import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  SCREAMING_SNAKE_CASE :	Optional[int]							     =		"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )



def   __A		(			_A							,				_A							,				_A=None							,				_A=None							,				_A=None							,				_A=None							,				_A=None							,				_A=None							,				):



 """simple docstring"""


 if attention_mask is None:
  __a       =  np.where(input_ids != config.pad_token_id							,				1							,				0					)
 if decoder_attention_mask is None:
  __a       =  np.where(decoder_input_ids != config.pad_token_id							,				1							,				0					)
 if head_mask is None:
  __a       =  np.ones((config.encoder_layers, config.encoder_attention_heads)					)
 if decoder_head_mask is None:
  __a       =  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
 if cross_attn_head_mask is None:
  __a       =  np.ones((config.decoder_layers, config.decoder_attention_heads)					)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }
class    A_ :

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict=13     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=7     ,							__SCREAMING_SNAKE_CASE :      int=True     ,							__SCREAMING_SNAKE_CASE :      int=False     ,							__SCREAMING_SNAKE_CASE :      Tuple=99     ,							__SCREAMING_SNAKE_CASE :      Tuple=16     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							__SCREAMING_SNAKE_CASE :      Tuple=4     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=4     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]="gelu"     ,							__SCREAMING_SNAKE_CASE :      Any=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=32     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Tuple=0     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  seq_length
   __a       =  is_training
   __a       =  use_labels
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  eos_token_id
   __a       =  pad_token_id
   __a       =  bos_token_id
   __a       =  initializer_range

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,							self.vocab_size       )     ,							3     ,							self.vocab_size       )
   __a       =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,							dtype=np.intaa       ))     ,							-1       )

   __a       =  shift_tokens_right(__SCREAMING_SNAKE_CASE     ,							1     ,							2       )

   __a       =  BlenderbotConfig(
       vocab_size=self.vocab_size     ,							d_model=self.hidden_size     ,							encoder_layers=self.num_hidden_layers     ,							decoder_layers=self.num_hidden_layers     ,							encoder_attention_heads=self.num_attention_heads     ,							decoder_attention_heads=self.num_attention_heads     ,							encoder_ffn_dim=self.intermediate_size     ,							decoder_ffn_dim=self.intermediate_size     ,							dropout=self.hidden_dropout_prob     ,							attention_dropout=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							eos_token_id=self.eos_token_id     ,							bos_token_id=self.bos_token_id     ,							pad_token_id=self.pad_token_id     ,							initializer_range=self.initializer_range     ,							use_cache=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  prepare_blenderbot_inputs_dict(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   return config, inputs_dict

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       ,     __a       =  self.prepare_config_and_inputs()
   return config, inputs_dict

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  20
   __a       =  model_class_name(__SCREAMING_SNAKE_CASE       )

   __a       =  model.encode(inputs_dict["input_ids"]       )

   __a       ,     __a       =  (
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   __a       =  model.init_cache(decoder_input_ids.shape[0]     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,							dtype="i4"       )

   __a       =  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]     ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,							)
   __a       =  model.decode(
       decoder_input_ids[:, :-1]     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							past_key_values=__SCREAMING_SNAKE_CASE     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,							dtype="i4"       )
   __a       =  model.decode(
       decoder_input_ids[:, -1:]     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							past_key_values=outputs_cache.past_key_values     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  model.decode(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
   self.parent.assertTrue(diff < 1E-3     ,							msg=f"""Max diff is {diff}"""       )

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  20
   __a       =  model_class_name(__SCREAMING_SNAKE_CASE       )

   __a       =  model.encode(inputs_dict["input_ids"]       )

   __a       ,     __a       =  (
       inputs_dict["decoder_input_ids"],
       inputs_dict["decoder_attention_mask"],
   )

   __a       =  jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
       ]     ,							axis=-1     ,							)

   __a       =  model.init_cache(decoder_input_ids.shape[0]     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]     ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,							)

   __a       =  model.decode(
       decoder_input_ids[:, :-1]     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							past_key_values=__SCREAMING_SNAKE_CASE     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,							dtype="i4"       )
   __a       =  model.decode(
       decoder_input_ids[:, -1:]     ,							__SCREAMING_SNAKE_CASE     ,							past_key_values=outputs_cache.past_key_values     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							decoder_position_ids=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  model.decode(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE       )

   __a       =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
   self.parent.assertTrue(diff < 1E-3     ,							msg=f"""Max diff is {diff}"""       )
@require_flax
class    A_ (       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			99

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  np.array(
       [
           [71, 82, 18, 33, 46, 91, 2],
           [68, 34, 26, 58, 30, 82, 2],
           [5, 97, 17, 39, 94, 40, 2],
           [76, 83, 94, 25, 70, 78, 2],
           [87, 59, 41, 35, 48, 66, 2],
           [55, 13, 16, 58, 5, 2, 1],  # note padding
           [64, 27, 31, 51, 12, 75, 2],
           [52, 64, 86, 17, 83, 39, 2],
           [48, 61, 9, 24, 71, 82, 2],
           [26, 1, 60, 48, 22, 13, 2],
           [21, 5, 62, 28, 14, 76, 2],
           [45, 98, 37, 86, 59, 48, 2],
           [70, 70, 50, 9, 28, 0, 2],
       ]     ,							dtype=np.intaa     ,							)

