



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_						=       get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class 			_snake_case     (   lowercase__      ,		unittest.TestCase):
    UpperCamelCase__			:   int       						=XLMRobertaTokenizer
    UpperCamelCase__			:   List[Any]       						=XLMRobertaTokenizerFast
    UpperCamelCase__			:   List[str]       						=True
    UpperCamelCase__			:   Optional[int]       						=True




    def 							A__ (  self   :  List[Any]  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       lowercase__   					=			XLMRobertaTokenizer(__lowercase,   keep_accents=__lowercase  )
       tokenizer.save_pretrained(self.tmpdirname  )




    def 							A__ (  self   :  Optional[Any]  ):
       lowercase__   					=			"<pad>"
       lowercase__   					=			1

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




    def 							A__ (  self   :  Optional[Any]  ):
       lowercase__   					=			list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0],   "<s>"  )
       self.assertEqual(vocab_keys[1],   "<pad>"  )
       self.assertEqual(vocab_keys[-1],   "<mask>"  )
       self.assertEqual(len(__lowercase  ),   1002  )




    def 							A__ (  self   :  str  ):
       self.assertEqual(self.get_tokenizer().vocab_size,   1002  )




    def 							A__ (  self   :  str  ):
       lowercase__   					=			XLMRobertaTokenizer(__lowercase,   keep_accents=__lowercase  )

       lowercase__   					=			tokenizer.tokenize("This is a test"  )
       self.assertListEqual(__lowercase,   ["▁This", "▁is", "▁a", "▁t", "est"]  )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(__lowercase  ),   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],   )

       lowercase__   					=			tokenizer.tokenize("I was born in 92000, and this is falsé."  )
       self.assertListEqual(
           __lowercase,   [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ],   )
       lowercase__   					=			tokenizer.convert_tokens_to_ids(__lowercase  )
       self.assertListEqual(
           __lowercase,   [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
           ],   )

       lowercase__   					=			tokenizer.convert_ids_to_tokens(__lowercase  )
       self.assertListEqual(
           __lowercase,   [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ],   )




    def 							A__ (  self   :  str  ):
       if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return

       lowercase__   					=			(self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
             lowercase__   					=			self.rust_tokenizer_class.from_pretrained(__lowercase,   **__lowercase  )
             lowercase__   					=			self.tokenizer_class.from_pretrained(__lowercase,   **__lowercase  )

             lowercase__   					=			tempfile.mkdtemp()

             lowercase__   					=			tokenizer_r.save_pretrained(__lowercase  )
             lowercase__   					=			tokenizer_p.save_pretrained(__lowercase  )

             # Checks it save with the same files + the tokenizer.json file for the fast one
             self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files  )  )
             lowercase__   					=			tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f  )
             self.assertSequenceEqual(__lowercase,   __lowercase  )

             # Checks everything loads correctly in the same way
             lowercase__   					=			tokenizer_r.from_pretrained(__lowercase  )
             lowercase__   					=			tokenizer_p.from_pretrained(__lowercase  )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                self.assertTrue(hasattr(__lowercase,   __lowercase  )  )
                # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

             shutil.rmtree(__lowercase  )

             # Save tokenizer rust, legacy_format=True
             lowercase__   					=			tempfile.mkdtemp()

             lowercase__   					=			tokenizer_r.save_pretrained(__lowercase,   legacy_format=__lowercase  )
             lowercase__   					=			tokenizer_p.save_pretrained(__lowercase  )

             # Checks it save with the same files
             self.assertSequenceEqual(__lowercase,   __lowercase  )

             # Checks everything loads correctly in the same way
             lowercase__   					=			tokenizer_r.from_pretrained(__lowercase  )
             lowercase__   					=			tokenizer_p.from_pretrained(__lowercase  )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                self.assertTrue(hasattr(__lowercase,   __lowercase  )  )

             shutil.rmtree(__lowercase  )

             # Save tokenizer rust, legacy_format=False
             lowercase__   					=			tempfile.mkdtemp()

             lowercase__   					=			tokenizer_r.save_pretrained(__lowercase,   legacy_format=__lowercase  )
             lowercase__   					=			tokenizer_p.save_pretrained(__lowercase  )

             # Checks it saved the tokenizer.json file
             self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files  )  )

             # Checks everything loads correctly in the same way
             lowercase__   					=			tokenizer_r.from_pretrained(__lowercase  )
             lowercase__   					=			tokenizer_p.from_pretrained(__lowercase  )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                self.assertTrue(hasattr(__lowercase,   __lowercase  )  )

             shutil.rmtree(__lowercase  )




    @cached_property
    def 							A__ (  self   :  List[Any]  ):
       return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"  )




    def 							A__ (  self   :  Dict  ):
       with tempfile.NamedTemporaryFile() as f:
          shutil.copyfile(__lowercase,   f.name  )
          lowercase__   					=			XLMRobertaTokenizer(f.name,   keep_accents=__lowercase  )
          lowercase__   					=			pickle.dumps(__lowercase  )
       pickle.loads(__lowercase  )




    def 							A__ (  self   :  Tuple  ):
       if not self.test_rust_tokenizer:
          return

       lowercase__   					=			self.get_tokenizer()
       lowercase__   					=			self.get_rust_tokenizer()

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

       lowercase__   					=			tokenizer.tokenize(__lowercase  )
       lowercase__   					=			rust_tokenizer.tokenize(__lowercase  )
       self.assertListEqual(__lowercase,   __lowercase  )

       lowercase__   					=			tokenizer.encode(__lowercase,   add_special_tokens=__lowercase  )
       lowercase__   					=			rust_tokenizer.encode(__lowercase,   add_special_tokens=__lowercase  )
       self.assertListEqual(__lowercase,   __lowercase  )

       lowercase__   					=			self.get_rust_tokenizer()
       lowercase__   					=			tokenizer.encode(__lowercase  )
       lowercase__   					=			rust_tokenizer.encode(__lowercase  )
       self.assertListEqual(__lowercase,   __lowercase  )




