






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 TFCamembertModel

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




							@slow
							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Union[str, Any] 						= TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

														lowerCamelCase__      :			Optional[Any] 						= tf.convert_to_tensor(
														    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]    ,							dtype=tf.intaa    ,							)  # J'aime le camembert !"

														lowerCamelCase__      :			Any 						= model(UpperCamelCase__  )["""last_hidden_state"""]
														lowerCamelCase__      :			Optional[int] 						= tf.TensorShape((1, 10, 768)  )
														self.assertEqual(output.shape    ,							UpperCamelCase__  )
														# compare the actual values for a slice.
														lowerCamelCase__      :			str 						= tf.convert_to_tensor(
														    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,							dtype=tf.floataa    ,							)
														# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
														# camembert.eval()
														# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

														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


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Dict					  ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A			:    Tuple					  ={
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_A			:    List[Any]					  ={
    '''gpt-neox-20b''': 2_048,
}

class _lowercase			(       _lowercase	):
							a          = VOCAB_FILES_NAMES
							a          = PRETRAINED_VOCAB_FILES_MAP
							a          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a          = ["""input_ids""", """attention_mask"""]




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]=None    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       Tuple=None    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Any="<|endoftext|>"    ,							UpperCamelCase__:       Union[str, Any]="<|endoftext|>"    ,							UpperCamelCase__:       Tuple=False    ,							**UpperCamelCase__:       str    ,							):
														super().__init__(
														    UpperCamelCase__    ,							UpperCamelCase__    ,							tokenizer_file=UpperCamelCase__    ,							unk_token=UpperCamelCase__    ,							bos_token=UpperCamelCase__    ,							eos_token=UpperCamelCase__    ,							add_prefix_space=UpperCamelCase__    ,							**UpperCamelCase__    ,							)

														lowerCamelCase__      :			Dict 						= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
														if pre_tok_state.get("""add_prefix_space"""    ,							UpperCamelCase__  ) != add_prefix_space:
																					lowerCamelCase__      :			Any 						= getattr(UpperCamelCase__    ,							pre_tok_state.pop("""type"""  )  )
																					lowerCamelCase__      :			Dict 						= add_prefix_space
																					lowerCamelCase__      :			Optional[int] 						= pre_tok_class(**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= add_prefix_space




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Optional[str] = None  ):
														lowerCamelCase__      :			Optional[Any] 						= self._tokenizer.model.save(UpperCamelCase__    ,							name=UpperCamelCase__  )
														return tuple(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       "Conversation"  ):
														lowerCamelCase__      :			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:
																					lowerCamelCase__      :			int 						= input_ids[-self.model_max_length :]
														return input_ids

