:



	@staticmethod
	def 				lowercase     (					snake_case__ ,					snake_case__    ):
		raise NotImplementedError



	@staticmethod
	def 				lowercase     (					snake_case__    ):
		raise NotImplementedError



	@staticmethod
	def 				lowercase     (					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=False ,					snake_case__="[CLS]" ,					snake_case__=1 ,					snake_case__="[SEP]" ,					snake_case__=False ,					snake_case__=False ,					snake_case__=0 ,					snake_case__=0 ,					snake_case__=-100 ,					snake_case__=0 ,					snake_case__=True ,					):
		lowerCAmelCase					:    Optional[int]      							= {label: i for i, label in enumerate(snake_case__    )}

		lowerCAmelCase					:    Tuple      							= []
		for ex_index, example in enumerate(snake_case__    ):
			if ex_index % 1_0000 == 0:
				logger.info('Writing example %d of %d' ,					snake_case__ ,					len(snake_case__    )    )

			lowerCAmelCase					:    Dict      							= []
			lowerCAmelCase					:    Union[str, Any]      							= []
			for word, label in zip(example.words ,					example.labels    ):
				lowerCAmelCase					:    Any      							= tokenizer.tokenize(snake_case__    )

				# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
				if len(snake_case__    ) > 0:
					tokens.extend(snake_case__    )
					# Use the real label id for the first token of the word, and padding ids for the remaining tokens
					label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case__    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
			lowerCAmelCase					:    Optional[int]      							= tokenizer.num_special_tokens_to_add()
			if len(snake_case__    ) > max_seq_length - special_tokens_count:
				lowerCAmelCase					:    Optional[Any]      							= tokens[: (max_seq_length - special_tokens_count)]
				lowerCAmelCase					:    List[Any]      							= label_ids[: (max_seq_length - special_tokens_count)]

			# The convention in BERT is:
			# (a) For sequence pairs:
			#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
			#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
			# (b) For single sequences:
			#  tokens:   [CLS] the dog is hairy . [SEP]
			#  type_ids:   0   0   0   0  0     0   0
			#
			# Where "type_ids" are used to indicate whether this is the first
			# sequence or the second sequence. The embedding vectors for `type=0` and
			# `type=1` were learned during pre-training and are added to the wordpiece
			# embedding vector (and position vector). This is not *strictly* necessary
			# since the [SEP] token unambiguously separates the sequences, but it makes
			# it easier for the model to learn the concept of sequences.
			#
			# For classification tasks, the first vector (corresponding to [CLS]) is
			# used as the "sentence vector". Note that this only makes sense because
			# the entire model is fine-tuned.
			tokens += [sep_token]
			label_ids += [pad_token_label_id]
			if sep_token_extra:
				# roberta uses an extra separator b/w pairs of sentences
				tokens += [sep_token]
				label_ids += [pad_token_label_id]
			lowerCAmelCase					:    Optional[Any]      							= [sequence_a_segment_id] * len(snake_case__    )

			if cls_token_at_end:
				tokens += [cls_token]
				label_ids += [pad_token_label_id]
				segment_ids += [cls_token_segment_id]
			else:
				lowerCAmelCase					:    List[str]      							= [cls_token] + tokens
				lowerCAmelCase					:    List[str]      							= [pad_token_label_id] + label_ids
				lowerCAmelCase					:    List[str]      							= [cls_token_segment_id] + segment_ids

			lowerCAmelCase					:    Optional[Any]      							= tokenizer.convert_tokens_to_ids(snake_case__    )

			# The mask has 1 for real tokens and 0 for padding tokens. Only real
			# tokens are attended to.
			lowerCAmelCase					:    Dict      							= [1 if mask_padding_with_zero else 0] * len(snake_case__    )

