
import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase_						: str         =  logging.get_logger(__name__)


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

lowerCamelCase_						: List[Any]         =  {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_						: str         =  {
    """allenai/led-base-16384""": 1_63_84,
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = VOCAB_FILES_NAMES
			__UpperCamelCase      :						Dict					      = PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase      :						Any					      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase      :						Union[str, Any]					      = LEDTokenizer
			__UpperCamelCase      :						str					      = ["""input_ids""", """attention_mask"""]

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

							UpperCamelCase_:							Tuple	=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
							if pre_tok_state.get("""add_prefix_space"""						,	snake_case_      ) != add_prefix_space:
											UpperCamelCase_:							Union[str, Any]	=						getattr(snake_case_						,	pre_tok_state.pop("""type"""      )      )
											UpperCamelCase_:							Optional[Any]	=						add_prefix_space
											UpperCamelCase_:							Optional[int]	=						pre_tok_class(**snake_case_      )

							UpperCamelCase_:							Dict	=						add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							UpperCamelCase_:							Union[str, Any]	=						"""post_processor"""
							UpperCamelCase_:							Tuple	=						getattr(self.backend_tokenizer						,	snake_case_						,	snake_case_      )
							if tokenizer_component_instance:
											UpperCamelCase_:							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:
															UpperCamelCase_:							List[Any]	=						tuple(state["""sep"""]      )
											if "cls" in state:
															UpperCamelCase_:							List[str]	=						tuple(state["""cls"""]      )

											UpperCamelCase_:							Optional[int]	=						False

											if state.get("""add_prefix_space"""						,	snake_case_      ) != add_prefix_space:
															UpperCamelCase_:							str	=						add_prefix_space
															UpperCamelCase_:							int	=						True

											if state.get("""trim_offsets"""						,	snake_case_      ) != trim_offsets:
															UpperCamelCase_:							Any	=						trim_offsets
															UpperCamelCase_:							int	=						True

											if changes_to_apply:
															UpperCamelCase_:							List[Any]	=						getattr(snake_case_						,	state.pop("""type"""      )      )
															UpperCamelCase_:							int	=						component_class(**snake_case_      )
															setattr(self.backend_tokenizer						,	snake_case_						,	snake_case_      )

			@property
			# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
			def       lowerCAmelCase__	(       self :       Tuple      ):
							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 :       Dict						,	snake_case_ :       int      ):
							UpperCamelCase_:							List[str]	=						AddedToken(snake_case_						,	lstrip=snake_case_						,	rstrip=snake_case_      ) if isinstance(snake_case_						,	snake_case_      ) else value
							UpperCamelCase_:							List[Any]	=						value

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	*snake_case_ :       Optional[Any]						,	**snake_case_ :       Union[str, Any]      ):
							UpperCamelCase_:							List[str]	=						kwargs.get("""is_split_into_words"""						,	snake_case_      )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs."""      )

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

			def       lowerCAmelCase__	(       self :       Optional[int]						,	*snake_case_ :       List[str]						,	**snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							List[Any]	=						kwargs.get("""is_split_into_words"""						,	snake_case_      )

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    """to use it with pretokenized inputs."""      )

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

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       str						,	snake_case_ :       Optional[str] = None      ):
							UpperCamelCase_:							Union[str, Any]	=						self._tokenizer.model.save(snake_case_						,	name=snake_case_      )
							return tuple(snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       str						,	snake_case_ :       List[str]=None      ):
							UpperCamelCase_:							List[str]	=						[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 :       Optional[Any]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = None      ):
							UpperCamelCase_:							int	=						[self.sep_token_id]
							UpperCamelCase_:							List[str]	=						[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]




			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Union[Dict[str, EncodedInput], BatchEncoding]						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       Optional[bool] = None						,	):
							UpperCamelCase_:							Tuple	=						super()._pad(
							    encoded_inputs=snake_case_						,	max_length=snake_case_						,	padding_strategy=snake_case_						,	pad_to_multiple_of=snake_case_						,	return_attention_mask=snake_case_						,	)

							# Load from model defaults
							if return_attention_mask is None:
											UpperCamelCase_:							Any	=						"""attention_mask""" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
											UpperCamelCase_:							List[str]	=						encoded_inputs[self.model_input_names[0]]
											# `global_attention_mask` need to have the same length as other (sequential) inputs.
											UpperCamelCase_:							List[str]	=						len(encoded_inputs["""global_attention_mask"""]      ) != len(snake_case_      )

											if needs_to_be_padded:
															UpperCamelCase_:							str	=						len(snake_case_      ) - len(encoded_inputs["""global_attention_mask"""]      )

															if self.padding_side == "right":
																			# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																			UpperCamelCase_:							List[Any]	=						(
																			    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																			)
															elif self.padding_side == "left":
																			UpperCamelCase_:							Tuple	=						[-1] * difference + encoded_inputs[
																			    """global_attention_mask"""
																			]
															else:
																			raise ValueError("""Invalid padding strategy:""" + str(self.padding_side      )      )

							return encoded_inputs








import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							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						)						):
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

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

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

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

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[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_						: Optional[int]         =  parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






