




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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	:		Tuple							       =   """platform"""

  import jax
  import jax.numpy as jnp

  from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
      FlaxBlenderbotSmallForConditionalGeneration,
      FlaxBlenderbotSmallModel,
      shift_tokens_right,
  )







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				lowercase_=None ,				)   ->       Optional[Any]:



       """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 				UpperCamelCase_    :

    '''simple docstring'''






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





           '''simple docstring'''

           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        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Tuple:





           '''simple docstring'''

           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(UpperCAmelCase__					, 1					, 2)

           A__       =    BlenderbotSmallConfig(
               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__					, )
           A__       =    prepare_blenderbot_inputs_dict(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)
           return config, inputs_dict


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							A__       =    self.prepare_config_and_inputs()
           return config, inputs_dict


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	List[Any]					, UpperCAmelCase__      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    20
           A__       =    model_class_name(UpperCAmelCase__)

           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]					, UpperCAmelCase__					, UpperCAmelCase__)
           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]					, UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__					, past_key_values=UpperCAmelCase__					, decoder_position_ids=UpperCAmelCase__					, )

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

           A__       =    model.decode(UpperCAmelCase__					, UpperCAmelCase__)

           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        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    20
           A__       =    model_class_name(UpperCAmelCase__)

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

           A__       =    model.decode(UpperCAmelCase__					, UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__)

           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 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      99


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Optional[int]:





           '''simple docstring'''

           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__       =    BlenderbotSmallConfig(
               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        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->str:





           '''simple docstring'''

           A__ ,							A__ ,							A__       =    self._get_config_and_data()
           A__       =    FlaxBlenderbotSmallForConditionalGeneration(UpperCAmelCase__)
           A__       =    lm_model(input_ids=UpperCAmelCase__)
           A__       =    (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Optional[Any]:





           '''simple docstring'''

           A__       =    BlenderbotSmallConfig(
               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__       =    FlaxBlenderbotSmallForConditionalGeneration(UpperCAmelCase__)
           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=UpperCAmelCase__					, decoder_input_ids=UpperCAmelCase__)
           A__       =    (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					, UpperCAmelCase__)





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





           '''simple docstring'''

           A__       =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					, dtype=np.intaa)
           A__       =    shift_tokens_right(UpperCAmelCase__					, 1					, 2)
           A__       =    np.equal(UpperCAmelCase__					, 1).astype(np.floataa).sum()
           A__       =    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_    ( UpperCAmelCase__		,					unittest.TestCase		,					UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    UpperCAmelCase__							  =      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Tuple:





           '''simple docstring'''

           A__       =    FlaxBlenderbotSmallModelTester(self)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->str:





           '''simple docstring'''

           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(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->List[str]:





           '''simple docstring'''

           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(UpperCAmelCase__					, UpperCAmelCase__)
                         A__       =    model_class(UpperCAmelCase__)

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

                         with self.subTest('''JIT Enabled'''):
                                A__       =    encode_jitted(**UpperCAmelCase__).to_tuple()

                         with self.subTest('''JIT Disabled'''):
                                with jax.disable_jit():
                                       A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           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(UpperCAmelCase__)
                         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(UpperCAmelCase__      :	int					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Any):
                                return model.decode(
                                    decoder_input_ids=UpperCAmelCase__					, decoder_attention_mask=UpperCAmelCase__					, encoder_outputs=UpperCAmelCase__					, )

                         with self.subTest('''JIT Enabled'''):
                                A__       =    decode_jitted(**UpperCAmelCase__).to_tuple()

                         with self.subTest('''JIT Disabled'''):
                                with jax.disable_jit():
                                       A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->Optional[int]:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__       =    model_class_name.from_pretrained('''facebook/blenderbot_small-90M''')
                  # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                  A__       =    np.ones((1, 1)) * model.config.eos_token_id
                  A__       =    model(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase	:		List[str]							       =   {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTMSNModel""",
      """ViTMSNForImageClassification""",
      """ViTMSNPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit_msn import (
        VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTMSNForImageClassification,
        ViTMSNModel,
        ViTMSNPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase	:		Tuple							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


