



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


_snake_case		=  get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		XLMRobertaTokenizer
							lowerCamelCase__   			=		XLMRobertaTokenizerFast
							lowerCamelCase__   			=		True
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														# We have a SentencePiece fixture for testing
														_lowerCAmelCase     :							int    =    XLMRobertaTokenizer(__a,			keep_accents=__a)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    "<pad>"
														_lowerCAmelCase     :							Dict    =    1

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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    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(__a),			1002)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.assertEqual(self.get_tokenizer().vocab_size,			1002)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    XLMRobertaTokenizer(__a,			keep_accents=__a)

														_lowerCAmelCase     :							List[Any]    =    tokenizer.tokenize("This is a test")
														self.assertListEqual(__a,			["▁This", "▁is", "▁a", "▁t", "est"])

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

														_lowerCAmelCase     :							List[Any]    =    tokenizer.tokenize("I was born in 92000, and this is falsé.")
														self.assertListEqual(
														    __a,			[
														        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",
														        ".",
														    ],			)
														_lowerCAmelCase     :							Any    =    tokenizer.convert_tokens_to_ids(__a)
														self.assertListEqual(
														    __a,			[
														        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 ^
														    ],			)

														_lowerCAmelCase     :							Tuple    =    tokenizer.convert_ids_to_tokens(__a)
														self.assertListEqual(
														    __a,			[
														        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    snake_case__      (   self):

														'''simple docstring'''

														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

														_lowerCAmelCase     :							str    =    (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})"):
																												_lowerCAmelCase     :							Optional[int]    =    self.rust_tokenizer_class.from_pretrained(__a,			**__a)
																												_lowerCAmelCase     :							Optional[int]    =    self.tokenizer_class.from_pretrained(__a,			**__a)

																												_lowerCAmelCase     :							Any    =    tempfile.mkdtemp()

																												_lowerCAmelCase     :							Any    =    tokenizer_r.save_pretrained(__a)
																												_lowerCAmelCase     :							Optional[int]    =    tokenizer_p.save_pretrained(__a)

																												# 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))
																												_lowerCAmelCase     :							int    =    tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
																												self.assertSequenceEqual(__a,			__a)

																												# Checks everything loads correctly in the same way
																												_lowerCAmelCase     :							List[Any]    =    tokenizer_r.from_pretrained(__a)
																												_lowerCAmelCase     :							Tuple    =    tokenizer_p.from_pretrained(__a)

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

																												shutil.rmtree(__a)

																												# Save tokenizer rust, legacy_format=True
																												_lowerCAmelCase     :							Union[str, Any]    =    tempfile.mkdtemp()

																												_lowerCAmelCase     :							Optional[Any]    =    tokenizer_r.save_pretrained(__a,			legacy_format=__a)
																												_lowerCAmelCase     :							List[Any]    =    tokenizer_p.save_pretrained(__a)

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

																												# Checks everything loads correctly in the same way
																												_lowerCAmelCase     :							List[str]    =    tokenizer_r.from_pretrained(__a)
																												_lowerCAmelCase     :							Dict    =    tokenizer_p.from_pretrained(__a)

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

																												shutil.rmtree(__a)

																												# Save tokenizer rust, legacy_format=False
																												_lowerCAmelCase     :							List[Any]    =    tempfile.mkdtemp()

																												_lowerCAmelCase     :							Any    =    tokenizer_r.save_pretrained(__a,			legacy_format=__a)
																												_lowerCAmelCase     :							int    =    tokenizer_p.save_pretrained(__a)

																												# 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
																												_lowerCAmelCase     :							Tuple    =    tokenizer_r.from_pretrained(__a)
																												_lowerCAmelCase     :							List[str]    =    tokenizer_p.from_pretrained(__a)

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

																												shutil.rmtree(__a)







							@cached_property
							def    snake_case__      (   self):

														'''simple docstring'''

														return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")







							def    snake_case__      (   self):

														'''simple docstring'''

														with tempfile.NamedTemporaryFile() as f:
																					shutil.copyfile(__a,			f.name)
																					_lowerCAmelCase     :							Dict    =    XLMRobertaTokenizer(f.name,			keep_accents=__a)
																					_lowerCAmelCase     :							Optional[int]    =    pickle.dumps(__a)
														pickle.loads(__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														if not self.test_rust_tokenizer:
																					return

														_lowerCAmelCase     :							Any    =    self.get_tokenizer()
														_lowerCAmelCase     :							int    =    self.get_rust_tokenizer()

														_lowerCAmelCase     :							Tuple    =    "I was born in 92000, and this is falsé."

														_lowerCAmelCase     :							Any    =    tokenizer.tokenize(__a)
														_lowerCAmelCase     :							List[str]    =    rust_tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Tuple    =    tokenizer.encode(__a,			add_special_tokens=__a)
														_lowerCAmelCase     :							Dict    =    rust_tokenizer.encode(__a,			add_special_tokens=__a)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							List[str]    =    self.get_rust_tokenizer()
														_lowerCAmelCase     :							List[Any]    =    tokenizer.encode(__a)
														_lowerCAmelCase     :							Optional[int]    =    rust_tokenizer.encode(__a)
														self.assertListEqual(__a,			__a)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    "Hello World!"
														_lowerCAmelCase     :							int    =    [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(__a,			self.big_tokenizer.encode(__a))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    (
														    "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"
														)
														_lowerCAmelCase     :							Tuple    =    [
														    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(__a,			self.big_tokenizer.encode(__a))







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    {"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=__a,			model_name="xlm-roberta-base",			revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3",			)




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



