




'''simple docstring'''





import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class     a__					(      snake_case__				,       unittest.TestCase   ):
						_a							:					Optional[Any]      =					XLMTokenizer
						_a							:					Union[str, Any]      =					False






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											__lowerCAmelCase            = [
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "w</w>",
											    "r</w>",
											    "t</w>",
											    "lo",
											    "low",
											    "er</w>",
											    "low</w>",
											    "lowest</w>",
											    "newer</w>",
											    "wider</w>",
											    "<unk>",
											]
											__lowerCAmelCase            = dict(zip(_A	,    range(len(_A    )    )    )    )
											__lowerCAmelCase            = ["l o 123", "lo w 1456", "e r</w> 1789", ""]

											__lowerCAmelCase            = os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["vocab_file"]    )
											__lowerCAmelCase            = os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["merges_file"]    )
											with open(self.vocab_file	,    "w"    ) as fp:
																fp.write(json.dumps(_A    )    )
											with open(self.merges_file	,    "w"    ) as fp:
																fp.write("\n".join(_A    )    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = "lower newer"
											__lowerCAmelCase            = "lower newer"
											return input_text, output_text






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = XLMTokenizer(self.vocab_file	,    self.merges_file    )

											__lowerCAmelCase            = "lower"
											__lowerCAmelCase            = ["low", "er</w>"]
											__lowerCAmelCase            = tokenizer.tokenize(_A    )
											self.assertListEqual(_A	,    _A    )

											__lowerCAmelCase            = tokens + ["<unk>"]
											__lowerCAmelCase            = [1_4, 1_5, 2_0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A    )	,    _A    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = XLMTokenizer.from_pretrained("xlm-mlm-en-2048"    )

											__lowerCAmelCase            = tokenizer.encode("sequence builders"	,    add_special_tokens=_A    )
											__lowerCAmelCase            = tokenizer.encode("multi-sequence build"	,    add_special_tokens=_A    )

											__lowerCAmelCase            = tokenizer.build_inputs_with_special_tokens(_A    )
											__lowerCAmelCase            = tokenizer.build_inputs_with_special_tokens(_A	,    _A    )

											assert encoded_sentence == [0] + text + [1]
											assert encoded_pair == [0] + text + [1] + text_a + [1]










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     a__					(      snake_case__   ):
						_a							:					Optional[Any]      =					"""M-CLIP"""






						def __init__(     self	,    _A=1_0_2_4	,    _A=7_6_8	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = transformerDimSize
											__lowerCAmelCase            = imageDimSize
											super().__init__(**_A    )






class     a__					(      snake_case__   ):
						_a							:					Tuple      =					MCLIPConfig






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




											"""simple docstring"""



											super().__init__(_A	,    *_A	,    **_A    )
											__lowerCAmelCase            = XLMRobertaModel(_A    )
											__lowerCAmelCase            = torch.nn.Linear(
											    in_features=config.transformerDimensions	,    out_features=config.numDims    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



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




