text: # coding=utf-8
  # Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """ Flax MBart model. """
  
  import math
  import random
  from functools import partial
  from typing import Callable, Optional, Tuple
  
  import numpy as np
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from flax.core.frozen_dict import FrozenDict, unfreeze
  from flax.linen import combine_masks, make_causal_mask
  from flax.linen.attention import dot_product_attention_weights
  from jax import lax
  from jax.random import PRNGKey
  
  from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
  from ...modeling_flax_outputs import (
      FlaxBaseModelOutput,
      FlaxBaseModelOutputWithPastAndCrossAttentions,
      FlaxCausalLMOutputWithCrossAttentions,
      FlaxSeq2SeqLMOutput,
      FlaxSeq2SeqModelOutput,
      FlaxSeq2SeqQuestionAnsweringModelOutput,
      FlaxSeq2SeqSequenceClassifierOutput,
  )
  from ...modeling_flax_utils import (
      ACT2FN,
      FlaxPreTrainedModel,
      append_call_sample_docstring,
      append_replace_return_docstrings,
      overwrite_call_docstring,
  )
  from ...utils import logging
  from .configuration_mbart import MBartConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "facebook/mbart-large-cc25"
  _CONFIG_FOR_DOC = "MBartConfig"
  _TOKENIZER_FOR_DOC = "MBartTokenizer"
  
  
  MBART_START_DOCSTRING = r"""
      This model inherits from :class:`~transformers.FlaxPreTrainedModel`. Check the superclass documentation for the
      generic methods the library implements for all its model (such as downloading or saving, resizing the input
      embeddings, pruning heads etc.)
  
      This model is also a Flax Linen `flax.nn.Module
      <https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html>`__ subclass. Use it as a regular Flax
      Module and refer to the Flax documentation for all matter related to general usage and behavior.
  
      Finally, this model supports inherent JAX features such as:
  
      - `Just-In-Time (JIT) compilation <https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit>`__
      - `Automatic Differentiation <https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation>`__
      - `Vectorization <https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap>`__
      - `Parallelization <https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap>`__
  
      Parameters:
          config (:class:`~transformers.MBartConfig`): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration. Check out the :meth:`~transformers.FlaxPreTrainedModel.from_pretrained` method to load the
              model weights.
          dtype (:obj:`jax.numpy.dtype`, `optional`, defaults to :obj:`jax.numpy.float32`):
              The data type of the computation. Can be one of :obj:`jax.numpy.float32`, :obj:`jax.numpy.float16` (on
              GPUs) and :obj:`jax.numpy.bfloat16` (on TPUs).
  
              This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
              specified all the computation will be performed with the given ``dtype``.
  
              **Note that this only specifies the dtype of the computation and does not influence the dtype of model
              parameters.**
  
              If you wish to change the dtype of the model parameters, see
              :meth:`~transformers.FlaxPreTrainedModel.to_fp16` and :meth:`~transformers.FlaxPreTrainedModel.to_bf16`.
  """
  
  MBART_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
              it.
  
              Indices can be obtained using :class:`~transformers.MBartTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          decoder_input_ids (:obj:`jnp.ndarray` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Indices of decoder input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.MBartTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are decoder input IDs? <../glossary.html#decoder-input-ids>`__
  
              For translation and summarization training, :obj:`decoder_input_ids` should be provided. If no
              :obj:`decoder_input_ids` is provided, the model will create this tensor by shifting the :obj:`input_ids` to
              the right for denoising pre-training following the paper.
          decoder_attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
              also be used by default.
  
              If you want to change padding behavior, you should modify to your needs. See diagram 1 in `the paper
              <https://arxiv.org/abs/1910.13461>`__ for more information on the default strategy.
          position_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
              config.max_position_embeddings - 1]``.
          decoder_position_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
              range ``[0, config.max_position_embeddings - 1]``.
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  MBART_ENCODE_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
              it.
  
              Indices can be obtained using :class:`~transformers.MBartTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are input IDs? <../glossary.html#input-ids>`__
          attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          position_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
              config.max_position_embeddings - 1]``.
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  MBART_DECODE_INPUTS_DOCSTRING = r"""
      Args:
          decoder_input_ids (:obj:`jnp.ndarray` of shape :obj:`(batch_size, target_sequence_length)`):
              Indices of decoder input sequence tokens in the vocabulary.
  
              Indices can be obtained using :class:`~transformers.MBartTokenizer`. See
              :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
              details.
  