   __a       =  input_ids.shape[0]
   __a       =  BlenderbotConfig(
       vocab_size=self.vocab_size     ,							d_model=24     ,							encoder_layers=2     ,							decoder_layers=2     ,							encoder_attention_heads=2     ,							decoder_attention_heads=2     ,							encoder_ffn_dim=32     ,							decoder_ffn_dim=32     ,							max_position_embeddings=48     ,							eos_token_id=2     ,							pad_token_id=1     ,							bos_token_id=0     ,							)
   return config, input_ids, batch_size

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       ,     __a       =  self._get_config_and_data()
   __a       =  FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE       )
   __a       =  lm_model(input_ids=__SCREAMING_SNAKE_CASE       )
   __a       =  (batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["logits"].shape     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  BlenderbotConfig(
       vocab_size=self.vocab_size     ,							d_model=14     ,							encoder_layers=2     ,							decoder_layers=2     ,							encoder_attention_heads=2     ,							decoder_attention_heads=2     ,							encoder_ffn_dim=8     ,							decoder_ffn_dim=8     ,							max_position_embeddings=48     ,							)
   __a       =  FlaxBlenderbotForConditionalGeneration(__SCREAMING_SNAKE_CASE       )
   __a       =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,							dtype=np.intaa       )
   __a       =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,							dtype=np.intaa       )
   __a       =  lm_model(input_ids=__SCREAMING_SNAKE_CASE     ,							decoder_input_ids=__SCREAMING_SNAKE_CASE       )
   __a       =  (*summary.shape, config.vocab_size)
   self.assertEqual(outputs["logits"].shape     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,							dtype=np.intaa       )
   __a       =  shift_tokens_right(__SCREAMING_SNAKE_CASE     ,							1     ,							2       )
   __a       =  np.equal(__SCREAMING_SNAKE_CASE     ,							1       ).astype(np.floataa       ).sum()
   __a       =  np.equal(__SCREAMING_SNAKE_CASE     ,							1       ).astype(np.floataa       ).sum()
   self.assertEqual(shifted.shape     ,							input_ids.shape       )
   self.assertEqual(__SCREAMING_SNAKE_CASE     ,							n_pad_before - 1       )
   self.assertTrue(np.equal(shifted[:, 0]     ,							2       ).all()       )
@require_flax
class    A_ (       a_			,       unittest.TestCase			,       a_ ):
  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  FlaxBlenderbotModelTester(self       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
    self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
    self.model_tester.check_use_cache_forward_with_attn_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__       ):
     __a       =  self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
     __a       =  model_class(__SCREAMING_SNAKE_CASE       )

     @jax.jit
     def encode_jitted(__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							**__SCREAMING_SNAKE_CASE :      Any       ):
      return model.encode(input_ids=__SCREAMING_SNAKE_CASE     ,							attention_mask=__SCREAMING_SNAKE_CASE       )

     with self.subTest("JIT Enabled"       ):
      __a       =  encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     with self.subTest("JIT Disabled"       ):
      with jax.disable_jit():
       __a       =  encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							len(__SCREAMING_SNAKE_CASE       )       )
     for jitted_output, output in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
      self.assertEqual(jitted_output.shape     ,							output.shape       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__       ):
     __a       =  model_class(__SCREAMING_SNAKE_CASE       )
     __a       =  model.encode(inputs_dict["input_ids"]     ,							inputs_dict["attention_mask"]       )

     __a       =  {
         "decoder_input_ids": inputs_dict["decoder_input_ids"],
         "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
         "encoder_outputs": encoder_outputs,
     }

     @jax.jit
     def decode_jitted(__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
      return model.decode(
          decoder_input_ids=__SCREAMING_SNAKE_CASE     ,							decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,							encoder_outputs=__SCREAMING_SNAKE_CASE     ,							)

     with self.subTest("JIT Enabled"       ):
      __a       =  decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     with self.subTest("JIT Disabled"       ):
      with jax.disable_jit():
       __a       =  decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()

     self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							len(__SCREAMING_SNAKE_CASE       )       )
     for jitted_output, output in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
      self.assertEqual(jitted_output.shape     ,							output.shape       )

  @slow
  def 		_UpperCAmelCase   (		self :      Any       ):
   for model_class_name in self.all_model_classes:
    __a       =  model_class_name.from_pretrained("facebook/blenderbot-400M-distill"       )
    # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
    __a       =  np.ones((1, 1)       ) * model.config.eos_token_id
    __a       =  model(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

  @unittest.skipUnless(jax_device != "cpu"     ,							"3B test too slow on CPU."       )
  @slow
  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
   __a       =  {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

   __a       =  FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"     ,							from_pt=__SCREAMING_SNAKE_CASE       )
   __a       =  BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"       )

   __a       =  ["Sam"]
   __a       =  tokenizer(__SCREAMING_SNAKE_CASE     ,							return_tensors="jax"       )

   __a       =  model.generate(**__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  "Sam is a great name. It means \"sun\" in Gaelic."

   __a       =  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   assert generated_txt[0].strip() == tgt_text



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       )       )


