

import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Tuple	)						->				Union[str, Any]:




	'''simple docstring'''




	if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
		# expert layer
		_UpperCamelCase   :		str			=						flax_key_tuple[:-1] + ('weight',)
		_UpperCamelCase   :		str			=						torch.permute(snake_case__							,       (0, 2, 1)	)
	elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__	):
		# linear layer
		_UpperCamelCase   :		Any			=						flax_key_tuple[:-1] + ('weight',)
		_UpperCamelCase   :		Optional[Any]			=						flax_tensor.T
	elif flax_key_tuple[-1] in ["scale", "embedding"]:
		_UpperCamelCase   :		Any			=						flax_key_tuple[:-1] + ('weight',)

	return flax_key_tuple, flax_tensor






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Dict	)						->				List[Any]:




	'''simple docstring'''




	if "metadata" in layer:
		_UpperCamelCase   :		Optional[int]			=						layer.split('metadata'	)
		_UpperCamelCase   :		Optional[Any]			=						''.join(split_layer[0]	)[:-1]
		_UpperCamelCase   :		Optional[Any]			=						[tuple(('metadata' + split_layer[1]).split('/'	)	)]
	elif "kvstore" in layer:
		_UpperCamelCase   :		int			=						layer.split('kvstore'	)
		_UpperCamelCase   :		List[Any]			=						''.join(split_layer[0]	)[:-1]
		_UpperCamelCase   :		List[str]			=						[tuple(('kvstore' + split_layer[1]).split('/'	)	)]

	else:
		_UpperCamelCase   :		Tuple			=						layer.split('/'	)
		_UpperCamelCase   :		int			=						'/'.join(split_layer[:-1]	)
		_UpperCamelCase   :		Tuple			=						(split_layer[-1],)

	if "kvstore/path" in layer:
		_UpperCamelCase   :		Tuple			=						F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
	elif "kvstore/driver" in layer:
		_UpperCamelCase   :		Dict			=						'file'
	else:
		_UpperCamelCase   :		Tuple			=						checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     List[str]	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						rename_keys(snake_case__	)
	_UpperCamelCase   :		List[Any]			=						{}
	for k, v in current_block.items():
		_UpperCamelCase   :		int			=						v
	_UpperCamelCase   :		int			=						new_current_block
	torch.save(snake_case__							,       snake_case__	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     str = WEIGHTS_NAME	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						convert_file_size_to_int(snake_case__	)
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		Union[str, Any]			=						{}
	_UpperCamelCase   :		List[str]			=						0
	_UpperCamelCase   :		List[Any]			=						0

	os.makedirs(snake_case__							,       exist_ok=snake_case__	)
	with gfile.GFile(switch_checkpoint_path + '/checkpoint'							,       'rb'	) as fp:
		_UpperCamelCase   :		int			=						serialization.msgpack_restore(fp.read()	)['optimizer']['target']
		_UpperCamelCase   :		str			=						flatten_dict(snake_case__							,       sep='/'	)

	_UpperCamelCase   :		Optional[int]			=						{}
	for layer in checkpoint_info.keys():
		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						get_key_and_tensorstore_dict(
		    snake_case__							,       snake_case__							,       snake_case__	)
		if curr_real_layer_name in all_layers:
			_UpperCamelCase   :		Optional[Any]			=						content
		else:
			_UpperCamelCase   :		Any			=						{split_layer[-1]: content}

	for key in all_layers.keys():
		# open tensorstore file
		_UpperCamelCase   :		Dict			=						ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
		_UpperCamelCase   :		Tuple			=						torch.tensor(snake_case__	)
		_UpperCamelCase   :		List[str]			=						raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

		# use the renaming pattern from the small conversion scripts
		_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						rename_base_flax_keys(tuple(key.split('/'	)	)							,       snake_case__	)
		_UpperCamelCase   :		Tuple			=						'/'.join(snake_case__	)

		# If this weight is going to tip up over the maximal size, we split.
		if current_block_size + weight_size > max_shard_size:
			_UpperCamelCase   :		List[Any]			=						os.path.join(
			    snake_case__							,       weights_name.replace('.bin'							,       F'''-{len(snake_case__	)+1:05d}-of-???.bin'''	)	)
			rename_and_save_block(snake_case__							,       snake_case__	)
			sharded_state_dicts.append(current_block.keys()	)
			del current_block
			_UpperCamelCase   :		Optional[int]			=						{}
			_UpperCamelCase   :		Optional[Any]			=						0

