



'''simple docstring'''



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
							raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
lowerCAmelCase_     :       List[str]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       int								=						'Hello world! cécé herlolip'
def 					UpperCAmelCase   (						A  :				str					,		A  :				str					,		A  :				bool				):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			FairseqRobertaModel.from_pretrained(A				)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,		hidden_size=roberta.cfg.model.encoder_embed_dim					,		num_hidden_layers=roberta.cfg.model.encoder_layers					,		num_attention_heads=roberta.cfg.model.encoder_attention_heads					,		intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim					,		max_position_embeddings=514					,		type_vocab_size=1					,		layer_norm_eps=1e-5					,		)
							if classification_head:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print('''Our RoBERTa config:'''					,		A				)

							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			XLMRobertaXLForSequenceClassification(A				) if classification_head else XLMRobertaXLForMaskedLM(A				)
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			roberta_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE					:      str     					=			roberta_sent_encoder.embed_positions.weight
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.

							SCREAMING_SNAKE_CASE					:      List[Any]     					=			roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_sent_encoder.layer_norm.bias

							for i in range(config.num_hidden_layers				):
														# Encoder: start of layer
														SCREAMING_SNAKE_CASE					:      BertLayer     					=			model.roberta.encoder.layer[i]
														SCREAMING_SNAKE_CASE					:      TransformerSentenceEncoderLayer     					=			roberta_sent_encoder.layers[i]

														SCREAMING_SNAKE_CASE					:      RobertaAttention     					=			layer.attention
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE					:      BertSelfAttention     					=			layer.attention.self
														assert (
														    roberta_layer.self_attn.k_proj.weight.data.shape
														    == roberta_layer.self_attn.q_proj.weight.data.shape
														    == roberta_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)				)
														)

														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE					:      int     					=			roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE					:      Dict     					=			roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE					:      int     					=			roberta_layer.self_attn.v_proj.bias

														# self-attention output
														SCREAMING_SNAKE_CASE					:      BertSelfOutput     					=			layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE					:      BertIntermediate     					=			layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE					:      str     					=			roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE					:      BertOutput     					=			layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta_layer.fca.bias
														# end of layer

							if classification_head:
														SCREAMING_SNAKE_CASE					:      Any     					=			roberta.model.classification_heads['''mnli'''].dense.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			roberta.model.classification_heads['''mnli'''].dense.bias
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			roberta.model.classification_heads['''mnli'''].out_proj.weight
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta.model.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														SCREAMING_SNAKE_CASE					:      List[str]     					=			roberta.model.encoder.lm_head.dense.weight
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			roberta.model.encoder.lm_head.dense.bias
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta.model.encoder.lm_head.layer_norm.weight
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			roberta.model.encoder.lm_head.layer_norm.bias
														SCREAMING_SNAKE_CASE					:      Tuple     					=			roberta.model.encoder.lm_head.weight
														SCREAMING_SNAKE_CASE					:      int     					=			roberta.model.encoder.lm_head.bias

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE					:      torch.Tensor     					=			roberta.encode(A				).unsqueeze(0				)  # batch of size 1

							SCREAMING_SNAKE_CASE					:      str     					=			model(A				)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE					:      Any     					=			roberta.model.classification_heads['''mnli'''](roberta.extract_features(A				)				)
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			roberta.model(A				)[0]
							print(our_output.shape					,		their_output.shape				)
							SCREAMING_SNAKE_CASE					:      Any     					=			torch.max(torch.abs(our_output - their_output				)				).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
							SCREAMING_SNAKE_CASE					:      int     					=			torch.allclose(A					,		A					,		atol=1e-3				)
							print('''Do both models output the same tensors?'''					,		'''🔥''' if success else '''💩'''				)
							if not success:
														raise Exception('''Something went wRoNg'''				)

							pathlib.Path(A				).mkdir(parents=A					,		exist_ok=A				)
							print(F"""Saving model to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(A				)


if __name__ == "__main__":
							lowerCAmelCase_     :       int								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
							)
							lowerCAmelCase_     :       int								=						parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)









'''simple docstring'''



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


lowerCAmelCase_     :       int								=						get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				XLMRobertaTokenizer
						_lowerCAmelCase     :   Optional[Any]	     =				XLMRobertaTokenizerFast
						_lowerCAmelCase     :   List[str]	     =				True
						_lowerCAmelCase     :   Union[str, Any]	     =				True







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)
													tokenizer.save_pretrained(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''<pad>'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			1

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







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			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(lowerCAmelCase__				)				,    10_02				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.assertEqual(self.get_tokenizer().vocab_size				,    10_02				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCAmelCase__				,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)				,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,    )

													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        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''',
													        '''.''',
													    ]				,    )
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]				,    )

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        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 					__lowercase    (  self   :      Tuple				):

													"""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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			(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})"""				):
																											SCREAMING_SNAKE_CASE					:      int     					=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self.tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      int     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# 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				)				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

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

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=True
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

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

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      str     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

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

																											shutil.rmtree(lowerCAmelCase__				)

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

																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# 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
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

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

																											shutil.rmtree(lowerCAmelCase__				)







						@cached_property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													with tempfile.NamedTemporaryFile() as f:
																				shutil.copyfile(lowerCAmelCase__				,    f.name				)
																				SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(f.name				,    keep_accents=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			pickle.dumps(lowerCAmelCase__				)
													pickle.loads(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''I was born in 92000, and this is falsé.'''

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.tokenize(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			rust_tokenizer.tokenize(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.encode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''Hello World!'''
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[0, 3_53_78, 66_61, 38, 2]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

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







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			(
													    '''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'''
													)
													SCREAMING_SNAKE_CASE					:      Any     					=			[
													    0,
													    32_93,
													    83,
													    10,
													    45_52,
													    49_89,
													    79_86,
													    6_78,
													    10,
													    59_15,
													    1_11,
													    17_94_59,
													    12_48_50,
													    4,
													    60_44,
													    2_37,
													    12,
													    6,
													    5,
													    6,
													    4,
													    67_80,
													    7_05,
													    15,
													    13_88,
													    44,
													    3_78,
													    1_01_14,
													    7_11,
													    1_52,
													    20,
													    6,
													    5,
													    2_23_76,
													    6_42,
													    12_21,
													    1_51_90,
													    3_41_53,
													    4_50,
													    56_08,
													    9_59,
													    11_19,
													    5_77_02,
													    1_36,
													    1_86,
													    47,
													    10_98,
													    2_93_67,
													    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,
													    60_44,
													    2_37,
													    62_84,
													    5_09_01,
													    5_28,
													    31,
													    90,
													    34,
													    9_27,
													    2,
													]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

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







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													# fmt: off
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=lowerCAmelCase__				,    model_name='''xlm-roberta-base'''				,    revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,    )




