
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
    __lowerCAmelCase			:     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:
    __magic_name__				     =       np.where(input_ids != config.pad_token_id,     1,     0					)
  if decoder_attention_mask is None:
    __magic_name__				     =       np.where(decoder_input_ids != config.pad_token_id,     1,     0					)
  if head_mask is None:
    __magic_name__				     =       np.ones((config.encoder_layers, config.encoder_attention_heads)					)
  if decoder_head_mask is None:
    __magic_name__				     =       np.ones((config.decoder_layers, config.decoder_attention_heads)					)
  if cross_attn_head_mask is None:
    __magic_name__				     =       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        UpperCAmelCase_			:


  '''simple docstring'''







  def __init__( self			: int			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[Any]=13			,							UpperCamelCase__			: Any=7			,							UpperCamelCase__			: Tuple=True			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: int=99			,							UpperCamelCase__			: str=16			,							UpperCamelCase__			: int=2			,							UpperCamelCase__			: str=4			,							UpperCamelCase__			: int=4			,							UpperCamelCase__			: Optional[int]="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Union[str, Any]=32			,							UpperCamelCase__			: Union[str, Any]=2			,							UpperCamelCase__			: Optional[int]=1			,							UpperCamelCase__			: Tuple=0			,							UpperCamelCase__			: str=0.02			,							)			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       parent
    __magic_name__				     =       batch_size
    __magic_name__				     =       seq_length
    __magic_name__				     =       is_training
    __magic_name__				     =       use_labels
    __magic_name__				     =       vocab_size
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       eos_token_id
    __magic_name__				     =       pad_token_id
    __magic_name__				     =       bos_token_id
    __magic_name__				     =       initializer_range
  def 							_lowercase							( self			: Dict    )			->	Any:







    """simple docstring"""






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

    __magic_name__				     =       shift_tokens_right(UpperCamelCase__			,							1			,							2    )

    __magic_name__				     =       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=UpperCamelCase__			,							)
    __magic_name__				     =       prepare_blenderbot_inputs_dict(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
    return config, inputs_dict
  def 							_lowercase							( self			: str    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.prepare_config_and_inputs()
    return config, inputs_dict
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Optional[Any]    )			->	int:







    """simple docstring"""






    __magic_name__				     =       20
    __magic_name__				     =       model_class_name(UpperCamelCase__    )

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

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

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

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

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

    __magic_name__				     =       model.decode(UpperCamelCase__			,							UpperCamelCase__    )

    __magic_name__				     =       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 							_lowercase							( self			: str			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Any			,							UpperCamelCase__			: Tuple    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       20
    __magic_name__				     =       model_class_name(UpperCamelCase__    )

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

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

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

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

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

    __magic_name__				     =       model.decode(UpperCamelCase__			,							UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__    )

    __magic_name__				     =       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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  a__           =		99
  def 							_lowercase							( self			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       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			,							)

    __magic_name__				     =       input_ids.shape[0]
    __magic_name__				     =       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 							_lowercase							( self			: Tuple    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       self._get_config_and_data()
    __magic_name__				     =       FlaxBlenderbotForConditionalGeneration(UpperCamelCase__    )
    __magic_name__				     =       lm_model(input_ids=UpperCamelCase__    )
    __magic_name__				     =       (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Any    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       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			,							)
    __magic_name__				     =       FlaxBlenderbotForConditionalGeneration(UpperCamelCase__    )
    __magic_name__				     =       np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,							dtype=np.intaa    )
    __magic_name__				     =       np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,							dtype=np.intaa    )
    __magic_name__				     =       lm_model(input_ids=UpperCamelCase__			,							decoder_input_ids=UpperCamelCase__    )
    __magic_name__				     =       (*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape			,							UpperCamelCase__    )





  def 							_lowercase							( self			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,							dtype=np.intaa    )
    __magic_name__				     =       shift_tokens_right(UpperCamelCase__			,							1			,							2    )
    __magic_name__				     =       np.equal(UpperCamelCase__			,							1    ).astype(np.floataa    ).sum()
    __magic_name__				     =       np.equal(UpperCamelCase__			,							1    ).astype(np.floataa    ).sum()
    self.assertEqual(shifted.shape			,							input_ids.shape    )
    self.assertEqual(UpperCamelCase__			,							n_pad_before - 1    )
    self.assertTrue(np.equal(shifted[:, 0]			,							2    ).all()    )







@require_flax
class        UpperCAmelCase_			( _A  ,       unittest.TestCase  ,       _A     ):


  '''simple docstring'''







  a__           =		True
  a__           =		(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  a__           =		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
  def 							_lowercase							( self			: List[str]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       FlaxBlenderbotModelTester(self    )
  def 							_lowercase							( self			: Optional[int]    )			->	Tuple:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Dict    )			->	Dict:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       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(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Any    )			->	int:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__    ):
        __magic_name__				     =       self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__    )
        __magic_name__				     =       model_class(UpperCamelCase__    )

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

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       encode_jitted(**UpperCamelCase__    ).to_tuple()

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

        self.assertEqual(len(UpperCamelCase__    )			,							len(UpperCamelCase__    )    )
        for jitted_output, output in zip(UpperCamelCase__			,							UpperCamelCase__    ):
          self.assertEqual(jitted_output.shape			,							output.shape    )
  def 							_lowercase							( self			: int    )			->	List[Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

        __magic_name__				     =       {
            """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(UpperCamelCase__			: Dict			,							UpperCamelCase__			: Optional[Any]			,							UpperCamelCase__			: Optional[int]    ):
          return model.decode(
              decoder_input_ids=UpperCamelCase__			,							decoder_attention_mask=UpperCamelCase__			,							encoder_outputs=UpperCamelCase__			,							)

        with self.subTest("""JIT Enabled"""    ):
          __magic_name__				     =       decode_jitted(**UpperCamelCase__    ).to_tuple()

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

        self.assertEqual(len(UpperCamelCase__    )			,							len(UpperCamelCase__    )    )
        for jitted_output, output in zip(UpperCamelCase__			,							UpperCamelCase__    ):
          self.assertEqual(jitted_output.shape			,							output.shape    )
  @slow
  def 							_lowercase							( self			: Dict    )			->	Optional[Any]:







    """simple docstring"""






    for model_class_name in self.all_model_classes:
      __magic_name__				     =       model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""    )
      # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
      __magic_name__				     =       np.ones((1, 1)    ) * model.config.eos_token_id
      __magic_name__				     =       model(UpperCamelCase__    )
      self.assertIsNotNone(UpperCamelCase__    )





  @unittest.skipUnless(jax_device != """cpu"""			,							"""3B test too slow on CPU."""    )
  @slow
  def 							_lowercase							( self			: Optional[int]    )			->	Optional[int]:







    """simple docstring"""






    __magic_name__				     =       {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
    __magic_name__				     =       {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

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

    __magic_name__				     =       ["""Sam"""]
    __magic_name__				     =       tokenizer(UpperCamelCase__			,							return_tensors="""jax"""    )

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

    __magic_name__				     =       tokenizer.batch_decode(UpperCamelCase__			,							**UpperCamelCase__    )
    assert generated_txt[0].strip() == tgt_text





'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





