






from argparse import ArgumentParser

from . import BaseTransformersCLICommand




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Optional[Any]:
       return DownloadCommand(args.model   ,      args.cache_dir   ,      args.force   ,      args.trust_remote_code )





class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  @staticmethod
  def  lowerCamelCase       (      lowerCamelCase		:			ArgumentParser							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    parser.add_parser("""download"""							)
         download_parser.add_argument(
             """--cache-dir"""       ,				type=lowerCamelCase       ,				default=lowerCamelCase       ,				help="""Path to location to store the models"""							)
         download_parser.add_argument(
             """--force"""       ,				action="""store_true"""       ,				help="""Force the model to be download even if already in cache-dir"""							)
         download_parser.add_argument(
             """--trust-remote-code"""       ,				action="""store_true"""       ,				help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""       ,				)
         download_parser.add_argument("""model"""       ,				type=lowerCamelCase       ,				help="""Name of the model to download"""							)
         download_parser.set_defaults(func=lowerCamelCase							)


  def __init__(      self		:			str       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool       ,				lowerCamelCase		:			bool							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    model
         _UpperCAmelCase							=    cache
         _UpperCAmelCase							=    force
         _UpperCAmelCase							=    trust_remote_code





  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         from ..models.auto import AutoModel, AutoTokenizer

         AutoModel.from_pretrained(
             self._model       ,				cache_dir=self._cache       ,				force_download=self._force       ,				trust_remote_code=self._trust_remote_code							)
         AutoTokenizer.from_pretrained(
             self._model       ,				cache_dir=self._cache       ,				force_download=self._force       ,				trust_remote_code=self._trust_remote_code							)







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     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			List[str]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-base"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 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(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		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-large"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 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(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							)							)