			# Zero-pad up to the sequence length.
			lowerCAmelCase					:    Any      							= max_seq_length - len(snake_case__    )
			if pad_on_left:
				lowerCAmelCase					:    Union[str, Any]      							= ([pad_token] * padding_length) + input_ids
				lowerCAmelCase					:    Tuple      							= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
				lowerCAmelCase					:    Union[str, Any]      							= ([pad_token_segment_id] * padding_length) + segment_ids
				lowerCAmelCase					:    Any      							= ([pad_token_label_id] * padding_length) + label_ids
			else:
				input_ids += [pad_token] * padding_length
				input_mask += [0 if mask_padding_with_zero else 1] * padding_length
				segment_ids += [pad_token_segment_id] * padding_length
				label_ids += [pad_token_label_id] * padding_length

			assert len(snake_case__    ) == max_seq_length
			assert len(snake_case__    ) == max_seq_length
			assert len(snake_case__    ) == max_seq_length
			assert len(snake_case__    ) == max_seq_length

			if ex_index < 5:
				logger.info('*** Example ***'    )
				logger.info('guid: %s' ,					example.guid    )
				logger.info('tokens: %s' ,					' '.join([str(snake_case__    ) for x in tokens]    )    )
				logger.info('input_ids: %s' ,					' '.join([str(snake_case__    ) for x in input_ids]    )    )
				logger.info('input_mask: %s' ,					' '.join([str(snake_case__    ) for x in input_mask]    )    )
				logger.info('segment_ids: %s' ,					' '.join([str(snake_case__    ) for x in segment_ids]    )    )
				logger.info('label_ids: %s' ,					' '.join([str(snake_case__    ) for x in label_ids]    )    )

			if "token_type_ids" not in tokenizer.model_input_names:
				lowerCAmelCase					:    Optional[Any]      							= None

			features.append(
			    InputFeatures(
			        input_ids=snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					label_ids=snake_case__    )    )
		return features


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset



				class  lowerCAmelCase				(				a							):
					_lowerCamelCase   :					List[InputFeatures]
					_lowerCamelCase   :					int			      = nn.CrossEntropyLoss().ignore_index



					def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ = None ,					snake_case__=False ,					snake_case__ = Split.train ,					):
						# Load data features from cache or dataset file
						lowerCAmelCase					:    List[Any]      							= os.path.join(
						    snake_case__ ,					'cached_{}_{}_{}'.format(mode.value ,					tokenizer.__class__.__name__ ,					str(snake_case__    )    ) ,					)

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						lowerCAmelCase					:    str      							= cached_features_file + '.lock'
						with FileLock(snake_case__    ):
							if os.path.exists(snake_case__    ) and not overwrite_cache:
								logger.info(f"Loading features from cached file {cached_features_file}"    )
								lowerCAmelCase					:    Any      							= torch.load(snake_case__    )
							else:
								logger.info(f"Creating features from dataset file at {data_dir}"    )
								lowerCAmelCase					:    Union[str, Any]      							= token_classification_task.read_examples_from_file(snake_case__ ,					snake_case__    )
								# TODO clean up all this to leverage built-in features of tokenizers
								lowerCAmelCase					:    str      							= token_classification_task.convert_examples_to_features(
								    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					cls_token_at_end=bool(model_type in ['xlnet']    ) ,					cls_token=tokenizer.cls_token ,					cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,					sep_token=tokenizer.sep_token ,					sep_token_extra=snake_case__ ,					pad_on_left=bool(tokenizer.padding_side == 'left'    ) ,					pad_token=tokenizer.pad_token_id ,					pad_token_segment_id=tokenizer.pad_token_type_id ,					pad_token_label_id=self.pad_token_label_id ,					)
								logger.info(f"Saving features into cached file {cached_features_file}"    )
								torch.save(self.features ,					snake_case__    )



					def __len__(					self    ):
						return len(self.features    )



					def __getitem__(					self ,					snake_case__    ):
						return self.features[i]


if is_tf_available():
				import tensorflow as tf



				class  lowerCAmelCase				:
					_lowerCamelCase   :					List[InputFeatures]
					_lowerCamelCase   :					int			      = -100



