

import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:str						=     logging.get_logger("""transformers.models.speecht5""")




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    hf_model.apply_weight_norm()

    UpperCamelCase_			  =						checkpoint["input_conv.weight_g"]
    UpperCamelCase_			  =						checkpoint["input_conv.weight_v"]
    UpperCamelCase_			  =						checkpoint["input_conv.bias"]

    for i in range(len(config.upsample_rates			)			):
        UpperCamelCase_			  =						checkpoint[f"upsamples.{i}.1.weight_g"]
        UpperCamelCase_			  =						checkpoint[f"upsamples.{i}.1.weight_v"]
        UpperCamelCase_			  =						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_			  =						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
            UpperCamelCase_			  =						checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
            UpperCamelCase_			  =						checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]

            UpperCamelCase_			  =						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
            UpperCamelCase_			  =						checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
            UpperCamelCase_			  =						checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]

    UpperCamelCase_			  =						checkpoint["output_conv.1.weight_g"]
    UpperCamelCase_			  =						checkpoint["output_conv.1.weight_v"]
    UpperCamelCase_			  =						checkpoint["output_conv.1.bias"]

    hf_model.remove_weight_norm()




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			,						)->						Any:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_			  =						SpeechTaHifiGanConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						SpeechTaHifiGanConfig()

    UpperCamelCase_			  =						SpeechTaHifiGan(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			)
    load_weights(orig_checkpoint["model"]["generator"]			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						np.load(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						stats[0].reshape(-1			)
    UpperCamelCase_			  =						stats[1].reshape(-1			)
    UpperCamelCase_			  =						torch.from_numpy(SCREAMING_SNAKE_CASE_			).float()
    UpperCamelCase_			  =						torch.from_numpy(SCREAMING_SNAKE_CASE_			).float()

    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if repo_id:
        print("Pushing to the hub..."			)
        model.push_to_hub(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Optional[int]						=     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."""
 )

 SCREAMING_SNAKE_CASE		:Tuple						=     parser.parse_args()
 convert_hifigan_checkpoint(
     args.checkpoint_path,
     args.stats_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      LayoutLMTokenizer
     UpperCamelCase_	:Dict										=      LayoutLMTokenizerFast
     UpperCamelCase_	:List[str]										=      True
     UpperCamelCase_	:Dict										=      True






     def 	UpperCAmelCase_				(				self	)->     str:
         super().setUp()

         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						"UNwant\u00E9d,running"
         UpperCamelCase_			  =						"unwanted, running"
         return input_text, output_text






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("UNwant\u00E9d,running"	)
         self.assertListEqual(_lowercase		,						["un", "##want", "##ed", ",", "runn", "##ing"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase	)		,						[7, 4, 5, 10, 8, 9]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         pass
