'''simple docstring'''
import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.generation import (
          FlaxForcedBOSTokenLogitsProcessor,
          FlaxForcedEOSTokenLogitsProcessor,
          FlaxLogitsProcessorList,
          FlaxMinLengthLogitsProcessor,
          FlaxTemperatureLogitsWarper,
          FlaxTopKLogitsWarper,
          FlaxTopPLogitsWarper,
      )







@require_flax
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       int  )				->Optional[Any]:
        UpperCAmelCase_            =  jnp.ones((batch_size, length)  ) / length
        return scores






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Tuple:
        UpperCAmelCase_            =  None
        UpperCAmelCase_            =  20

        UpperCAmelCase_            =  self._get_uniform_logits(batch_size=2  ,							length=UpperCAmelCase__  )

        # tweak scores to not be uniform anymore
        UpperCAmelCase_            =  scores.at[1, 5].set((1 / length) + 0.1  )  # peak, 1st batch
        UpperCAmelCase_            =  scores.at[1, 10].set((1 / length) - 0.4  )  # valley, 1st batch

        # compute softmax
        UpperCAmelCase_            =  jax.nn.softmax(UpperCAmelCase__  ,							axis=-1  )

        UpperCAmelCase_            =  FlaxTemperatureLogitsWarper(temperature=0.5  )
        UpperCAmelCase_            =  FlaxTemperatureLogitsWarper(temperature=1.3  )

        UpperCAmelCase_            =  jax.nn.softmax(temp_dist_warper_sharper(UpperCAmelCase__  ,							scores.copy()  ,							cur_len=UpperCAmelCase__  )  ,							axis=-1  )
        UpperCAmelCase_            =  jax.nn.softmax(temp_dist_warper_smoother(UpperCAmelCase__  ,							scores.copy()  ,							cur_len=UpperCAmelCase__  )  ,							axis=-1  )

        # uniform distribution stays uniform
        self.assertTrue(jnp.allclose(probs[0, :]  ,							warped_prob_sharp[0, :]  ,							atol=1e-3  )  )
        self.assertTrue(jnp.allclose(probs[0, :]  ,							warped_prob_smooth[0, :]  ,							atol=1e-3  )  )

        # sharp peaks get higher, valleys get lower
        self.assertLess(probs[1, :].max()  ,							warped_prob_sharp[1, :].max()  )
        self.assertGreater(probs[1, :].min()  ,							warped_prob_sharp[1, :].min()  )

        # smooth peaks get lower, valleys get higher
        self.assertGreater(probs[1, :].max()  ,							warped_prob_smooth[1, :].max()  )
        self.assertLess(probs[1, :].min()  ,							warped_prob_smooth[1, :].min()  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->str:
        UpperCAmelCase_            =  None
        UpperCAmelCase_            =  10
        UpperCAmelCase_            =  2

        # create ramp distribution
        UpperCAmelCase_            =  np.broadcast_to(np.arange(UpperCAmelCase__  )[None, :]  ,							(batch_size, vocab_size)  ).copy()
        UpperCAmelCase_            =  ramp_logits[1:, : vocab_size // 2] + vocab_size

        UpperCAmelCase_            =  FlaxTopKLogitsWarper(3  )

        UpperCAmelCase_            =  top_k_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )

        # check that correct tokens are filtered
        self.assertListEqual(jnp.isinf(scores[0]  ).tolist()  ,							7 * [True] + 3 * [False]  )
        self.assertListEqual(jnp.isinf(scores[1]  ).tolist()  ,							2 * [True] + 3 * [False] + 5 * [True]  )

        # check special case
        UpperCAmelCase_            =  5
        UpperCAmelCase_            =  FlaxTopKLogitsWarper(top_k=1  ,							filter_value=0.0  ,							min_tokens_to_keep=3  )

        UpperCAmelCase_            =  np.broadcast_to(np.arange(UpperCAmelCase__  )[None, :]  ,							(batch_size, length)  ).copy()
        UpperCAmelCase_            =  top_k_warp_safety_check(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )

        # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
        self.assertListEqual((scores == 0.0).sum(axis=-1  ).tolist()  ,							[2, 2]  )






  def  lowerCAmelCase__ (      self			:       Any  )				->Union[str, Any]:
        UpperCAmelCase_            =  None
        UpperCAmelCase_            =  10
        UpperCAmelCase_            =  2

        # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
        UpperCAmelCase_            =  np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]  )  )

        UpperCAmelCase_            =  FlaxTopPLogitsWarper(0.8  )
        UpperCAmelCase_            =  np.exp(top_p_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )  )

        # dist should be filtered to keep min num values so that sum is >= top_p
        # exp (-inf) => 0
        UpperCAmelCase_            =  np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]  )
        self.assertTrue(np.allclose(UpperCAmelCase__  ,							UpperCAmelCase__  ,							atol=1e-3  )  )

        # check edge cases with negative and extreme logits
        UpperCAmelCase_            =  np.broadcast_to(np.arange(UpperCAmelCase__  )[None, :]  ,							(batch_size, vocab_size)  ).copy() - (
            vocab_size // 2
        )

        # make ramp_logits more extreme
        UpperCAmelCase_            =  ramp_logits[1] * 100.0

        # make sure at least 2 tokens are kept
        UpperCAmelCase_            =  FlaxTopPLogitsWarper(0.9  ,							min_tokens_to_keep=2  ,							filter_value=0.0  )
        UpperCAmelCase_            =  top_p_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )

        # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
        self.assertListEqual((filtered_dist != 0.0).sum(axis=-1  ).tolist()  ,							[3, 2]  )






  def  lowerCAmelCase__ (      self			:       Dict  )				->List[Any]:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  4
        UpperCAmelCase_            =  0

        UpperCAmelCase_            =  FlaxMinLengthLogitsProcessor(min_length=10  ,							eos_token_id=UpperCAmelCase__  )

        # check that min length is applied at length 5
        UpperCAmelCase_            =  ids_tensor((batch_size, 20)  ,							vocab_size=20  )
        UpperCAmelCase_            =  5
        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  min_dist_processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()  ,							4 * [-float('''inf'''  )]  )

        # check that min length is not applied anymore at length 15
        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  15
        UpperCAmelCase_            =  min_dist_processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        self.assertFalse(jnp.isinf(UpperCAmelCase__  ).any()  )






  def  lowerCAmelCase__ (      self			:       Any  )				->str:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  4
        UpperCAmelCase_            =  0

        UpperCAmelCase_            =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__  )

        # check that all scores are -inf except the bos_token_id score
        UpperCAmelCase_            =  ids_tensor((batch_size, 1)  ,							vocab_size=20  )
        UpperCAmelCase_            =  1
        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  logits_processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]  ).all()  )
        self.assertListEqual(scores[:, bos_token_id].tolist()  ,							4 * [0]  )  # score for bos_token_id shold be zero

        # check that bos_token_id is not forced if current length is greater than 1
        UpperCAmelCase_            =  3
        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  logits_processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        self.assertFalse(jnp.isinf(UpperCAmelCase__  ).any()  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->Dict:
        UpperCAmelCase_            =  20
        UpperCAmelCase_            =  4
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  5

        UpperCAmelCase_            =  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  )

        # check that all scores are -inf except the eos_token_id when max_length is reached
        UpperCAmelCase_            =  ids_tensor((batch_size, 4)  ,							vocab_size=20  )
        UpperCAmelCase_            =  4
        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  logits_processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]  ).all()  )
        self.assertListEqual(scores[:, eos_token_id].tolist()  ,							4 * [0]  )  # score for eos_token_id should be zero

        # check that eos_token_id is not forced if max_length is not reached
        UpperCAmelCase_            =  3
        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  logits_processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        self.assertFalse(jnp.isinf(UpperCAmelCase__  ).any()  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[int]:
        UpperCAmelCase_            =  4
        UpperCAmelCase_            =  10
        UpperCAmelCase_            =  15
        UpperCAmelCase_            =  2
        UpperCAmelCase_            =  1
        UpperCAmelCase_            =  15

