



"""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
       UpperCAmelCase						:		List[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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  __lowerCAmelCase=None ,  ) ->						Optional[int]:







  '''simple docstring'''


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

    def __init__(				self			:				Tuple ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				Optional[int]=1_3 ,  lowerCAmelCase_			:				List[str]=7 ,  lowerCAmelCase_			:				int=True ,  lowerCAmelCase_			:				Union[str, Any]=False ,  lowerCAmelCase_			:				Union[str, Any]=9_9 ,  lowerCAmelCase_			:				List[Any]=1_6 ,  lowerCAmelCase_			:				Dict=2 ,  lowerCAmelCase_			:				str=4 ,  lowerCAmelCase_			:				str=4 ,  lowerCAmelCase_			:				List[str]="gelu" ,  lowerCAmelCase_			:				str=0.1 ,  lowerCAmelCase_			:				Union[str, Any]=0.1 ,  lowerCAmelCase_			:				List[Any]=3_2 ,  lowerCAmelCase_			:				List[Any]=2 ,  lowerCAmelCase_			:				List[Any]=1 ,  lowerCAmelCase_			:				List[str]=0 ,  lowerCAmelCase_			:				Optional[Any]=0.02 ,  ):







      """simple docstring"""







      lowercase_								= parent
      lowercase_								= batch_size
      lowercase_								= seq_length
      lowercase_								= is_training
      lowercase_								= use_labels
      lowercase_								= vocab_size
      lowercase_								= hidden_size
      lowercase_								= num_hidden_layers
      lowercase_								= num_attention_heads
      lowercase_								= intermediate_size
      lowercase_								= hidden_act
      lowercase_								= hidden_dropout_prob
      lowercase_								= attention_probs_dropout_prob
      lowercase_								= max_position_embeddings
      lowercase_								= eos_token_id
      lowercase_								= pad_token_id
      lowercase_								= bos_token_id
      lowercase_								= initializer_range

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







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

      lowercase_								= shift_tokens_right(lowerCAmelCase_ ,  1 ,  2)

      lowercase_								= 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=lowerCAmelCase_ ,  )
      lowercase_								= prepare_blenderbot_inputs_dict(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)
      return config, inputs_dict

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







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

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= 2_0
      lowercase_								= model_class_name(lowerCAmelCase_)

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

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

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

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

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

      lowercase_								= model.decode(lowerCAmelCase_ ,  lowerCAmelCase_)

      lowercase_								= 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			:				int ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[Any] ,  lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      lowercase_								= 2_0
      lowercase_								= model_class_name(lowerCAmelCase_)

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

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

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

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

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

      lowercase_								= model.decode(lowerCAmelCase_ ,  lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_)

      lowercase_								= 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    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):
    lowercase__      =   99

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= np.array(
          [
              [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
              [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
              [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
              [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
              [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
              [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
              [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
              [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
              [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
              [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
              [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
              [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
              [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
          ] ,  dtype=np.intaa ,  )

      lowercase_								= input_ids.shape[0]
      lowercase_								= BlenderbotConfig(
          vocab_size=self.vocab_size ,  d_model=2_4 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=3_2 ,  decoder_ffn_dim=3_2 ,  max_position_embeddings=4_8 ,  eos_token_id=2 ,  pad_token_id=1 ,  bos_token_id=0 ,  )
      return config, input_ids, batch_size

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_					,    lowercase_					,    lowercase_								= self._get_config_and_data()
      lowercase_								= FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
      lowercase_								= lm_model(input_ids=lowerCAmelCase_)
      lowercase_								= (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs["""logits"""].shape ,  lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_								= BlenderbotConfig(
          vocab_size=self.vocab_size ,  d_model=1_4 ,  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=4_8 ,  )
      lowercase_								= FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_)
      lowercase_								= np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,  dtype=np.intaa)
      lowercase_								= np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,  dtype=np.intaa)
      lowercase_								= lm_model(input_ids=lowerCAmelCase_ ,  decoder_input_ids=lowerCAmelCase_)
      lowercase_								= (*summary.shape, config.vocab_size)
      self.assertEqual(outputs["""logits"""].shape ,  lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[str]):







      """simple docstring"""







      lowercase_								= np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,  dtype=np.intaa)
      lowercase_								= shift_tokens_right(lowerCAmelCase_ ,  1 ,  2)
      lowercase_								= np.equal(lowerCAmelCase_ ,  1).astype(np.floataa).sum()
      lowercase_								= np.equal(lowerCAmelCase_ ,  1).astype(np.floataa).sum()
      self.assertEqual(shifted.shape ,  input_ids.shape)
      self.assertEqual(lowerCAmelCase_ ,  n_pad_before - 1)
      self.assertTrue(np.equal(shifted[:, 0] ,  2).all())







@require_flax
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  unittest.TestCase      ,  __UpperCAmelCase   ):
    lowercase__      =   True
    lowercase__      =   (
        (
            FlaxBlenderbotModel,
            FlaxBlenderbotForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    lowercase__      =   (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      lowercase_								= FlaxBlenderbotModelTester(self)

    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







      lowercase_					,    lowercase_								= self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[Any]):







      """simple docstring"""







      lowercase_					,    lowercase_								= 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(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      lowercase_					,    lowercase_								= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__):
          lowercase_								= self._prepare_for_class(lowerCAmelCase_ ,  lowerCAmelCase_)
          lowercase_								= model_class(lowerCAmelCase_)

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

          with self.subTest("""JIT Enabled"""):
            lowercase_								= encode_jitted(**lowerCAmelCase_).to_tuple()

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

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

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_					,    lowercase_								= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase_								= {
              """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(lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				Any ,  lowerCAmelCase_			:				List[Any]):
            return model.decode(
                decoder_input_ids=lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_ ,  encoder_outputs=lowerCAmelCase_ ,  )

          with self.subTest("""JIT Enabled"""):
            lowercase_								= decode_jitted(**lowerCAmelCase_).to_tuple()

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

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

    @slow
    def 					_UpperCAmelCase	(				self			:				Dict):







      """simple docstring"""







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




    @unittest.skipUnless(jax_device != """cpu""" ,  """3B test too slow on CPU.""")
    @slow
    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= {"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
      lowercase_								= {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

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

      lowercase_								= ["""Sam"""]
      lowercase_								= tokenizer(lowerCAmelCase_ ,  return_tensors="""jax""")

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

      lowercase_								= tokenizer.batch_decode(lowerCAmelCase_ ,  **lowerCAmelCase_)
      assert generated_txt[0].strip() == tgt_text






"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						list[list[int]]:







  '''simple docstring'''


  lowercase_								= []
  if len(__lowerCAmelCase							) == 1:
    return [nums.copy()]
  for _ in range(len(__lowerCAmelCase							)							):
    lowercase_								= nums.pop(0							)
    lowercase_								= permute(__lowerCAmelCase							)
    for perm in permutations:
      perm.append(__lowerCAmelCase							)
    result.extend(__lowerCAmelCase							)
    nums.append(__lowerCAmelCase							)
  return result


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  def backtrack(__lowerCAmelCase							):
    if start == len(__lowerCAmelCase							) - 1:
      output.append(nums[:]							)
    else:
      for i in range(__lowerCAmelCase ,  len(__lowerCAmelCase							)							):
        lowercase_					,    lowercase_								= nums[i], nums[start]
        backtrack(start + 1							)
        lowercase_					,    lowercase_								= nums[i], nums[start]  # backtrack

  lowercase_								= []
  backtrack(0							)
  return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       UpperCAmelCase						:		Optional[Any]			= permutea([1, 2, 3])
       print(res)
       doctest.testmod()

