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    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

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



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

   __a       =  encoder_input_tokens.shape[1]
   __a       =  torch.arange(__SCREAMING_SNAKE_CASE     ,							device=encoder_input_tokens.device       )
   x += self.position_encoding(__SCREAMING_SNAKE_CASE       )

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


