

'''simple docstring'''

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
       import jax.numpy as jnp

       from transformers import FlaxXLMRobertaModel





@require_sentencepiece
@require_tokenizers
@require_flax
class 							lowercase						(						unittest.TestCase ):


   @slow
   def __UpperCAmelCase       (   self				:	str)      ->					Tuple:
         lowercase_     =      FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base")
         lowercase_     =      AutoTokenizer.from_pretrained("xlm-roberta-base")
         lowercase_     =      "The dog is cute and lives in the garden house"
         lowercase_     =      jnp.array([tokenizer.encode(__lowerCAmelCase)])

         lowercase_     =      (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
         lowercase_     =      jnp.array(
             [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

         lowercase_     =      model(__lowerCAmelCase)["last_hidden_state"]
         self.assertEqual(output.shape   ,		__lowerCAmelCase)
         # compare the actual values for a slice of last dim
         self.assertTrue(jnp.allclose(output[:, :, -1]   ,		__lowerCAmelCase   ,		atol=1e-3))







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






