"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A  =				logging.get_logger(__name__)

__A  =				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A  =				{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__A  =				{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}
class  _snake_case     (					_UpperCAmelCase		):
						snake_case__			  =			VOCAB_FILES_NAMES
						snake_case__			  =			PRETRAINED_VOCAB_FILES_MAP
						snake_case__			  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__			  =			['''input_ids''', '''attention_mask''']
						snake_case__			  =			RobertaTokenizer






						def __init__(       self      :			str							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			Optional[Any]="replace"							,					UpperCAmelCase      :			Optional[int]="<s>"							,					UpperCAmelCase      :			int="</s>"							,					UpperCAmelCase      :			List[Any]="</s>"							,					UpperCAmelCase      :			str="<s>"							,					UpperCAmelCase      :			Tuple="<unk>"							,					UpperCAmelCase      :			List[str]="<pad>"							,					UpperCAmelCase      :			Optional[int]="<mask>"							,					UpperCAmelCase      :			str=False							,					UpperCAmelCase      :			Union[str, Any]=True							,					**UpperCAmelCase      :			Dict							,					):
													super().__init__(
													    A_							,					A_							,					tokenizer_file=A_							,					errors=A_							,					bos_token=A_							,					eos_token=A_							,					sep_token=A_							,					cls_token=A_							,					unk_token=A_							,					pad_token=A_							,					mask_token=A_							,					add_prefix_space=A_							,					trim_offsets=A_							,					**A_							,					)

													__lowerCamelCase					:		Tuple		   =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
													if pre_tok_state.get("add_prefix_space"							,					A_    ) != add_prefix_space:
																				__lowerCamelCase					:		Tuple		   =     getattr(A_							,					pre_tok_state.pop("type"    )    )
																				__lowerCamelCase					:		Any		   =     add_prefix_space
																				__lowerCamelCase					:		Tuple		   =     pre_tok_class(**A_    )

													__lowerCamelCase					:		int		   =     add_prefix_space

													__lowerCamelCase					:		Dict		   =     "post_processor"
													__lowerCamelCase					:		Any		   =     getattr(self.backend_tokenizer							,					A_							,					A_    )
													if tokenizer_component_instance:
																				__lowerCamelCase					:		int		   =     json.loads(tokenizer_component_instance.__getstate__()    )

																				# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																				if "sep" in state:
																											__lowerCamelCase					:		Optional[int]		   =     tuple(state["sep"]    )
																				if "cls" in state:
																											__lowerCamelCase					:		List[str]		   =     tuple(state["cls"]    )

																				__lowerCamelCase					:		int		   =     False

																				if state.get("add_prefix_space"							,					A_    ) != add_prefix_space:
																											__lowerCamelCase					:		str		   =     add_prefix_space
																											__lowerCamelCase					:		Dict		   =     True

																				if state.get("trim_offsets"							,					A_    ) != trim_offsets:
																											__lowerCamelCase					:		Tuple		   =     trim_offsets
																											__lowerCamelCase					:		Optional[Any]		   =     True

																				if changes_to_apply:
																											__lowerCamelCase					:		List[str]		   =     getattr(A_							,					state.pop("type"    )    )
																											__lowerCamelCase					:		Union[str, Any]		   =     component_class(**A_    )
																											setattr(self.backend_tokenizer							,					A_							,					A_    )






						@property
						def       lowerCamelCase__     (       self      :			List[str]    ):
													if self._mask_token is None:
																				if self.verbose:
																											logger.error("Using mask_token, but it is not set yet."    )
																				return None
													return str(self._mask_token    )






						@mask_token.setter
						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			List[str]    ):
													__lowerCamelCase					:		List[Any]		   =     AddedToken(A_							,					lstrip=A_							,					rstrip=A_    ) if isinstance(A_							,					A_    ) else value
													__lowerCamelCase					:		Optional[Any]		   =     value






						def       lowerCamelCase__     (       self      :			Dict							,					*UpperCAmelCase      :			Optional[int]							,					**UpperCAmelCase      :			Tuple    ):
													__lowerCamelCase					:		str		   =     kwargs.get("is_split_into_words"							,					A_    )
													assert self.add_prefix_space or not is_split_into_words, (
													    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*A_							,					**A_    )






						def       lowerCamelCase__     (       self      :			str							,					*UpperCAmelCase      :			Dict							,					**UpperCAmelCase      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[Any]		   =     kwargs.get("is_split_into_words"							,					A_    )

													assert self.add_prefix_space or not is_split_into_words, (
													    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*A_							,					**A_    )






						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[str] = None    ):
													__lowerCamelCase					:		List[str]		   =     self._tokenizer.model.save(A_							,					name=A_    )
													return tuple(A_    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		Optional[int]		   =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
													if token_ids_a is None:
																				return output

													return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None    ):
													__lowerCamelCase					:		Optional[int]		   =     [self.sep_token_id]
													__lowerCamelCase					:		Dict		   =     [self.cls_token_id]

													if token_ids_a is None:
																				return len(cls + token_ids_a + sep    ) * [0]
													return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
"""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    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

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

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     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    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		List[str]		   =     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__":
				__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,
				)