
'''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  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       XLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
											lowercase_	:     Union[str, Any]													=       torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]		)
											# The dog is cute and lives in the garden house

											lowercase_	:     Optional[Any]													=       torch.Size((1, 12, 7_68)		)  # batch_size, sequence_length, embedding_vector_dim
											lowercase_	:     Union[str, Any]													=       torch.tensor(
											    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]		)
											#  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():
																	lowercase_	:     Optional[Any]													=       model(__SCREAMING_SNAKE_CASE		)['''last_hidden_state'''].detach()
											self.assertEqual(output.shape				,							__SCREAMING_SNAKE_CASE		)
											# compare the actual values for a slice of last dim
											self.assertTrue(torch.allclose(output[:, :, -1]				,							__SCREAMING_SNAKE_CASE				,							atol=1E-3		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       XLMRobertaModel.from_pretrained('''xlm-roberta-large'''		)
											lowercase_	:     Any													=       torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]		)
											# The dog is cute and lives in the garden house

											lowercase_	:     List[Any]													=       torch.Size((1, 12, 10_24)		)  # batch_size, sequence_length, embedding_vector_dim
											lowercase_	:     str													=       torch.tensor(
											    [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]		)
											#  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():
																	lowercase_	:     Optional[Any]													=       model(__SCREAMING_SNAKE_CASE		)['''last_hidden_state'''].detach()
											self.assertEqual(output.shape				,							__SCREAMING_SNAKE_CASE		)
											# compare the actual values for a slice of last dim
											self.assertTrue(torch.allclose(output[:, :, -1]				,							__SCREAMING_SNAKE_CASE				,							atol=1E-3		)		)





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       ["Pix2StructImageProcessor"]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Pix2StructPreTrainedModel",
							    "Pix2StructForConditionalGeneration",
							    "Pix2StructVisionModel",
							    "Pix2StructTextModel",
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

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