              `What are decoder input IDs? <../glossary.html#decoder-input-ids>`__
  
              For translation and summarization training, :obj:`decoder_input_ids` should be provided. If no
              :obj:`decoder_input_ids` is provided, the model will create this tensor by shifting the :obj:`input_ids` to
              the right for denoising pre-training following the paper.
          encoder_outputs (:obj:`tuple(tuple(jnp.ndarray)`):
              Tuple consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`, `optional`:
              :obj:`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`,
              `optional`) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
              cross-attention of the decoder.
          encoder_attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              `What are attention masks? <../glossary.html#attention-mask>`__
          decoder_attention_mask (:obj:`jnp.ndarray` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
              Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
              also be used by default.
  
              If you want to change padding behavior, you should modify to your needs. See diagram 1 in `the paper
              <https://arxiv.org/abs/1910.13461>`__ for more information on the default strategy.
          decoder_position_ids (:obj:`numpy.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
              Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
              range ``[0, config.max_position_embeddings - 1]``.
          past_key_values (:obj:`Dict[str, np.ndarray]`, `optional`, returned by ``init_cache`` or when passing previous ``past_key_values``):
              Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
              auto-regressive decoding. Pre-computed key and value hidden-states are of shape `[batch_size, max_length]`.
          output_attentions (:obj:`bool`, `optional`):
              Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
              tensors for more detail.
          output_hidden_states (:obj:`bool`, `optional`):
              Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
              more detail.
          return_dict (:obj:`bool`, `optional`):
              Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
  """
  
  
  def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int) -> jnp.ndarray:
      """
      Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
      have a single `decoder_start_token_id` in contrast to other Bart-like models.
      """
      prev_output_tokens = np.array(input_ids).copy()
  
      assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
  
      # replace possible -100 values in labels by `pad_token_id`
      prev_output_tokens = np.where(prev_output_tokens == -100, pad_token_id, input_ids)
      index_of_eos = (np.where(prev_output_tokens != pad_token_id, 1, 0).sum(axis=-1) - 1).reshape(-1, 1)
      decoder_start_tokens = np.array(
          [prev_output_tokens[i, eos_idx] for i, eos_idx in enumerate(index_of_eos)], dtype=np.int32
      ).squeeze()
  
      prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].copy()
      prev_output_tokens[:, 0] = decoder_start_tokens
  
      return prev_output_tokens
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->MBart
  class FlaxMBartAttention(nn.Module):
      config: MBartConfig
      embed_dim: int
      num_heads: int
      dropout: float = 0.0
      causal: bool = False
      bias: bool = True
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self) -> None:
          self.head_dim = self.embed_dim // self.num_heads
          if self.head_dim * self.num_heads != self.embed_dim:
              raise ValueError(
                  f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                  f" and `num_heads`: {self.num_heads})."
              )
  
          dense = partial(
              nn.Dense,
              self.embed_dim,
              use_bias=self.bias,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
          self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
          self.out_proj = dense()
  
          self.dropout_layer = nn.Dropout(rate=self.dropout)
  
          if self.causal:
              self.causal_mask = make_causal_mask(
                  jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
              )
  
      def _split_heads(self, hidden_states):
          return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
  
      def _merge_heads(self, hidden_states):
          return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
  
      @nn.compact
      def _concatenate_to_cache(self, key, value, query, attention_mask):
          """
          This function takes projected key, value states from a single input token and concatenates the states to cached
          states from previous steps. This function is slighly adapted from the official Flax repository:
          https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
          """
          # detect if we're initializing by absence of existing cache data.
          is_initialized = self.has_variable("cache", "cached_key")
          cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
          cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
          cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
  
          if is_initialized:
              *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
              # update key, value caches with our new 1d spatial slices
              cur_index = cache_index.value
              indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
              key = lax.dynamic_update_slice(cached_key.value, key, indices)
              value = lax.dynamic_update_slice(cached_value.value, value, indices)
              cached_key.value = key
              cached_value.value = value
              num_updated_cache_vectors = query.shape[1]
              cache_index.value = cache_index.value + num_updated_cache_vectors
              # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
              pad_mask = jnp.broadcast_to(
                  jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                  tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
              )
              attention_mask = combine_masks(pad_mask, attention_mask)
          return key, value, attention_mask
  
      def __call__(
          self,
          hidden_states: jnp.ndarray,
          key_value_states: Optional[jnp.ndarray] = None,
          attention_mask: Optional[jnp.ndarray] = None,
          init_cache: bool = False,
          deterministic: bool = True,
      ) -> Tuple[jnp.ndarray]:
          """Input shape: Batch x Time x Channel"""
  