					def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ = None ,					snake_case__=False ,					snake_case__ = Split.train ,					):
						lowerCAmelCase					:    Any      							= token_classification_task.read_examples_from_file(snake_case__ ,					snake_case__    )
						# TODO clean up all this to leverage built-in features of tokenizers
						lowerCAmelCase					:    Dict      							= token_classification_task.convert_examples_to_features(
						    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					cls_token_at_end=bool(model_type in ['xlnet']    ) ,					cls_token=tokenizer.cls_token ,					cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,					sep_token=tokenizer.sep_token ,					sep_token_extra=snake_case__ ,					pad_on_left=bool(tokenizer.padding_side == 'left'    ) ,					pad_token=tokenizer.pad_token_id ,					pad_token_segment_id=tokenizer.pad_token_type_id ,					pad_token_label_id=self.pad_token_label_id ,					)

						def gen():
							for ex in self.features:
								if ex.token_type_ids is None:
									yield (
									    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
									    ex.label_ids,
									)
								else:
									yield (
									    {
									        "input_ids": ex.input_ids,
									        "attention_mask": ex.attention_mask,
									        "token_type_ids": ex.token_type_ids,
									    },
									    ex.label_ids,
									)

						if "token_type_ids" not in tokenizer.model_input_names:
							lowerCAmelCase					:    List[Any]      							= tf.data.Dataset.from_generator(
							    snake_case__ ,					({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,					(
							        {'input_ids': tf.TensorShape([None]    ), 'attention_mask': tf.TensorShape([None]    )},
							        tf.TensorShape([None]    ),
							    ) ,					)
						else:
							lowerCAmelCase					:    Tuple      							= tf.data.Dataset.from_generator(
							    snake_case__ ,					({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,					(
							        {
							            'input_ids': tf.TensorShape([None]    ),
							            'attention_mask': tf.TensorShape([None]    ),
							            'token_type_ids': tf.TensorShape([None]    ),
							        },
							        tf.TensorShape([None]    ),
							    ) ,					)



					def 				lowercase     (					self    ):
						lowerCAmelCase					:    List[str]      							= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

						return self.dataset



					def __len__(					self    ):
						return len(self.features    )



					def __getitem__(					self ,					snake_case__    ):
						return self.features[i]




'''simple docstring'''




import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase  :		List[str]		     =	logging.get_logger('transformers.models.speecht5')

def 			__UpperCamelCase    (    _A    :				Any					,   _A    :				Dict					,   _A    :				Any				)    ->      Union[str, Any]:




	"""simple docstring"""





	hf_model.apply_weight_norm()

	lowerCAmelCase					:    int      							= checkpoint['input_conv.weight_g']
	lowerCAmelCase					:    Optional[int]      							= checkpoint['input_conv.weight_v']
	lowerCAmelCase					:    Dict      							= checkpoint['input_conv.bias']

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

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

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

	hf_model.remove_weight_norm()

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Union[str, Any]					,   _A    :				List[Any]					,   _A    :				Any=None					,   _A    :				Any=None					,   )    ->      Dict:




	"""simple docstring"""





	if config_path is not None:
		lowerCAmelCase					:    Dict      							= SpeechTaHifiGanConfig.from_pretrained(_A				)
	else:
		lowerCAmelCase					:    Union[str, Any]      							= SpeechTaHifiGanConfig()

	lowerCAmelCase					:    List[Any]      							= SpeechTaHifiGan(_A				)

	lowerCAmelCase					:    List[str]      							= torch.load(_A				)
	load_weights(orig_checkpoint['model']['generator']					,   _A					,   _A				)

	lowerCAmelCase					:    Tuple      							= np.load(_A				)
	lowerCAmelCase					:    List[Any]      							= stats[0].reshape(-1				)
	lowerCAmelCase					:    int      							= stats[1].reshape(-1				)
	lowerCAmelCase					:    Union[str, Any]      							= torch.from_numpy(_A				).float()
	lowerCAmelCase					:    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__":
				_lowerCAmelCase  :		List[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  :		Union[str, Any]		     =	parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)
