




'''simple docstring'''





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE							:       Any						=			get_tests_dir("fixtures/test_sentencepiece.model")
_SCREAMING_SNAKE_CASE							:       Tuple						=			get_tests_dir("fixtures/test_sentencepiece_bpe.model")

_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			"pt" if is_torch_available() else "tf"




@require_sentencepiece
@require_tokenizers
class 			_snake_case		(     lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_					:				List[Any]							=   CamembertTokenizer
			lowerCAmelCase_					:				int							=   CamembertTokenizerFast
			lowerCAmelCase_					:				Dict							=   True
			lowerCAmelCase_					:				Optional[Any]							=   True




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case_      =				CamembertTokenizer(a__							)
										tokenizer.save_pretrained(self.tmpdirname							)




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				"<pad>"
										snake_case_      =				1

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




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				list(self.get_tokenizer().get_vocab().keys()							)

										self.assertEqual(vocab_keys[0]    ,							"<s>NOTUSED"							)
										self.assertEqual(vocab_keys[1]    ,							"<pad>"							)
										self.assertEqual(vocab_keys[-1]    ,							"<mask>"							)
										self.assertEqual(len(a__							)    ,							1_004							)




			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										self.assertEqual(self.get_tokenizer().vocab_size    ,							1_005							)




			def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				CamembertTokenizer(a__							)
										tokenizer.save_pretrained(self.tmpdirname							)
										snake_case_      =				CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

										snake_case_      =				"I was born in 92000, and this is falsé."

										snake_case_      =				tokenizer.encode(a__							)
										snake_case_      =				rust_tokenizer.encode(a__							)
										self.assertListEqual(a__    ,							a__							)

										snake_case_      =				tokenizer.encode(a__    ,							add_special_tokens=a__							)
										snake_case_      =				rust_tokenizer.encode(a__    ,							add_special_tokens=a__							)
										self.assertListEqual(a__    ,							a__							)

										# <unk> tokens are not the same for `rust` than for `slow`.
										# Because spm gives back raw token instead of `unk` in EncodeAsPieces
										# tokens = tokenizer.tokenize(sequence)
										snake_case_      =				tokenizer.convert_ids_to_tokens(a__							)
										snake_case_      =				rust_tokenizer.tokenize(a__							)
										self.assertListEqual(a__    ,							a__							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										if not self.test_rust_tokenizer:
																	return

										snake_case_      =				self.get_tokenizer()
										snake_case_      =				self.get_rust_tokenizer()

										snake_case_      =				"I was born in 92000, and this is falsé."

										snake_case_      =				tokenizer.tokenize(a__							)
										snake_case_      =				rust_tokenizer.tokenize(a__							)
										self.assertListEqual(a__    ,							a__							)

										snake_case_      =				tokenizer.encode(a__    ,							add_special_tokens=a__							)
										snake_case_      =				rust_tokenizer.encode(a__    ,							add_special_tokens=a__							)
										self.assertListEqual(a__    ,							a__							)

										snake_case_      =				self.get_rust_tokenizer()
										snake_case_      =				tokenizer.encode(a__							)
										snake_case_      =				rust_tokenizer.encode(a__							)
										self.assertListEqual(a__    ,							a__							)




			@slow
			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				{"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], "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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
										# fmt: on

										# camembert is a french model. So we also use french texts.
										snake_case_      =				[
										    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
										    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
										    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
										    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
										    "telles que la traduction et la synthèse de texte.",
										]

										self.tokenizer_integration_test_util(
										    expected_encoding=a__    ,							model_name="camembert-base"    ,							revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"    ,							sequences=a__    ,							)










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Dict							=   "decision_transformer"
			lowerCAmelCase_					:				List[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__( self    ,							a__=17    ,							a__=4    ,							a__=128    ,							a__=4_096    ,							a__=True    ,							a__=1    ,							a__=1_024    ,							a__=3    ,							a__=1    ,							a__=None    ,							a__="relu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__=True    ,							a__=True    ,							a__=50_256    ,							a__=50_256    ,							a__=False    ,							a__=False    ,							**a__    ,							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				state_dim
										snake_case_      =				act_dim
										snake_case_      =				hidden_size
										snake_case_      =				max_ep_len
										snake_case_      =				action_tanh
										snake_case_      =				vocab_size
										snake_case_      =				n_positions
										snake_case_      =				n_layer
										snake_case_      =				n_head
										snake_case_      =				n_inner
										snake_case_      =				activation_function
										snake_case_      =				resid_pdrop
										snake_case_      =				embd_pdrop
										snake_case_      =				attn_pdrop
										snake_case_      =				layer_norm_epsilon
										snake_case_      =				initializer_range
										snake_case_      =				scale_attn_weights
										snake_case_      =				use_cache
										snake_case_      =				scale_attn_by_inverse_layer_idx
										snake_case_      =				reorder_and_upcast_attn

										snake_case_      =				bos_token_id
										snake_case_      =				eos_token_id

										super().__init__(bos_token_id=a__    ,							eos_token_id=a__    ,							**a__							)




