
'''simple docstring'''







import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
					from test_module.custom_tokenization_fast import CustomTokenizerFast


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		0


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
									self.assertIsNotNone(lowerCAmelCase_  )
									self.assertIsInstance(lowerCAmelCase_    ,   (BertTokenizer, BertTokenizerFast)  )
									self.assertGreater(len(lowerCAmelCase_  )    ,   0  )

								for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
									self.assertIsNotNone(lowerCAmelCase_  )
									self.assertIsInstance(lowerCAmelCase_    ,   (GPTaTokenizer, GPTaTokenizerFast)  )
									self.assertGreater(len(lowerCAmelCase_  )    ,   0  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
								self.assertIsInstance(lowerCAmelCase_    ,   (BertTokenizer, BertTokenizerFast)  )
								self.assertEqual(tokenizer.vocab_size    ,   12  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
								self.assertIsInstance(lowerCAmelCase_    ,   (RobertaTokenizer, RobertaTokenizerFast)  )
								self.assertEqual(tokenizer.vocab_size    ,   20  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoConfig.from_pretrained(lowerCAmelCase_  )
								self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )
								# Check that tokenizer_type ≠ model_type
								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   config=lowerCAmelCase_  )
								self.assertIsInstance(lowerCAmelCase_    ,   (BertTokenizer, BertTokenizerFast)  )
								self.assertEqual(tokenizer.vocab_size    ,   12  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy('./tests/fixtures/vocab.txt'    ,   os.path.join(lowerCAmelCase_    ,   'vocab.txt'  )  )

									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   tokenizer_type='bert'    ,   use_fast=lowerCAmelCase_  )
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )

								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy('./tests/fixtures/vocab.json'    ,   os.path.join(lowerCAmelCase_    ,   'vocab.json'  )  )
									shutil.copy('./tests/fixtures/merges.txt'    ,   os.path.join(lowerCAmelCase_    ,   'merges.txt'  )  )

									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   tokenizer_type='gpt2'    ,   use_fast=lowerCAmelCase_  )
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy('./tests/fixtures/vocab.txt'    ,   os.path.join(lowerCAmelCase_    ,   'vocab.txt'  )  )

									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   tokenizer_type='bert'  )
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )

								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy('./tests/fixtures/vocab.json'    ,   os.path.join(lowerCAmelCase_    ,   'vocab.json'  )  )
									shutil.copy('./tests/fixtures/merges.txt'    ,   os.path.join(lowerCAmelCase_    ,   'merges.txt'  )  )

									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   tokenizer_type='gpt2'  )
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with pytest.raises(lowerCAmelCase_  ):
									AutoTokenizer.from_pretrained('./'    ,   tokenizer_type='xxx'  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
									_snake_case   						=		tokenizer_class.from_pretrained('wietsedv/bert-base-dutch-cased'  )
									self.assertIsInstance(lowerCAmelCase_    ,   (BertTokenizer, BertTokenizerFast)  )

									if isinstance(lowerCAmelCase_    ,   lowerCAmelCase_  ):
										self.assertEqual(tokenizer.basic_tokenizer.do_lower_case    ,   lowerCAmelCase_  )
									else:
										self.assertEqual(tokenizer.do_lower_case    ,   lowerCAmelCase_  )

									self.assertEqual(tokenizer.model_max_length    ,   5_12  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
									with self.assertRaisesRegex(
									    lowerCAmelCase_    ,   'julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'    ,   ):
										_snake_case   						=		tokenizer_class.from_pretrained('julien-c/herlolip-not-exists'  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TOKENIZER_MAPPING.values()
								_snake_case   						=		[]

								for slow_tok, fast_tok in tokenizers:
									if slow_tok is not None:
										tokenizer_names.append(slow_tok.__name__  )

									if fast_tok is not None:
										tokenizer_names.append(fast_tok.__name__  )

								for tokenizer_name in tokenizer_names:
									# must find the right class
									tokenizer_class_from_name(lowerCAmelCase_  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'    ,   use_fast=lowerCAmelCase_  )    ,   lowerCAmelCase_  )
								self.assertIsInstance(AutoTokenizer.from_pretrained('bert-base-cased'  )    ,   lowerCAmelCase_  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('distilbert-base-uncased'    ,   do_lower_case=lowerCAmelCase_  )
								_snake_case   						=		'Hello, world. How are you?'
								_snake_case   						=		tokenizer.tokenize(lowerCAmelCase_  )
								self.assertEqual('[UNK]'    ,   tokens[0]  )