		_UpperCamelCase   :		Dict			=						raw_weights.to(getattr(snake_case__							,       snake_case__	)	)
		current_block_size += weight_size
		total_size += weight_size

	# Add the last block
	_UpperCamelCase   :		List[Any]			=						os.path.join(snake_case__							,       weights_name.replace('.bin'							,       F'''-{len(snake_case__	)+1:05d}-of-???.bin'''	)	)
	rename_and_save_block(snake_case__							,       snake_case__	)
	sharded_state_dicts.append(current_block.keys()	)

	# If we only have one shard, we return it
	if len(snake_case__	) == 1:
		return {weights_name: sharded_state_dicts[0]}, None

	# Otherwise, let's build the index
	_UpperCamelCase   :		Optional[Any]			=						{}
	_UpperCamelCase   :		Tuple			=						{}
	for idx, shard in enumerate(snake_case__	):
		_UpperCamelCase   :		int			=						weights_name.replace(
		    '.bin'							,       F'''-{idx+1:05d}-of-{len(snake_case__	):05d}.bin'''	)  # len(sharded_state_dicts):05d}
		_UpperCamelCase   :		Union[str, Any]			=						os.path.join(snake_case__							,       weights_name.replace('.bin'							,       F'''-{idx+1:05d}-of-???.bin'''	)	)
		os.rename(snake_case__							,       os.path.join(snake_case__							,       snake_case__	)	)
		_UpperCamelCase   :		int			=						shard
		for key in shard:
			_UpperCamelCase   :		List[str]			=						shard_file

    # Add the metadata
	_UpperCamelCase   :		int			=						{'total_size': total_size}
	_UpperCamelCase   :		Dict			=						{'metadata': metadata, 'weight_map': weight_map}

	with open(os.path.join(snake_case__							,       snake_case__	)							,       'w'							,       encoding='utf-8'	) as f:
		_UpperCamelCase   :		str			=						json.dumps(snake_case__							,       indent=2							,       sort_keys=snake_case__	) + '\n'
		f.write(snake_case__	)

	return metadata, index


if __name__ == "__main__":
							lowerCAmelCase__  			=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--switch_t5x_checkpoint_path""",
							    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
							    type=str,
							    required=False,
							    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
							)
							parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
							parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
							    type=str,
							    required=False,
							    help="""Path to the output pytorch model.""",
							)
							lowerCAmelCase__  			=		parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)







def 					lowerCamelCase_     (							)						->				Dict:




	'''simple docstring'''




	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

	_UpperCamelCase   :		Optional[Any]			=						SwitchTransformersConfig.from_pretrained('google/switch-base-8'	)
	config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'	)
	_UpperCamelCase   :		List[str]			=						SwitchTransformersForConditionalGeneration.from_pretrained(
	    '/home/arthur_huggingface_co/transformers/switch_converted'							,       device_map='auto'	)

	_UpperCamelCase   :		List[str]			=						TaTokenizer.from_pretrained('t5-small'	)
	_UpperCamelCase   :		Any			=						'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

	_UpperCamelCase   :		Optional[Any]			=						tokenizer(snake_case__							,       return_tensors='pt'	).input_ids
	_UpperCamelCase   :		Dict			=						model.generate(snake_case__							,       decoder_start_token_id=0	)
	print(tokenizer.decode(out[0]	)	)







import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__								=		get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_sentencepiece_available():
							import sentencepiece as sp


lowerCAmelCase__								=		5
lowerCAmelCase__								=		1_0







@require_sentencepiece
@require_tokenizers
class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  SpeechaTextTokenizer
		a__					       =  False
		a__					       =  True


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

			_UpperCamelCase   :		Any			=						sp.SentencePieceProcessor()
			spm_model.Load(__snake_case)
			_UpperCamelCase   :		List[str]			=						['<s>', '<pad>', '</s>', '<unk>']

			vocab += [spm_model.IdToPiece(id_) for id_ in range(len(__snake_case))]
			_UpperCamelCase   :		Dict			=						dict(zip(__snake_case	,       range(len(__snake_case))))