    @slow
    def 							A__ (  self   :  str  ):
       lowercase__   					=			"Hello World!"
       lowercase__   					=			[0, 3_5378, 6661, 38, 2]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(__lowercase,   self.big_tokenizer.encode(__lowercase  )  )




    @slow
    def 							A__ (  self   :  List[str]  ):
       lowercase__   					=			(
           "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
           " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
       )
       lowercase__   					=			[
           0,
           3293,
           83,
           10,
           4552,
           4989,
           7986,
           678,
           10,
           5915,
           111,
           17_9459,
           12_4850,
           4,
           6044,
           237,
           12,
           6,
           5,
           6,
           4,
           6780,
           705,
           15,
           1388,
           44,
           378,
           1_0114,
           711,
           152,
           20,
           6,
           5,
           2_2376,
           642,
           1221,
           1_5190,
           3_4153,
           450,
           5608,
           959,
           1119,
           5_7702,
           136,
           186,
           47,
           1098,
           2_9367,
           47,
           # 4426, # What fairseq tokenizes from "<unk>": "_<"
           # 3678, # What fairseq tokenizes from "<unk>": "unk"
           # 2740, # What fairseq tokenizes from "<unk>": ">"
           3,  # What we tokenize from "<unk>": "<unk>"
           6,  # Residue from the tokenization: an extra sentencepiece underline
           4,
           6044,
           237,
           6284,
           5_0901,
           528,
           31,
           90,
           34,
           927,
           2,
       ]
       # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
       # xlmr.eval()
       # xlmr.encode(symbols)

       self.assertListEqual(__lowercase,   self.big_tokenizer.encode(__lowercase  )  )




    @slow
    def 							A__ (  self   :  Dict  ):
       # fmt: off
       lowercase__   					=			{"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 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, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 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]], "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, 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], [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, 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=__lowercase,   model_name="xlm-roberta-base",   revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3",   )









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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Tuple       						="""nllb-moe"""
				UpperCamelCase__			:   Dict       						=["""past_key_values"""]
				UpperCamelCase__			:   List[Any]       						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self   :  List[str],   __lowercase   :  str=12_8112,   __lowercase   :  List[Any]=1024,   __lowercase   :  int=12,   __lowercase   :  Union[str, Any]=4096,   __lowercase   :  Union[str, Any]=16,   __lowercase   :  Tuple=12,   __lowercase   :  int=4096,   __lowercase   :  Any=16,   __lowercase   :  List[str]=0.05,   __lowercase   :  Optional[int]=0.05,   __lowercase   :  int=True,   __lowercase   :  Optional[Any]=True,   __lowercase   :  Optional[Any]="relu",   __lowercase   :  str=1024,   __lowercase   :  Optional[Any]=0.1,   __lowercase   :  List[Any]=0.1,   __lowercase   :  Union[str, Any]=0.0,   __lowercase   :  Tuple=0.02,   __lowercase   :  Optional[Any]=2,   __lowercase   :  Optional[int]=True,   __lowercase   :  List[Any]=False,   __lowercase   :  Any="float32",   __lowercase   :  Union[str, Any]=False,   __lowercase   :  Optional[int]=128,   __lowercase   :  Union[str, Any]=64,   __lowercase   :  Optional[int]=4,   __lowercase   :  Dict=4,   __lowercase   :  Any=0.001,   __lowercase   :  Optional[int]=0.001,   __lowercase   :  int="all",   __lowercase   :  Any=False,   __lowercase   :  Optional[int]=False,   __lowercase   :  Dict=1.0,   __lowercase   :  int=0.2,   __lowercase   :  str=1,   __lowercase   :  Optional[Any]=0,   __lowercase   :  Optional[int]=2,   __lowercase   :  Any=False,   **__lowercase   :  List[str],   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			max_position_embeddings
							lowercase__   					=			d_model
							lowercase__   					=			encoder_ffn_dim
							lowercase__   					=			encoder_layers
							lowercase__   					=			encoder_attention_heads
							lowercase__   					=			decoder_ffn_dim
							lowercase__   					=			decoder_layers
							lowercase__   					=			decoder_attention_heads
							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			activation_function
							lowercase__   					=			init_std
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop
							lowercase__   					=			use_cache
							lowercase__   					=			encoder_layers
							lowercase__   					=			scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__   					=			router_z_loss_coef
							lowercase__   					=			router_aux_loss_coef
							lowercase__   					=			decoder_sparse_step
							lowercase__   					=			encoder_sparse_step
							lowercase__   					=			num_experts
							lowercase__   					=			expert_capacity
							lowercase__   					=			router_bias
							if router_dtype not in ["float32", "float16", "bfloat16"]:
										raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''  )
							lowercase__   					=			router_dtype

							lowercase__   					=			router_ignore_padding_tokens
							lowercase__   					=			batch_prioritized_routing
							lowercase__   					=			second_expert_policy
							lowercase__   					=			normalize_router_prob_before_dropping
							lowercase__   					=			moe_eval_capacity_token_fraction
							lowercase__   					=			moe_token_dropout
							lowercase__   					=			output_router_logits
							super().__init__(
							    pad_token_id=__lowercase,   bos_token_id=__lowercase,   eos_token_id=__lowercase,   is_encoder_decoder=__lowercase,   decoder_start_token_id=__lowercase,   **__lowercase,   )




