




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   ):


 '''simple docstring'''







 @slow
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel.from_pretrained('xlm-roberta-base'		)
       SCREAMING_SNAKE_CASE_      		=     torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]		)
       # The dog is cute and lives in the garden house

       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 12, 768)		)  # batch_size, sequence_length, embedding_vector_dim
       SCREAMING_SNAKE_CASE_      		=     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():
             SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)['last_hidden_state'].detach()
       self.assertEqual(output.shape							,							_lowerCAmelCase		)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]							,							_lowerCAmelCase							,							atol=1E-3		)		)




 @slow
 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel.from_pretrained('xlm-roberta-large'		)
       SCREAMING_SNAKE_CASE_      		=     torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]		)
       # The dog is cute and lives in the garden house

       SCREAMING_SNAKE_CASE_      		=     torch.Size((1, 12, 1_024)		)  # batch_size, sequence_length, embedding_vector_dim
       SCREAMING_SNAKE_CASE_      		=     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():
             SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)['last_hidden_state'].detach()
       self.assertEqual(output.shape							,							_lowerCAmelCase		)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]							,							_lowerCAmelCase							,							atol=1E-3		)		)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase__					:  List[Any]      							=				TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				[
       'MT5EncoderModel',
       'MT5ForConditionalGeneration',
       'MT5ForQuestionAnswering',
       'MT5Model',
       'MT5PreTrainedModel',
       'MT5Stack',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   lowerCamelCase__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )