





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
					import torch

					from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      XLMRobertaModel.from_pretrained("""xlm-roberta-base"""	)
														UpperCamelCase__					  =      torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]	)
														# The dog is cute and lives in the garden house

														UpperCamelCase__					  =      torch.Size((1, 12, 768)	)  # batch_size, sequence_length, embedding_vector_dim
														UpperCamelCase__					  =      torch.tensor(
														    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]	)
														#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
														#  xlmr.eval()
														#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
														with torch.no_grad():
																					UpperCamelCase__					  =      model(UpperCamelCase_	)['''last_hidden_state'''].detach()
														self.assertEqual(output.shape	,			UpperCamelCase_	)
														# compare the actual values for a slice of last dim
														self.assertTrue(torch.allclose(output[:, :, -1]	,			UpperCamelCase_	,			atol=1E-3	)	)







							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      XLMRobertaModel.from_pretrained("""xlm-roberta-large"""	)
														UpperCamelCase__					  =      torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]	)
														# The dog is cute and lives in the garden house

														UpperCamelCase__					  =      torch.Size((1, 12, 1024)	)  # batch_size, sequence_length, embedding_vector_dim
														UpperCamelCase__					  =      torch.tensor(
														    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]	)
														#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
														#  xlmr.eval()
														#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
														with torch.no_grad():
																					UpperCamelCase__					  =      model(UpperCamelCase_	)['''last_hidden_state'''].detach()
														self.assertEqual(output.shape	,			UpperCamelCase_	)
														# compare the actual values for a slice of last dim
														self.assertTrue(torch.allclose(output[:, :, -1]	,			UpperCamelCase_	,			atol=1E-3	)	)








'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)