			_UpperCamelCase   :		Tuple			=						Path(self.tmpdirname)
			save_json(__snake_case	,       save_dir / VOCAB_FILES_NAMES['vocab_file'])
			if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
				copyfile(__snake_case	,       save_dir / VOCAB_FILES_NAMES['spm_file'])

			_UpperCamelCase   :		int			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
			tokenizer.save_pretrained(self.tmpdirname)


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						'<pad>'
			_UpperCamelCase   :		Dict			=						1

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case)	,       __snake_case)
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case)	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						list(self.get_tokenizer().get_vocab().keys())

			self.assertEqual(vocab_keys[0]	,       '<s>')
			self.assertEqual(vocab_keys[1]	,       '<pad>')
			self.assertEqual(vocab_keys[-1]	,       'j')
			self.assertEqual(len(__snake_case)	,       10_01)


		def 		A__					(				self):
			self.assertEqual(self.get_tokenizer().vocab_size	,       10_01)


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname)

			_UpperCamelCase   :		List[str]			=						tokenizer.tokenize('This is a test')
			self.assertListEqual(__snake_case	,       ['▁This', '▁is', '▁a', '▁t', 'est'])

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(__snake_case)	,       [2_89, 50, 14, 1_74, 3_86]	,       )

			_UpperCamelCase   :		int			=						tokenizer.tokenize('I was born in 92000, and this is falsé.')
			self.assertListEqual(
			    __snake_case	,       [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']	,       )
			_UpperCamelCase   :		int			=						tokenizer.convert_tokens_to_ids(__snake_case)
			self.assertListEqual(__snake_case	,       [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8])

			_UpperCamelCase   :		Tuple			=						tokenizer.convert_ids_to_tokens(__snake_case)
			self.assertListEqual(
			    __snake_case	,       [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']	,       )






		@slow
		def 		A__					(				self):
			# fmt: off
			_UpperCamelCase   :		Optional[int]			=						{'input_ids': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=__snake_case	,       model_name='facebook/s2t-small-mustc-en-de-st'	,       revision='a14f04cf0776c02f62a8cb800cf7909e15ea23ad'	,       )







@require_sentencepiece
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		a__					       =  "valhalla/s2t_mustc_multilinguial_medium"

		a__					       =  "C'est trop cool"
		a__					       =  "Esto es genial"


		@classmethod
		def 		A__					(				cls):
			_UpperCamelCase   :		SpeechaTextTokenizer			=						SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name)
			return cls


		def 		A__					(				self):
			self.assertEqual(self.tokenizer.lang_code_to_id['pt']	,       4)
			self.assertEqual(self.tokenizer.lang_code_to_id['ru']	,       6)
			self.assertEqual(self.tokenizer.lang_code_to_id['it']	,       9)
			self.assertEqual(self.tokenizer.lang_code_to_id['de']	,       11)


		def 		A__					(				self):
			self.assertEqual(self.tokenizer.vocab_size	,       1_00_00)


		def 		A__					(				self):
			self.assertIn(__snake_case	,       self.tokenizer.all_special_ids)
			_UpperCamelCase   :		Optional[int]			=						[ES_CODE, 4, 16_01, 47, 76_47, 2]
			_UpperCamelCase   :		Tuple			=						self.tokenizer.decode(__snake_case	,       skip_special_tokens=__snake_case)
			_UpperCamelCase   :		Dict			=						self.tokenizer.decode(generated_ids[1:]	,       skip_special_tokens=__snake_case)
			self.assertEqual(__snake_case	,       __snake_case)
			self.assertNotIn(self.tokenizer.eos_token	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						'fr'
			_UpperCamelCase   :		List[Any]			=						self.tokenizer(self.french_text).input_ids
			self.assertEqual(encoded[0]	,       __snake_case)
			self.assertEqual(encoded[-1]	,       self.tokenizer.eos_token_id)






		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						'fr'
			self.assertListEqual(self.tokenizer.prefix_tokens	,       [FR_CODE])

			_UpperCamelCase   :		List[str]			=						'es'
			self.assertListEqual(self.tokenizer.prefix_tokens	,       [ES_CODE])