          # if key_value_states are provided this layer is used as a cross-attention layer
          # for the decoder
          is_cross_attention = key_value_states is not None
          batch_size = hidden_states.shape[0]
  
          # get query proj
          query_states = self.q_proj(hidden_states)
          # get key, value proj
          if is_cross_attention:
              # cross_attentions
              key_states = self.k_proj(key_value_states)
              value_states = self.v_proj(key_value_states)
          else:
              # self_attention
              key_states = self.k_proj(hidden_states)
              value_states = self.v_proj(hidden_states)
  
          query_states = self._split_heads(query_states)
          key_states = self._split_heads(key_states)
          value_states = self._split_heads(value_states)
  
          # handle cache prepare causal attention mask
          if self.causal:
              query_length, key_length = query_states.shape[1], key_states.shape[1]
              if self.has_variable("cache", "cached_key"):
                  mask_shift = self.variables["cache"]["cache_index"]
                  max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                  causal_mask = lax.dynamic_slice(
                      self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                  )
              else:
                  causal_mask = self.causal_mask[:, :, :query_length, :key_length]
              causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
  
          # combine masks if needed
          if attention_mask is not None and self.causal:
              attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
              attention_mask = combine_masks(attention_mask, causal_mask)
          elif self.causal:
              attention_mask = causal_mask
          elif attention_mask is not None:
              attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
  
          # During fast autoregressive decoding, we feed one position at a time,
          # and cache the keys and values step by step.
          if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
              key_states, value_states, attention_mask = self._concatenate_to_cache(
                  key_states, value_states, query_states, attention_mask
              )
  
          # Convert the boolean attention mask to an attention bias.
          if attention_mask is not None:
              # attention mask in the form of attention bias
              attention_bias = lax.select(
                  attention_mask > 0,
                  jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                  jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype),
              )
          else:
              attention_bias = None
  
          dropout_rng = None
          if not deterministic and self.dropout > 0.0:
              dropout_rng = self.make_rng("dropout")
  
          attn_weights = dot_product_attention_weights(
              query_states,
              key_states,
              bias=attention_bias,
              dropout_rng=dropout_rng,
              dropout_rate=self.dropout,
              broadcast_dropout=True,
              deterministic=deterministic,
              dtype=self.dtype,
              precision=None,
          )
  
          attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
          attn_output = self._merge_heads(attn_output)
          attn_output = self.out_proj(attn_output)
  
          return attn_output, attn_weights
  
  
  class FlaxMBartEncoderLayer(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self) -> None:
          self.embed_dim = self.config.d_model
          self.self_attn = FlaxMBartAttention(
              config=self.config,
              embed_dim=self.embed_dim,
              num_heads=self.config.encoder_attention_heads,
              dropout=self.config.attention_dropout,
              dtype=self.dtype,
          )
          self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
          self.activation_fn = ACT2FN[self.config.activation_function]
          self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
          self.fc1 = nn.Dense(
              self.config.encoder_ffn_dim,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.init_std),
          )
          self.fc2 = nn.Dense(
              self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
          )
          self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
  
      def __call__(
          self,
          hidden_states: jnp.ndarray,
          attention_mask: jnp.ndarray,
          output_attentions: bool = True,
          deterministic: bool = True,
      ) -> Tuple[jnp.ndarray]:
          residual = hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
          hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask)
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = residual + hidden_states
  
          residual = hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = residual + hidden_states
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (attn_weights,)
  
          return outputs
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->MBart
  class FlaxMBartEncoderLayerCollection(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layers = [
              FlaxMBartEncoderLayer(self.config, name=str(i), dtype=self.dtype)
              for i in range(self.config.encoder_layers)
          ]
          self.layerdrop = self.config.encoder_layerdrop
  
      def __call__(
          self,
          hidden_states,
          attention_mask,
          deterministic: bool = True,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
      ):
          all_attentions = () if output_attentions else None
          all_hidden_states = () if output_hidden_states else None
  
          for encoder_layer in self.layers:
              if output_hidden_states:
                  all_hidden_states = all_hidden_states + (hidden_states,)
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = random.uniform(0, 1)
              if not deterministic and (dropout_probability < self.layerdrop):  # skip the layer
                  layer_outputs = (None, None)
              else:
                  layer_outputs = encoder_layer(
                      hidden_states,
                      attention_mask,
                      output_attentions,
                      deterministic,
                  )
              hidden_states = layer_outputs[0]
              if output_attentions:
                  all_attentions = all_attentions + (layer_outputs[1],)
  
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          outputs = (hidden_states, all_hidden_states, all_attentions)
  
          if not return_dict:
              return tuple(v for v in outputs if v is not None)
  
          return FlaxBaseModelOutput(
              last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
          )
  
  
  class FlaxMBartDecoderLayer(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self) -> None:
          self.embed_dim = self.config.d_model
          self.self_attn = FlaxMBartAttention(
              config=self.config,
              embed_dim=self.embed_dim,
              num_heads=self.config.decoder_attention_heads,
              dropout=self.config.attention_dropout,
              causal=True,
              dtype=self.dtype,
          )
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
          self.activation_fn = ACT2FN[self.config.activation_function]
          self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
  
          self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          self.encoder_attn = FlaxMBartAttention(
              config=self.config,
              embed_dim=self.embed_dim,
              num_heads=self.config.decoder_attention_heads,
              dropout=self.config.attention_dropout,
              dtype=self.dtype,
          )
          self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          self.fc1 = nn.Dense(
              self.config.encoder_ffn_dim,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.init_std),
          )
          self.fc2 = nn.Dense(
              self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
          )
          self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
  
      def __call__(
          self,
          hidden_states: jnp.ndarray,
          attention_mask: jnp.ndarray,
          encoder_hidden_states: Optional[jnp.ndarray] = None,
          encoder_attention_mask: Optional[jnp.ndarray] = None,
          init_cache: bool = False,
          output_attentions: bool = True,
          deterministic: bool = True,
      ) -> Tuple[jnp.ndarray]:
          residual = hidden_states
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Self Attention
          hidden_states, self_attn_weights = self.self_attn(
              hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
          )
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = residual + hidden_states
  
          # Cross-Attention Block
          cross_attn_weights = None
          if encoder_hidden_states is not None:
              residual = hidden_states
  
              hidden_states = self.encoder_attn_layer_norm(hidden_states)
              hidden_states, cross_attn_weights = self.encoder_attn(
                  hidden_states=hidden_states,
                  key_value_states=encoder_hidden_states,
                  attention_mask=encoder_attention_mask,
              )
              hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
              hidden_states = residual + hidden_states
  
          # Fully Connected
          residual = hidden_states
          hidden_states = self.final_layer_norm(hidden_states)
          hidden_states = self.activation_fn(self.fc1(hidden_states))
          hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = residual + hidden_states
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (self_attn_weights, cross_attn_weights)
  
          return outputs
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->MBart
  class FlaxMBartDecoderLayerCollection(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layers = [
              FlaxMBartDecoderLayer(self.config, name=str(i), dtype=self.dtype)
              for i in range(self.config.decoder_layers)
          ]
          self.layerdrop = self.config.decoder_layerdrop
  
      def __call__(
          self,
          hidden_states,
          attention_mask,
          encoder_hidden_states: Optional[jnp.ndarray] = None,
          encoder_attention_mask: Optional[jnp.ndarray] = None,
          deterministic: bool = True,
          init_cache: bool = False,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
      ):
          # decoder layers
          all_hidden_states = () if output_hidden_states else None
          all_self_attns = () if output_attentions else None
          all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
  
          for decoder_layer in self.layers:
              if output_hidden_states:
                  all_hidden_states += (hidden_states,)
                  # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
              dropout_probability = random.uniform(0, 1)
              if not deterministic and (dropout_probability < self.layerdrop):
                  layer_outputs = (None, None, None)
              else:
                  layer_outputs = decoder_layer(
                      hidden_states,
                      attention_mask=attention_mask,
                      encoder_hidden_states=encoder_hidden_states,
                      encoder_attention_mask=encoder_attention_mask,
                      init_cache=init_cache,
                      output_attentions=output_attentions,
                      deterministic=deterministic,
                  )
  
              hidden_states = layer_outputs[0]
              if output_attentions:
                  all_self_attns += (layer_outputs[1],)
  
                  if encoder_hidden_states is not None:
                      all_cross_attentions += (layer_outputs[2],)
  
