






"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFXLMRobertaModel





@require_tf
@require_sentencepiece
@require_tokenizers
class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	@slow
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       TFXLMRobertaModel.from_pretrained('jplu/tf-xlm-roberta-base'						)

					A__							 =       {
					    'input_ids': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]  ,					dtype=tf.intaa						),  # "My dog is cute"
					    'attention_mask': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]  ,					dtype=tf.intaa						),
					}

					A__							 =       model(__a						)['last_hidden_state']
					A__							 =       tf.TensorShape((1, 6, 768)						)
					self.assertEqual(output.shape  ,					__a						)
					# compare the actual values for a slice.
					A__							 =       tf.convert_to_tensor(
					    [
					        [
					            [0.068_1762, 0.1089_4451, 0.0677_2504],
					            [-0.0642_3668, 0.0236_6615, 0.0432_9344],
					            [-0.0605_7295, 0.0997_4135, -0.0007_0584],
					        ]
					    ]  ,					dtype=tf.floataa  ,					)

					self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,					expected_slice.numpy()  ,					atol=1E-4						)						)










"""simple docstring"""






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

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

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


	def __init__(      self  ,					__a=None  ,					__a=None  ,					__a=None  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a="<|endoftext|>"  ,					__a=False  ,					**__a  ,					):


					"""simple docstring"""

					super().__init__(
					    __a  ,					__a  ,					tokenizer_file=__a  ,					unk_token=__a  ,					bos_token=__a  ,					eos_token=__a  ,					add_prefix_space=__a  ,					**__a  ,					)

					A__							 =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'  ,					__a						) != add_prefix_space:
									A__							 =       getattr(__a  ,					pre_tok_state.pop('type'						)						)
									A__							 =       add_prefix_space
									A__							 =       pre_tok_class(**__a						)

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__a  ,					add_special_tokens=__a						) + [self.eos_token_id]						)

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