								_snake_case   						=		AutoTokenizer.from_pretrained('microsoft/mpnet-base'    ,   do_lower_case=lowerCAmelCase_  )
								_snake_case   						=		tokenizer.tokenize(lowerCAmelCase_  )
								self.assertEqual('[UNK]'    ,   tokens[0]  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('robot-test/dummy-tokenizer-fast-with-model-config'  )
								self.assertEqual(type(lowerCAmelCase_  )    ,   lowerCAmelCase_  )
								self.assertEqual(tokenizer.model_max_length    ,   5_12  )
								self.assertEqual(tokenizer.vocab_size    ,   3_00_00  )
								self.assertEqual(tokenizer.unk_token    ,   '[UNK]'  )
								self.assertEqual(tokenizer.padding_side    ,   'right'  )
								self.assertEqual(tokenizer.truncation_side    ,   'right'  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
								self.assertIsInstance(lowerCAmelCase_    ,   (BertTokenizer, BertTokenizerFast)  )
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )

								self.assertIsInstance(lowerCAmelCase_    ,   tokenizer.__class__  )
								self.assertEqual(tokenizera.vocab_size    ,   12  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('ctrl'  )
								# There is no fast CTRL so this always gives us a slow tokenizer.
								self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		get_tokenizer_config('bert-base-cased'  )
								_snake_case   						=		config.pop('_commit_hash'    ,   lowerCAmelCase_  )
								# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
								self.assertEqual(lowerCAmelCase_    ,   {'do_lower_case': False}  )

								# This model does not have a tokenizer_config so we get back an empty dict.
								_snake_case   						=		get_tokenizer_config(lowerCAmelCase_  )
								self.assertDictEqual(lowerCAmelCase_    ,   {}  )

								# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
								_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		get_tokenizer_config(lowerCAmelCase_  )

								# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
								self.assertEqual(config['tokenizer_class']    ,   'BertTokenizer'  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								try:
									AutoConfig.register('custom'    ,   lowerCAmelCase_  )

									AutoTokenizer.register(lowerCAmelCase_    ,   slow_tokenizer_class=lowerCAmelCase_  )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(lowerCAmelCase_  ):
										AutoTokenizer.register(lowerCAmelCase_    ,   slow_tokenizer_class=lowerCAmelCase_  )

									_snake_case   						=		CustomTokenizer.from_pretrained(lowerCAmelCase_  )
									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(lowerCAmelCase_  )

										_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
										self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )

								finally:
									if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
										del TOKENIZER_MAPPING._extra_content[CustomConfig]


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								try:
									AutoConfig.register('custom'    ,   lowerCAmelCase_  )

									# Can register in two steps
									AutoTokenizer.register(lowerCAmelCase_    ,   slow_tokenizer_class=lowerCAmelCase_  )
									self.assertEqual(TOKENIZER_MAPPING[CustomConfig]    ,   (CustomTokenizer, None)  )
									AutoTokenizer.register(lowerCAmelCase_    ,   fast_tokenizer_class=lowerCAmelCase_  )
									self.assertEqual(TOKENIZER_MAPPING[CustomConfig]    ,   (CustomTokenizer, CustomTokenizerFast)  )

									del TOKENIZER_MAPPING._extra_content[CustomConfig]
									# Can register in one step
									AutoTokenizer.register(
									    lowerCAmelCase_    ,   slow_tokenizer_class=lowerCAmelCase_    ,   fast_tokenizer_class=lowerCAmelCase_  )
									self.assertEqual(TOKENIZER_MAPPING[CustomConfig]    ,   (CustomTokenizer, CustomTokenizerFast)  )

									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(lowerCAmelCase_  ):
										AutoTokenizer.register(lowerCAmelCase_    ,   fast_tokenizer_class=lowerCAmelCase_  )

									# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
									# and that model does not have a tokenizer.json
									with tempfile.TemporaryDirectory() as tmp_dir:
										_snake_case   						=		BertTokenizerFast.from_pretrained(lowerCAmelCase_  )
										bert_tokenizer.save_pretrained(lowerCAmelCase_  )
										_snake_case   						=		CustomTokenizerFast.from_pretrained(lowerCAmelCase_  )

									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(lowerCAmelCase_  )

										_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_  )
										self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )

										_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   use_fast=lowerCAmelCase_  )
										self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )

								finally:
									if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
										del TOKENIZER_MAPPING._extra_content[CustomConfig]


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with self.assertRaises(lowerCAmelCase_  ):
									_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'  )
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(lowerCAmelCase_  ):
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_  )

								_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_  )
								self.assertTrue(tokenizer.special_attribute_present  )
								# Test tokenizer can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   trust_remote_code=lowerCAmelCase_  )
								self.assertTrue(reloaded_tokenizer.special_attribute_present  )

								if is_tokenizers_available():
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizerFast'  )
									self.assertEqual(reloaded_tokenizer.__class__.__name__    ,   'NewTokenizerFast'  )

									# Test we can also load the slow version
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_    ,   use_fast=lowerCAmelCase_  )
									self.assertTrue(tokenizer.special_attribute_present  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )
									# Test tokenizer can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(lowerCAmelCase_  )
										_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   trust_remote_code=lowerCAmelCase_    ,   use_fast=lowerCAmelCase_  )
									self.assertEqual(reloaded_tokenizer.__class__.__name__    ,   'NewTokenizer'  )
									self.assertTrue(reloaded_tokenizer.special_attribute_present  )
								else:
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )
									self.assertEqual(reloaded_tokenizer.__class__.__name__    ,   'NewTokenizer'  )


