



'''simple docstring'''



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  UpperCamelCase__(     unittest.TestCase ):




				@slow
				def   a__(	self    :				Union[str, Any]   )->  List[Any]:

										"""simple docstring"""




										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(lowercase__   )["""last_hidden_state"""].detach()
										self.assertEqual(output.shape   ,					lowercase__   )
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]   ,					lowercase__   ,					atol=1E-3   )   )





				@slow
				def   a__(	self    :				List[Any]   )->  Tuple:

										"""simple docstring"""




										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(lowercase__   )["""last_hidden_state"""].detach()
										self.assertEqual(output.shape   ,					lowercase__   )
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]   ,					lowercase__   ,					atol=1E-3   )   )









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





