
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE    :      Dict          =			get_tests_dir('fixtures/test_sentencepiece.model')

_SCREAMING_SNAKE_CASE    :      int          =			{'target_lang': 'fi', 'source_lang': 'en'}
_SCREAMING_SNAKE_CASE    :      List[Any]          =			'>>zh<<'
_SCREAMING_SNAKE_CASE    :      List[str]          =			'Helsinki-NLP/'

if is_torch_available():
			_SCREAMING_SNAKE_CASE    :      Dict          =			'pt'
elif is_tf_available():
			_SCREAMING_SNAKE_CASE    :      int          =			'tf'
else:
			_SCREAMING_SNAKE_CASE    :      Optional[int]          =			'jax'







@require_sentencepiece
class  UpperCamelCase__		(    __lowerCAmelCase,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				MarianTokenizer
	_lowerCAmelCase							       =				False
	_lowerCAmelCase							       =				True





	def   __snake_case				(			self		):
					super().setUp()
					A__  :	Optional[Any]         =							['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
					A__  :	Any         =							dict(zip(lowerCAmelCase_				,       range(len(lowerCAmelCase_		)		)		)		)
					A__  :	Optional[Any]         =							Path(self.tmpdirname		)
					save_json(lowerCAmelCase_				,       save_dir / VOCAB_FILES_NAMES['''vocab''']		)
					save_json(lowerCAmelCase_				,       save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']		)
					if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
									copyfile(lowerCAmelCase_				,       save_dir / VOCAB_FILES_NAMES['''source_spm''']		)
									copyfile(lowerCAmelCase_				,       save_dir / VOCAB_FILES_NAMES['''target_spm''']		)

					A__  :	str         =							MarianTokenizer.from_pretrained(self.tmpdirname		)
					tokenizer.save_pretrained(self.tmpdirname		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return MarianTokenizer.from_pretrained(self.tmpdirname				,       **lowerCAmelCase_		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return (
					    "This is a test",
					    "This is a test",
					)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							'''</s>'''
					A__  :	Optional[Any]         =							0

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





	def   __snake_case				(			self		):
					A__  :	Dict         =							list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]				,       '''</s>'''		)
					self.assertEqual(vocab_keys[1]				,       '''<unk>'''		)
					self.assertEqual(vocab_keys[-1]				,       '''<pad>'''		)
					self.assertEqual(len(lowerCAmelCase_		)				,       9		)





	def   __snake_case				(			self		):
					self.assertEqual(self.get_tokenizer().vocab_size				,       9		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de"		)
					A__  :	Optional[Any]         =							en_de_tokenizer(['''I am a small frog''']				,       return_tensors=lowerCAmelCase_		)
					self.assertIsInstance(lowerCAmelCase_				,       lowerCAmelCase_		)
					A__  :	Any         =							[38, 121, 14, 697, 3_8848, 0]
					self.assertListEqual(lowerCAmelCase_				,       batch.input_ids[0]		)

					A__  :	List[Any]         =							tempfile.mkdtemp()
					en_de_tokenizer.save_pretrained(lowerCAmelCase_		)
					A__  :	Dict         =							[x.name for x in Path(lowerCAmelCase_		).glob('''*'''		)]
					self.assertIn('''source.spm'''				,       lowerCAmelCase_		)
					MarianTokenizer.from_pretrained(lowerCAmelCase_		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.get_tokenizer()

					A__  :	Union[str, Any]         =							tok(
					    ['''I am a small frog''' * 1000, '''I am a small frog''']				,       padding=lowerCAmelCase_				,       truncation=lowerCAmelCase_				,       return_tensors=lowerCAmelCase_		)
					self.assertIsInstance(lowerCAmelCase_				,       lowerCAmelCase_		)
					self.assertEqual(batch.input_ids.shape				,       (2, 512)		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.get_tokenizer()
					A__  :	str         =							tok(['''I am a tiny frog''', '''I am a small frog''']				,       padding=lowerCAmelCase_				,       return_tensors=lowerCAmelCase_		)
					self.assertIsInstance(lowerCAmelCase_				,       lowerCAmelCase_		)
					self.assertEqual(batch_smaller.input_ids.shape				,       (2, 10)		)





	@slow
	def   __snake_case				(			self		):
					# fmt: off
					A__  :	Optional[int]         =							{'''input_ids''': [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], '''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, 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, 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=lowerCAmelCase_				,       model_name='''Helsinki-NLP/opus-mt-en-de'''				,       revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''				,       decode_kwargs={'''use_source_tokenizer''': True}				,       )





	def   __snake_case				(			self		):
					A__  :	str         =							MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''		)

					A__  :	Tuple         =							'''Tämä on testi'''
					A__  :	Any         =							'''This is a test'''

					A__  :	List[Any]         =							[76, 7, 2047, 2]
					A__  :	List[Any]         =							[69, 12, 11, 940, 2]

					A__  :	Optional[int]         =							tokenizer(lowerCAmelCase_		).input_ids
					self.assertListEqual(lowerCAmelCase_				,       lowerCAmelCase_		)

					A__  :	Dict         =							tokenizer(text_target=lowerCAmelCase_		).input_ids
					self.assertListEqual(lowerCAmelCase_				,       lowerCAmelCase_		)

					A__  :	Tuple         =							tokenizer.decode(lowerCAmelCase_				,       skip_special_tokens=lowerCAmelCase_		)
					self.assertEqual(lowerCAmelCase_				,       lowerCAmelCase_		)

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids