'''simple docstring'''






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
    SCREAMING_SNAKE_CASE_	   =			'platform'

    import jax
    import jax.numpy as jnp

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






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]   ,    _lowercase    :				Union[str, Any]   ,    _lowercase    :				Optional[Any]=None   ,    _lowercase    :				List[str]=None   ,    _lowercase    :				int=None   ,    _lowercase    :				Optional[int]=None   ,    _lowercase    :				Dict=None   ,    _lowercase    :				Optional[Any]=None   ,    )							->	Tuple:
  if attention_mask is None:
    __UpperCAmelCase:					Optional[Any]		= np.where(input_ids != config.pad_token_id   ,    1   ,    0     )
  if decoder_attention_mask is None:
    __UpperCAmelCase:					int		= np.where(decoder_input_ids != config.pad_token_id   ,    1   ,    0     )
  if head_mask is None:
    __UpperCAmelCase:					Union[str, Any]		= np.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    __UpperCAmelCase:					Optional[Any]		= np.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    __UpperCAmelCase:					int		= 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 :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=13   ,    snake_case_=7   ,    snake_case_=True   ,    snake_case_=False   ,    snake_case_=99   ,    snake_case_=16   ,    snake_case_=2   ,    snake_case_=4   ,    snake_case_=4   ,    snake_case_="gelu"   ,    snake_case_=0.1   ,    snake_case_=0.1   ,    snake_case_=32   ,    snake_case_=2   ,    snake_case_=1   ,    snake_case_=0   ,    snake_case_=0.0_2   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					int		= parent
    __UpperCAmelCase:					int		= batch_size
    __UpperCAmelCase:					Union[str, Any]		= seq_length
    __UpperCAmelCase:					Tuple		= is_training
    __UpperCAmelCase:					Union[str, Any]		= use_labels
    __UpperCAmelCase:					Any		= vocab_size
    __UpperCAmelCase:					Tuple		= hidden_size
    __UpperCAmelCase:					int		= num_hidden_layers
    __UpperCAmelCase:					int		= num_attention_heads
    __UpperCAmelCase:					Tuple		= intermediate_size
    __UpperCAmelCase:					Optional[Any]		= hidden_act
    __UpperCAmelCase:					Tuple		= hidden_dropout_prob
    __UpperCAmelCase:					Dict		= attention_probs_dropout_prob
    __UpperCAmelCase:					Optional[int]		= max_position_embeddings
    __UpperCAmelCase:					Optional[int]		= eos_token_id
    __UpperCAmelCase:					Optional[int]		= pad_token_id
    __UpperCAmelCase:					Dict		= bos_token_id
    __UpperCAmelCase:					List[Any]		= initializer_range







  def    lowercase_  (							self					):

    '''simple docstring'''







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

    __UpperCAmelCase:					Dict		= shift_tokens_right(snake_case_   ,    1   ,    2					)

    __UpperCAmelCase:					List[str]		= 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=snake_case_   ,    )
    __UpperCAmelCase:					Dict		= prepare_blenderbot_inputs_dict(snake_case_   ,    snake_case_   ,    snake_case_					)
    return config, inputs_dict







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					int		= self.prepare_config_and_inputs()
    return config, inputs_dict







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= 20
    __UpperCAmelCase:					List[str]		= model_class_name(snake_case_					)

    __UpperCAmelCase:					int		= model.encode(inputs_dict["""input_ids"""]					)

    __UpperCAmelCase, __UpperCAmelCase:					Any		= (
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    __UpperCAmelCase:					Any		= model.init_cache(decoder_input_ids.shape[0]   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					Optional[int]		= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,    dtype="""i4"""					)

    __UpperCAmelCase:					str		= jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]   ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,    )
    __UpperCAmelCase:					Dict		= model.decode(
        decoder_input_ids[:, :-1]   ,    snake_case_   ,    decoder_attention_mask=snake_case_   ,    past_key_values=snake_case_   ,    decoder_position_ids=snake_case_   ,    )

    __UpperCAmelCase:					Optional[int]		= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,    dtype="""i4"""					)
    __UpperCAmelCase:					Any		= model.decode(
        decoder_input_ids[:, -1:]   ,    snake_case_   ,    decoder_attention_mask=snake_case_   ,    past_key_values=outputs_cache.past_key_values   ,    decoder_position_ids=snake_case_   ,    )

    __UpperCAmelCase:					Tuple		= model.decode(snake_case_   ,    snake_case_					)

    __UpperCAmelCase:					Optional[Any]		= 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   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= 20
    __UpperCAmelCase:					Optional[int]		= model_class_name(snake_case_					)

    __UpperCAmelCase:					Optional[Any]		= model.encode(inputs_dict["""input_ids"""]					)

    __UpperCAmelCase, __UpperCAmelCase:					Optional[int]		= (
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )

    __UpperCAmelCase:					List[str]		= jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
        ]   ,    axis=-1   ,    )

    __UpperCAmelCase:					List[Any]		= model.init_cache(decoder_input_ids.shape[0]   ,    snake_case_   ,    snake_case_					)
    __UpperCAmelCase:					List[str]		= jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]   ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,    )

