from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__  :      str        =					logging.get_logger(__name__)

lowercase__  :      Tuple        =					{
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''vit_msn'''






  def __init__(      self			:       List[Any]  ,							UpperCAmelCase__			:       str=768  ,							UpperCAmelCase__			:       Any=12  ,							UpperCAmelCase__			:       str=12  ,							UpperCAmelCase__			:       int=3072  ,							UpperCAmelCase__			:       str="gelu"  ,							UpperCAmelCase__			:       Union[str, Any]=0.0  ,							UpperCAmelCase__			:       Any=0.0  ,							UpperCAmelCase__			:       Union[str, Any]=0.02  ,							UpperCAmelCase__			:       Union[str, Any]=1e-06  ,							UpperCAmelCase__			:       str=224  ,							UpperCAmelCase__			:       Optional[int]=16  ,							UpperCAmelCase__			:       Union[str, Any]=3  ,							UpperCAmelCase__			:       str=True  ,							**UpperCAmelCase__			:       int  ,							)				->str:
        super().__init__(**UpperCAmelCase__  )

        UpperCAmelCase_            =  hidden_size
        UpperCAmelCase_            =  num_hidden_layers
        UpperCAmelCase_            =  num_attention_heads
        UpperCAmelCase_            =  intermediate_size
        UpperCAmelCase_            =  hidden_act
        UpperCAmelCase_            =  hidden_dropout_prob
        UpperCAmelCase_            =  attention_probs_dropout_prob
        UpperCAmelCase_            =  initializer_range
        UpperCAmelCase_            =  layer_norm_eps
        UpperCAmelCase_            =  image_size
        UpperCAmelCase_            =  patch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  qkv_bias






'''simple docstring'''
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
      lowercase__  :      Optional[Any]        =					"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  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]=None							,    _UpperCamelCase  :  Any=None							,    _UpperCamelCase  :  int=None							,    _UpperCamelCase  :  int=None							,    _UpperCamelCase  :  Union[str, Any]=None							,    _UpperCamelCase  :  Any=None							,    ):


      '''simple docstring'''


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




  '''simple docstring'''



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






  def  lowerCAmelCase__ (      self			:       int  )				->Any:
        UpperCAmelCase_            =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,							self.vocab_size  )  ,							3  ,							self.vocab_size  )
        UpperCAmelCase_            =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,							dtype=np.intaa  ))  ,							-1  )

        UpperCAmelCase_            =  shift_tokens_right(UpperCAmelCase__  ,							1  ,							2  )

        UpperCAmelCase_            =  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__  ,							)
        UpperCAmelCase_            =  prepare_blenderbot_inputs_dict(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
        return config, inputs_dict






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[str]:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.prepare_config_and_inputs()
        return config, inputs_dict






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Tuple  )				->Tuple:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  model_class_name(UpperCAmelCase__  )

        UpperCAmelCase_            =  model.encode(inputs_dict['''input_ids''']  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        UpperCAmelCase_            =  model.init_cache(decoder_input_ids.shape[0]  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,							dtype='''i4'''  )

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

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

        UpperCAmelCase_            =  model.decode(UpperCAmelCase__  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Any  )				->Union[str, Any]:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  model_class_name(UpperCAmelCase__  )

        UpperCAmelCase_            =  model.encode(inputs_dict['''input_ids''']  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

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

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

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

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

        UpperCAmelCase_            =  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 lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	99






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Optional[Any]:
        UpperCAmelCase_            =  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  ,							)

        UpperCAmelCase_            =  input_ids.shape[0]
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       Any  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  self._get_config_and_data()
        UpperCAmelCase_            =  FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__  )
        UpperCAmelCase_            =  lm_model(input_ids=UpperCAmelCase__  )
        UpperCAmelCase_            =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  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  ,							)
        UpperCAmelCase_            =  FlaxBlenderbotForConditionalGeneration(UpperCAmelCase__  )
        UpperCAmelCase_            =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,							dtype=np.intaa  )
        UpperCAmelCase_            =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,							dtype=np.intaa  )
        UpperCAmelCase_            =  lm_model(input_ids=UpperCAmelCase__  ,							decoder_input_ids=UpperCAmelCase__  )
        UpperCAmelCase_            =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->List[Any]:
        UpperCAmelCase_            =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,							dtype=np.intaa  )
        UpperCAmelCase_            =  shift_tokens_right(UpperCAmelCase__  ,							1  ,							2  )
        UpperCAmelCase_            =  np.equal(UpperCAmelCase__  ,							1  ).astype(np.floataa  ).sum()
        UpperCAmelCase_            =  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 lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase     ,		lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  lowerCAmelCase__										=	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[Any]:
        UpperCAmelCase_            =  FlaxBlenderbotModelTester(self  )






  def  lowerCAmelCase__ (      self			:       str  )				->Tuple:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       Tuple  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       Dict  )				->Tuple:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__  ):
                    UpperCAmelCase_            =  self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )
                    UpperCAmelCase_            =  model_class(UpperCAmelCase__  )

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

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

                    with self.subTest('''JIT Disabled'''  ):
                          with jax.disable_jit():
                                UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       str  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__  ):
                    UpperCAmelCase_            =  model_class(UpperCAmelCase__  )
                    UpperCAmelCase_            =  model.encode(inputs_dict['''input_ids''']  ,							inputs_dict['''attention_mask''']  )

                    UpperCAmelCase_            =  {
                        '''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__			:       Optional[int]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       int  ):
                          return model.decode(
                              decoder_input_ids=UpperCAmelCase__  ,							decoder_attention_mask=UpperCAmelCase__  ,							encoder_outputs=UpperCAmelCase__  ,							)

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

                    with self.subTest('''JIT Disabled'''  ):
                          with jax.disable_jit():
                                UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       int  )				->int:
        for model_class_name in self.all_model_classes:
              UpperCAmelCase_            =  model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''  )
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              UpperCAmelCase_            =  np.ones((1, 1)  ) * model.config.eos_token_id
              UpperCAmelCase_            =  model(UpperCAmelCase__  )
              self.assertIsNotNone(UpperCAmelCase__  )






  @unittest.skipUnless(jax_device != '''cpu'''  ,							'''3B test too slow on CPU.'''  )
  @slow
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[Any]:
        UpperCAmelCase_            =  {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
        UpperCAmelCase_            =  {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

        UpperCAmelCase_            =  FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''  ,							from_pt=UpperCAmelCase__  )
        UpperCAmelCase_            =  BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''  )

        UpperCAmelCase_            =  ['''Sam''']
        UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							return_tensors='''jax'''  )

        UpperCAmelCase_            =  model.generate(**UpperCAmelCase__  ,							**UpperCAmelCase__  )
        UpperCAmelCase_            =  '''Sam is a great name. It means "sun" in Gaelic.'''

        UpperCAmelCase_            =  tokenizer.batch_decode(UpperCAmelCase__  ,							**UpperCAmelCase__  )
        assert generated_txt[0].strip() == tgt_text





