



"""simple docstring"""







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


A    =			get_tests_dir('fixtures/test_sentencepiece.model')

A    =			{'target_lang': 'fi', 'source_lang': 'en'}
A    =			'>>zh<<'
A    =			'Helsinki-NLP/'

if is_torch_available():
							A    =			'pt'
elif is_tf_available():
							A    =			'tf'
else:
							A    =			'jax'






@require_sentencepiece
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     MarianTokenizer
		lowerCAmelCase_       :	Dict 							=     False
		lowerCAmelCase_       :	str 							=     True



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






									'''simple docstring'''

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

									A         =					MarianTokenizer.from_pretrained(self.tmpdirname				)
									tokenizer.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    Optional[Any]      ,							**snake_case  :    Union[str, Any]				)			->						MarianTokenizer:






									'''simple docstring'''

									return MarianTokenizer.from_pretrained(self.tmpdirname      ,							**snake_case				)



		def 					A_		( self  :    int      ,							snake_case  :    str				)			->						int:






									'''simple docstring'''

									return (
									    "This is a test",
									    "This is a test",
									)



		def 					A_		( self  :    List[str]				)			->						Dict:






									'''simple docstring'''

									A         =					'</s>'
									A         =					0

									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  :    Any				)			->						int:






									'''simple docstring'''

									A         =					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(snake_case				)      ,							9				)



		def 					A_		( self  :    Optional[int]				)			->						int:






									'''simple docstring'''

									self.assertEqual(self.get_tokenizer().vocab_size      ,							9				)



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






									'''simple docstring'''

									A         =					MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""				)
									A         =					en_de_tokenizer(['I am a small frog']      ,							return_tensors=snake_case				)
									self.assertIsInstance(snake_case      ,							snake_case				)
									A         =					[38, 121, 14, 697, 38_848, 0]
									self.assertListEqual(snake_case      ,							batch.input_ids[0]				)

									A         =					tempfile.mkdtemp()
									en_de_tokenizer.save_pretrained(snake_case				)
									A         =					[x.name for x in Path(snake_case				).glob('*'				)]
									self.assertIn('source.spm'      ,							snake_case				)
									MarianTokenizer.from_pretrained(snake_case				)



		def 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									A         =					self.get_tokenizer()

									A         =					tok(
									    ['I am a small frog' * 1_000, 'I am a small frog']      ,							padding=snake_case      ,							truncation=snake_case      ,							return_tensors=snake_case				)
									self.assertIsInstance(snake_case      ,							snake_case				)
									self.assertEqual(batch.input_ids.shape      ,							(2, 512)				)



		def 					A_		( self  :    Any				)			->						Dict:






									'''simple docstring'''

									A         =					self.get_tokenizer()
									A         =					tok(['I am a tiny frog', 'I am a small frog']      ,							padding=snake_case      ,							return_tensors=snake_case				)
									self.assertIsInstance(snake_case      ,							snake_case				)
									self.assertEqual(batch_smaller.input_ids.shape      ,							(2, 10)				)



		@slow
		def 					A_		( self  :    Optional[int]				)			->						Any:






									'''simple docstring'''

									A         =					{'input_ids': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '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=snake_case      ,							model_name='Helsinki-NLP/opus-mt-en-de'      ,							revision='1a8c2263da11e68e50938f97e10cd57820bd504c'      ,							decode_kwargs={'use_source_tokenizer': True}      ,							)



		def 					A_		( self  :    List[str]				)			->						int:






									'''simple docstring'''

									A         =					MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'				)

									A         =					'Tämä on testi'
									A         =					'This is a test'

									A         =					[76, 7, 2_047, 2]
									A         =					[69, 12, 11, 940, 2]

									A         =					tokenizer(snake_case				).input_ids
									self.assertListEqual(snake_case      ,							snake_case				)

									A         =					tokenizer(text_target=snake_case				).input_ids
									self.assertListEqual(snake_case      ,							snake_case				)

									A         =					tokenizer.decode(snake_case      ,							skip_special_tokens=snake_case				)
									self.assertEqual(snake_case      ,							snake_case				)








"""simple docstring"""







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


A    =			logging.get_logger(__name__)

A    =			{
    '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      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     """decision_transformer"""
		lowerCAmelCase_       :	Tuple 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Dict 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    int=17      ,							snake_case  :    int=4      ,							snake_case  :    Union[str, Any]=128      ,							snake_case  :    Optional[Any]=4_096      ,							snake_case  :    List[Any]=True      ,							snake_case  :    str=1      ,							snake_case  :    str=1_024      ,							snake_case  :    List[str]=3      ,							snake_case  :    str=1      ,							snake_case  :    Any=None      ,							snake_case  :    Optional[int]="relu"      ,							snake_case  :    List[Any]=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=1E-5      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    List[str]=False      ,							snake_case  :    Any=False      ,							**snake_case  :    List[str]      ,							)			->						List[Any]:






									'''simple docstring'''

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

									A         =					bos_token_id
									A         =					eos_token_id

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