							@require_tokenizers
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								class 						__UpperCAmelCase      (					_lowerCamelCase						):
															__lowercase      		=       False


								class 						__UpperCAmelCase      (					_lowerCamelCase						):
															__lowercase      		=       NewTokenizer
															__lowercase      		=       False

								try:
									AutoConfig.register('custom'    ,   lowerCAmelCase_  )
									AutoTokenizer.register(lowerCAmelCase_    ,   slow_tokenizer_class=lowerCAmelCase_  )
									AutoTokenizer.register(lowerCAmelCase_    ,   fast_tokenizer_class=lowerCAmelCase_  )
									# If remote code is not set, the default is to use local
									_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizerFast'  )
									self.assertFalse(tokenizer.special_attribute_present  )
									_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/test_dynamic_tokenizer'    ,   use_fast=lowerCAmelCase_  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )
									self.assertFalse(tokenizer.special_attribute_present  )

									# If remote code is disabled, we load the local one.
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizerFast'  )
									self.assertFalse(tokenizer.special_attribute_present  )
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_    ,   use_fast=lowerCAmelCase_  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )
									self.assertFalse(tokenizer.special_attribute_present  )

									# If remote is enabled, we load from the Hub
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizerFast'  )
									self.assertTrue(tokenizer.special_attribute_present  )
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer'    ,   trust_remote_code=lowerCAmelCase_    ,   use_fast=lowerCAmelCase_  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )
									self.assertTrue(tokenizer.special_attribute_present  )

								finally:
									if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
										del TOKENIZER_MAPPING._extra_content[CustomConfig]


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained(
								    'hf-internal-testing/test_dynamic_tokenizer_legacy'    ,   trust_remote_code=lowerCAmelCase_  )
								self.assertTrue(tokenizer.special_attribute_present  )
								if is_tokenizers_available():
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizerFast'  )

									# Test we can also load the slow version
									_snake_case   						=		AutoTokenizer.from_pretrained(
									    'hf-internal-testing/test_dynamic_tokenizer_legacy'    ,   trust_remote_code=lowerCAmelCase_    ,   use_fast=lowerCAmelCase_  )
									self.assertTrue(tokenizer.special_attribute_present  )
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )
								else:
									self.assertEqual(tokenizer.__class__.__name__    ,   'NewTokenizer'  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with self.assertRaisesRegex(
								    lowerCAmelCase_    ,   'bert-base is not a local folder and is not a valid model identifier'  ):
									_snake_case   						=		AutoTokenizer.from_pretrained('bert-base'  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								with self.assertRaisesRegex(
								    lowerCAmelCase_    ,   R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'  ):
									_snake_case   						=		AutoTokenizer.from_pretrained(lowerCAmelCase_    ,   revision='aaaaaa'  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'  )
								with RequestCounter() as counter:
									_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'  )
									self.assertEqual(counter.get_request_count    ,   0  )
									self.assertEqual(counter.head_request_count    ,   1  )
									self.assertEqual(counter.other_request_count    ,   0  )



'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowercase    :							List[str]      =					logging.get_logger("transformers.models.speecht5")







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	hf_model.apply_weight_norm()

	_snake_case   						=		checkpoint['input_conv.weight_g']
	_snake_case   						=		checkpoint['input_conv.weight_v']
	_snake_case   						=		checkpoint['input_conv.bias']

	for i in range(len(config.upsample_rates	)	):
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_g']
		_snake_case   						=		checkpoint[F'upsamples.{i}.1.weight_v']
		_snake_case   						=		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	)	):
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
			_snake_case   						=		checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

	_snake_case   						=		checkpoint['output_conv.1.weight_g']
	_snake_case   						=		checkpoint['output_conv.1.weight_v']
	_snake_case   						=		checkpoint['output_conv.1.bias']

	hf_model.remove_weight_norm()







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A=None			,				__A=None			,				)       ->	List[Any]:
	if config_path is not None:
		_snake_case   						=		SpeechTaHifiGanConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		SpeechTaHifiGanConfig()

	_snake_case   						=		SpeechTaHifiGan(__A	)

	_snake_case   						=		torch.load(__A	)
	load_weights(orig_checkpoint['model']['generator']			,				__A			,				__A	)

	_snake_case   						=		np.load(__A	)
	_snake_case   						=		stats[0].reshape(-1	)
	_snake_case   						=		stats[1].reshape(-1	)
	_snake_case   						=		torch.from_numpy(__A	).float()
	_snake_case   						=		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__":
					lowercase    :							Dict      =					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."
					)

					lowercase    :							List[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,
					)