          # add hidden states from the last decoder layer
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
  
          outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
  
          if not return_dict:
              return tuple(v for v in outputs if v is not None)
  
          return FlaxBaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=hidden_states,
              hidden_states=all_hidden_states,
              attentions=all_self_attns,
              cross_attentions=all_cross_attentions,
          )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartClassificationHead with Bart->MBart
  class FlaxMBartClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      config: MBartConfig
      inner_dim: int
      num_classes: int
      pooler_dropout: float
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.dense = nn.Dense(
              self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
          )
          self.dropout = nn.Dropout(rate=self.pooler_dropout)
          self.out_proj = nn.Dense(
              self.num_classes,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
      def __call__(self, hidden_states: jnp.ndarray, deterministic: bool):
          hidden_states = self.dropout(hidden_states, deterministic=deterministic)
          hidden_states = self.dense(hidden_states)
          hidden_states = jnp.tanh(hidden_states)
          hidden_states = self.dropout(hidden_states, deterministic=deterministic)
          hidden_states = self.out_proj(hidden_states)
          return hidden_states
  
  
  class FlaxMBartEncoder(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      embed_tokens: Optional[nn.Embed] = None
  
      def setup(self):
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
  
          embed_dim = self.config.d_model
          self.padding_idx = self.config.pad_token_id
          self.max_source_positions = self.config.max_position_embeddings
          self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
  
          if self.embed_tokens is None:
              self.embed_tokens = nn.Embed(
                  self.config.vocab_size,
                  embed_dim,
                  embedding_init=jax.nn.initializers.normal(self.config.init_std),
              )
  
          # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
          # and adjust num_embeddings appropriately. Other models don't have this hack
          self.offset = 2
          self.embed_positions = nn.Embed(
              self.config.max_position_embeddings + self.offset,
              embed_dim,
              embedding_init=jax.nn.initializers.normal(self.config.init_std),
          )
          self.layers = FlaxMBartEncoderLayerCollection(self.config, self.dtype)
          self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          position_ids,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
          input_shape = input_ids.shape
          input_ids = input_ids.reshape(-1, input_shape[-1])
  
          inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
          embed_pos = self.embed_positions(position_ids + self.offset)
  
          hidden_states = inputs_embeds + embed_pos
          hidden_states = self.layernorm_embedding(hidden_states)
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
  
          outputs = self.layers(
              hidden_states,
              attention_mask,
              deterministic=deterministic,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          last_hidden_states = outputs[0]
          last_hidden_states = self.layer_norm(last_hidden_states)
  
          if not return_dict:
              return (last_hidden_states,) + outputs[1:]
  
          return FlaxBaseModelOutput(
              last_hidden_state=last_hidden_states,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  class FlaxMBartDecoder(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      embed_tokens: Optional[nn.Embed] = None
  
      def setup(self):
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
  
          embed_dim = self.config.d_model
          self.padding_idx = self.config.pad_token_id
          self.max_target_positions = self.config.max_position_embeddings
          self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
  
          if self.embed_tokens is None:
              self.embed_tokens = nn.Embed(
                  self.config.vocab_size,
                  embed_dim,
                  embedding_init=jax.nn.initializers.normal(self.config.init_std),
              )
  
          # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2
          # and adjust num_embeddings appropriately. Other models don't have this hack
          self.offset = 2
          self.embed_positions = nn.Embed(
              self.config.max_position_embeddings + self.offset,
              embed_dim,
              embedding_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
          self.layers = FlaxMBartDecoderLayerCollection(self.config, self.dtype)
          self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          position_ids,
          encoder_hidden_states: Optional[jnp.ndarray] = None,
          encoder_attention_mask: Optional[jnp.ndarray] = None,
          init_cache: bool = False,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
          input_shape = input_ids.shape
          input_ids = input_ids.reshape(-1, input_shape[-1])
  
          inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
          # embed positions
          positions = self.embed_positions(position_ids + self.offset)
  
          hidden_states = inputs_embeds + positions
          hidden_states = self.layernorm_embedding(hidden_states)
  
