


'''simple docstring'''




import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Any			      = OpenAIGPTTokenizer
	_lowerCamelCase   :					Optional[Any]			      = OpenAIGPTTokenizerFast
	_lowerCamelCase   :					Optional[int]			      = True
	_lowerCamelCase   :					int			      = False



	def 				lowercase     (					self    ):
		super().setUp()

		# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
		lowerCAmelCase					:    List[Any]      							= [
		    'l',
		    'o',
		    'w',
		    'e',
		    'r',
		    's',
		    't',
		    'i',
		    'd',
		    'n',
		    'w</w>',
		    'r</w>',
		    't</w>',
		    'lo',
		    'low',
		    'er</w>',
		    'low</w>',
		    'lowest</w>',
		    'newer</w>',
		    'wider</w>',
		    '<unk>',
		]
		lowerCAmelCase					:    Tuple      							= dict(zip(snake_case__ ,					range(len(snake_case__    )    )    )    )
		lowerCAmelCase					:    int      							= ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

		lowerCAmelCase					:    Dict      							= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['vocab_file']    )
		lowerCAmelCase					:    Dict      							= os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['merges_file']    )
		with open(self.vocab_file ,					'w'    ) as fp:
			fp.write(json.dumps(snake_case__    )    )
		with open(self.merges_file ,					'w'    ) as fp:
			fp.write('\n'.join(snake_case__    )    )



	def 				lowercase     (					self ,					snake_case__    ):
		return "lower newer", "lower newer"



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Optional[int]      							= OpenAIGPTTokenizer(self.vocab_file ,					self.merges_file    )

		lowerCAmelCase					:    Union[str, Any]      							= 'lower'
		lowerCAmelCase					:    List[str]      							= ['low', 'er</w>']
		lowerCAmelCase					:    Union[str, Any]      							= tokenizer.tokenize(snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )

		lowerCAmelCase					:    Dict      							= tokens + ['<unk>']
		lowerCAmelCase					:    List[str]      							= [14, 15, 20]
		self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    ) ,					snake_case__    )



	def 				lowercase     (					self ,					snake_case__=15    ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"    ):
				lowerCAmelCase					:    List[str]      							= self.rust_tokenizer_class.from_pretrained(snake_case__ ,					**snake_case__    )

				# Simple input
				lowerCAmelCase					:    Any      							= 'This is a simple input'
				lowerCAmelCase					:    int      							= ['This is a simple input 1', 'This is a simple input 2']
				lowerCAmelCase					:    Dict      							= ('This is a simple input', 'This is a pair')
				lowerCAmelCase					:    List[Any]      							= [
				    ('This is a simple input 1', 'This is a simple input 2'),
				    ('This is a simple pair 1', 'This is a simple pair 2'),
				]

				# Simple input tests
				self.assertRaises(snake_case__ ,					tokenizer_r.encode ,					snake_case__ ,					max_length=snake_case__ ,					padding='max_length'    )

				# Simple input
				self.assertRaises(snake_case__ ,					tokenizer_r.encode_plus ,					snake_case__ ,					max_length=snake_case__ ,					padding='max_length'    )

				# Simple input
				self.assertRaises(
				    snake_case__ ,					tokenizer_r.batch_encode_plus ,					snake_case__ ,					max_length=snake_case__ ,					padding='max_length' ,					)

				# Pair input
				self.assertRaises(snake_case__ ,					tokenizer_r.encode ,					snake_case__ ,					max_length=snake_case__ ,					padding='max_length'    )

				# Pair input
				self.assertRaises(snake_case__ ,					tokenizer_r.encode_plus ,					snake_case__ ,					max_length=snake_case__ ,					padding='max_length'    )

				# Pair input
				self.assertRaises(
				    snake_case__ ,					tokenizer_r.batch_encode_plus ,					snake_case__ ,					max_length=snake_case__ ,					padding='max_length' ,					)



	def 				lowercase     (					self    ):
		pass



@require_ftfy
@require_spacy
@require_tokenizers
class  lowerCAmelCase				(				a							):
	pass




'''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,
				)