    __UpperCAmelCase:					List[str]		= model.decode(
        decoder_input_ids[:, :-1]   ,    snake_case_   ,    decoder_attention_mask=snake_case_   ,    past_key_values=snake_case_   ,    decoder_position_ids=snake_case_   ,    )
    __UpperCAmelCase:					Optional[Any]		= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,    dtype="""i4"""					)
    __UpperCAmelCase:					Any		= model.decode(
        decoder_input_ids[:, -1:]   ,    snake_case_   ,    past_key_values=outputs_cache.past_key_values   ,    decoder_attention_mask=snake_case_   ,    decoder_position_ids=snake_case_   ,    )

    __UpperCAmelCase:					Tuple		= model.decode(snake_case_   ,    snake_case_   ,    decoder_attention_mask=snake_case_					)

    __UpperCAmelCase:					Optional[Any]		= 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				):



  """simple docstring"""
  __lowerCAmelCase				      =					9_9







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= 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:					int		= input_ids.shape[0]
    __UpperCAmelCase:					Optional[int]		= 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    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					str		= self._get_config_and_data()
    __UpperCAmelCase:					Union[str, Any]		= FlaxBlenderbotSmallForConditionalGeneration(snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= lm_model(input_ids=snake_case_					)
    __UpperCAmelCase:					List[Any]		= (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= 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   ,    )
    __UpperCAmelCase:					str		= FlaxBlenderbotSmallForConditionalGeneration(snake_case_					)
    __UpperCAmelCase:					Dict		= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,    dtype=np.intaa					)
    __UpperCAmelCase:					int		= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,    dtype=np.intaa					)
    __UpperCAmelCase:					List[Any]		= lm_model(input_ids=snake_case_   ,    decoder_input_ids=snake_case_					)
    __UpperCAmelCase:					str		= (*summary.shape, config.vocab_size)
    self.assertEqual(outputs["""logits"""].shape   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







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




@require_flax
class    a (       __lowerCAmelCase						,  unittest.TestCase						,  __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					True
  __lowerCAmelCase				      =					(
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  __lowerCAmelCase				      =					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= FlaxBlenderbotSmallModelTester(self					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					int		= self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(snake_case_   ,    snake_case_   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					Union[str, Any]		= 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(snake_case_   ,    snake_case_   ,    snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					Dict		= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__					):
        __UpperCAmelCase:					Dict		= self._prepare_for_class(snake_case_   ,    snake_case_					)
        __UpperCAmelCase:					Dict		= model_class(snake_case_					)

        @jax.jit
        def encode_jitted(snake_case_   ,    snake_case_=None   ,    **snake_case_					):
          return model.encode(input_ids=snake_case_   ,    attention_mask=snake_case_					)

        with self.subTest("""JIT Enabled"""					):
          __UpperCAmelCase:					Optional[int]		= encode_jitted(**snake_case_					).to_tuple()

        with self.subTest("""JIT Disabled"""					):
          with jax.disable_jit():
            __UpperCAmelCase:					Optional[int]		= encode_jitted(**snake_case_					).to_tuple()

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







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase, __UpperCAmelCase:					Union[str, Any]		= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__					):
        __UpperCAmelCase:					Optional[int]		= model_class(snake_case_					)
        __UpperCAmelCase:					int		= model.encode(inputs_dict["""input_ids"""]   ,    inputs_dict["""attention_mask"""]					)

        __UpperCAmelCase:					Optional[Any]		= {
            """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(snake_case_   ,    snake_case_   ,    snake_case_					):
          return model.decode(
              decoder_input_ids=snake_case_   ,    decoder_attention_mask=snake_case_   ,    encoder_outputs=snake_case_   ,    )

        with self.subTest("""JIT Enabled"""					):
          __UpperCAmelCase:					List[str]		= decode_jitted(**snake_case_					).to_tuple()

        with self.subTest("""JIT Disabled"""					):
          with jax.disable_jit():
            __UpperCAmelCase:					Tuple		= decode_jitted(**snake_case_					).to_tuple()

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







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    for model_class_name in self.all_model_classes:
      __UpperCAmelCase:					List[Any]		= model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""					)
      # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
      __UpperCAmelCase:					Any		= np.ones((1, 1)					) * model.config.eos_token_id
      __UpperCAmelCase:					List[str]		= model(snake_case_					)
      self.assertIsNotNone(snake_case_					)
'''simple docstring'''






from __future__ import annotations

SCREAMING_SNAKE_CASE_	   =			10






def  UpperCamelCase__					(					_lowercase    :				list[int]     )							->	list[int]:
  __UpperCAmelCase:					Union[str, Any]		= 1
  __UpperCAmelCase:					Optional[Any]		= max(_lowercase     )
  while placement <= max_digit:
    # declare and initialize empty buckets
    __UpperCAmelCase:					list[list]		= [[] for _ in range(_lowercase     )]
    # split list_of_ints between the buckets
    for i in list_of_ints:
      __UpperCAmelCase:					Optional[Any]		= int((i / placement) % RADIX     )
      buckets[tmp].append(_lowercase     )
    # put each buckets' contents into list_of_ints
    __UpperCAmelCase:					Optional[int]		= 0
    for b in range(_lowercase     ):
      for i in buckets[b]:
        __UpperCAmelCase:					str		= i
        a += 1
        # move to next
    placement *= RADIX
  return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()