


'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
		from ... import PreTrainedTokenizerBase, TensorType

__snake_case				 =	logging.get_logger(__name__)
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							'vision-encoder-decoder'
		_a       					=							True




		def __init__(						self       ,  **UpperCamelCase_				):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_				)
							if "encoder" not in kwargs or "decoder" not in kwargs:
												raise ValueError(
												    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
												    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''				)

							UpperCamelCase__  :List[Any]				   =					kwargs.pop('''encoder'''				)
							UpperCamelCase__  :Union[str, Any]				   =					encoder_config.pop('''model_type'''				)
							UpperCamelCase__  :int				   =					kwargs.pop('''decoder'''				)
							UpperCamelCase__  :Tuple				   =					decoder_config.pop('''model_type'''				)

							UpperCamelCase__  :Tuple				   =					AutoConfig.for_model(UpperCamelCase_       ,  **UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					AutoConfig.for_model(UpperCamelCase_       ,  **UpperCamelCase_				)
							UpperCamelCase__  :str				   =					True




		@classmethod
		def        lowerCAmelCase__							(						cls       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
							UpperCamelCase__  :int				   =					True
							UpperCamelCase__  :Any				   =					True

							return cls(encoder=encoder_config.to_dict()       ,  decoder=decoder_config.to_dict()       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					copy.deepcopy(self.__dict__				)
							UpperCamelCase__  :Optional[Any]				   =					self.encoder.to_dict()
							UpperCamelCase__  :List[str]				   =					self.decoder.to_dict()
							UpperCamelCase__  :Any				   =					self.__class__.model_type
							return output



class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							version.parse('1.11'   )




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return 1e-4




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}				)



class   lowercase      (				A__   ):

		"""simple docstring"""

		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					OrderedDict()
							UpperCamelCase__  :Union[str, Any]				   =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							UpperCamelCase__  :Tuple				   =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							UpperCamelCase__  :Optional[int]				   =					{0: '''batch''', 1: '''encoder_sequence'''}

							return common_inputs




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = -1       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							import torch

							UpperCamelCase__  :Optional[Any]				   =					OrderedDict()

							UpperCamelCase__  :Union[str, Any]				   =					super().generate_dummy_inputs(
							    UpperCamelCase_       ,  batch_size=UpperCamelCase_       ,  seq_length=UpperCamelCase_       ,  is_pair=UpperCamelCase_       ,  framework=UpperCamelCase_				)

							UpperCamelCase__   ,							UpperCamelCase__  :str				   =					dummy_input['''input_ids'''].shape
							UpperCamelCase__  :Dict				   =					(batch, encoder_sequence, self._config.encoder_hidden_size)
							UpperCamelCase__  :Union[str, Any]				   =					dummy_input.pop('''input_ids'''				)
							UpperCamelCase__  :Optional[int]				   =					dummy_input.pop('''attention_mask'''				)
							UpperCamelCase__  :Dict				   =					torch.zeros(UpperCamelCase_				)

							return common_inputs



class   lowercase      (				A__   ):

		"""simple docstring"""

		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							return VisionEncoderDecoderEncoderOnnxConfig(UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_ = "default"				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					encoder_config.hidden_size
							return VisionEncoderDecoderDecoderOnnxConfig(UpperCamelCase_       ,  UpperCamelCase_				)



'''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   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
							UpperCamelCase__  :List[str]				   =					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__  :Union[str, Any]				   =					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :Union[str, Any]				   =					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__  :Any				   =					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				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
							UpperCamelCase__  :List[str]				   =					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__  :Any				   =					torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :List[Any]				   =					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__  :Optional[int]				   =					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				)				)