        # dummy input_ids and scores
        UpperCAmelCase_            =  ids_tensor((batch_size, sequence_length)  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  input_ids.copy()

        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  scores.copy()

        # instantiate all dist processors
        UpperCAmelCase_            =  FlaxTemperatureLogitsWarper(temperature=0.5  )
        UpperCAmelCase_            =  FlaxTopKLogitsWarper(3  )
        UpperCAmelCase_            =  FlaxTopPLogitsWarper(0.8  )

        # instantiate all logits processors
        UpperCAmelCase_            =  FlaxMinLengthLogitsProcessor(min_length=10  ,							eos_token_id=UpperCAmelCase__  )
        UpperCAmelCase_            =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__  )
        UpperCAmelCase_            =  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  )

        UpperCAmelCase_            =  10

        # no processor list
        UpperCAmelCase_            =  temp_dist_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        UpperCAmelCase_            =  top_k_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        UpperCAmelCase_            =  top_p_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        UpperCAmelCase_            =  min_dist_proc(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        UpperCAmelCase_            =  bos_dist_proc(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
        UpperCAmelCase_            =  eos_dist_proc(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )

        # with processor list
        UpperCAmelCase_            =  FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]  )
        UpperCAmelCase_            =  processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )

        # scores should be equal
        self.assertTrue(jnp.allclose(UpperCAmelCase__  ,							UpperCAmelCase__  ,							atol=1e-3  )  )

        # input_ids should never be changed
        self.assertListEqual(input_ids.tolist()  ,							input_ids_comp.tolist()  )






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Optional[int]:
        UpperCAmelCase_            =  4
        UpperCAmelCase_            =  10
        UpperCAmelCase_            =  15
        UpperCAmelCase_            =  2
        UpperCAmelCase_            =  1
        UpperCAmelCase_            =  15

        # dummy input_ids and scores
        UpperCAmelCase_            =  ids_tensor((batch_size, sequence_length)  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  input_ids.copy()

        UpperCAmelCase_            =  self._get_uniform_logits(UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  scores.copy()

        # instantiate all dist processors
        UpperCAmelCase_            =  FlaxTemperatureLogitsWarper(temperature=0.5  )
        UpperCAmelCase_            =  FlaxTopKLogitsWarper(3  )
        UpperCAmelCase_            =  FlaxTopPLogitsWarper(0.8  )

        # instantiate all logits processors
        UpperCAmelCase_            =  FlaxMinLengthLogitsProcessor(min_length=10  ,							eos_token_id=UpperCAmelCase__  )
        UpperCAmelCase_            =  FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCAmelCase__  )
        UpperCAmelCase_            =  FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  )

        UpperCAmelCase_            =  10

        # no processor list
        def run_no_processor_list(UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       List[Any]  ):
              UpperCAmelCase_            =  temp_dist_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              UpperCAmelCase_            =  top_k_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              UpperCAmelCase_            =  top_p_warp(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              UpperCAmelCase_            =  min_dist_proc(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              UpperCAmelCase_            =  bos_dist_proc(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              UpperCAmelCase_            =  eos_dist_proc(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              return scores

        # with processor list
        def run_processor_list(UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple  ):
              UpperCAmelCase_            =  FlaxLogitsProcessorList(
                  [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]  )
              UpperCAmelCase_            =  processor(UpperCAmelCase__  ,							UpperCAmelCase__  ,							cur_len=UpperCAmelCase__  )
              return scores

        UpperCAmelCase_            =  jax.jit(UpperCAmelCase__  )
        UpperCAmelCase_            =  jax.jit(UpperCAmelCase__  )

        UpperCAmelCase_            =  jitted_run_no_processor_list(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )
        UpperCAmelCase_            =  jitted_run_processor_list(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )

        # scores should be equal
        self.assertTrue(jnp.allclose(UpperCAmelCase__  ,							UpperCAmelCase__  ,							atol=1e-3  )  )

        # input_ids should never be changed
        self.assertListEqual(input_ids.tolist()  ,							input_ids_comp.tolist()  )






'''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