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
  
          outputs = self.layers(
              hidden_states,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
              deterministic=deterministic,
              init_cache=init_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
          )
  
          last_hidden_states = outputs[0]
          last_hidden_states = self.layer_norm(last_hidden_states)
  
          if not return_dict:
              return (last_hidden_states,) + outputs[1:]
  
          return FlaxBaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=last_hidden_states,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
              cross_attentions=outputs.cross_attentions,
          )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->MBart
  class FlaxMBartModule(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.shared = nn.Embed(
              self.config.vocab_size,
              self.config.d_model,
              embedding_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
          self.encoder = FlaxMBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
          self.decoder = FlaxMBartDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
  
      def _get_encoder_module(self):
          return self.encoder
  
      def _get_decoder_module(self):
          return self.decoder
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          decoder_input_ids,
          decoder_attention_mask,
          position_ids,
          decoder_position_ids,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
          encoder_outputs = self.encoder(
              input_ids=input_ids,
              attention_mask=attention_mask,
              position_ids=position_ids,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=deterministic,
          )
  
          decoder_outputs = self.decoder(
              input_ids=decoder_input_ids,
              attention_mask=decoder_attention_mask,
              position_ids=decoder_position_ids,
              encoder_hidden_states=encoder_outputs[0],
              encoder_attention_mask=attention_mask,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=deterministic,
          )
  
          if not return_dict:
              return decoder_outputs + encoder_outputs
  
          return FlaxSeq2SeqModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              decoder_hidden_states=decoder_outputs.hidden_states,
              decoder_attentions=decoder_outputs.attentions,
              cross_attentions=decoder_outputs.cross_attentions,
              encoder_last_hidden_state=encoder_outputs.last_hidden_state,
              encoder_hidden_states=encoder_outputs.hidden_states,
              encoder_attentions=encoder_outputs.attentions,
          )
  
  
  class FlaxMBartPreTrainedModel(FlaxPreTrainedModel):
      config_class = MBartConfig
      base_model_prefix: str = "model"
      module_class: nn.Module = None
  
      def __init__(
          self,
          config: MBartConfig,
          input_shape: Tuple[int] = (1, 1),
          seed: int = 0,
          dtype: jnp.dtype = jnp.float32,
          **kwargs
      ):
          module = self.module_class(config=config, dtype=dtype, **kwargs)
          super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype)
  
      def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
          # init input tensors
          input_ids = jnp.zeros(input_shape, dtype="i4")
          # make sure initialization pass will work for FlaxMBartForSequenceClassificationModule
          input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
          attention_mask = jnp.ones_like(input_ids)
          decoder_input_ids = input_ids
          decoder_attention_mask = jnp.ones_like(input_ids)
  
          batch_size, sequence_length = input_ids.shape
          position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
          decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
  
          params_rng, dropout_rng = jax.random.split(rng)
          rngs = {"params": params_rng, "dropout": dropout_rng}
  
          return self.module.init(
              rngs,
              input_ids,
              attention_mask,
              decoder_input_ids,
              decoder_attention_mask,
              position_ids,
              decoder_position_ids,
          )["params"]
  
      # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->MBart
      def init_cache(self, batch_size, max_length, encoder_outputs):
          r"""
          Args:
              batch_size (:obj:`int`):
                  batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
              max_length (:obj:`int`):
                  maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                  cache.
              encoder_outputs (:obj:`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
                  ``encoder_outputs`` consists of (:obj:`last_hidden_state`, `optional`: :obj:`hidden_states`,
                  `optional`: :obj:`attentions`). :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length,
                  hidden_size)`, `optional`) is a sequence of hidden-states at the output of the last layer of the
                  encoder. Used in the cross-attention of the decoder.
          """
          # init input variables to retrieve cache
          decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
          decoder_attention_mask = jnp.ones_like(decoder_input_ids)
          decoder_position_ids = jnp.broadcast_to(
              jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape
          )
  
          def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
              decoder_module = module._get_decoder_module()
              return decoder_module(
                  decoder_input_ids,
                  decoder_attention_mask,
                  decoder_position_ids,
                  **kwargs,
              )
  
          init_variables = self.module.init(
              jax.random.PRNGKey(0),
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              decoder_position_ids=decoder_position_ids,
              encoder_hidden_states=encoder_outputs[0],
              init_cache=True,
              method=_decoder_forward,  # we only need to call the decoder to init the cache
          )
          return unfreeze(init_variables["cache"])
  
      @add_start_docstrings(MBART_ENCODE_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MBartConfig)
      def encode(
          self,
          input_ids: jnp.ndarray,
          attention_mask: Optional[jnp.ndarray] = None,
          position_ids: Optional[jnp.ndarray] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          train: bool = False,
          params: dict = None,
          dropout_rng: PRNGKey = None,
      ):
          r"""
          Returns:
  
          Example::
  
              >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
  
              >>> model = FlaxMBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
              >>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
  
              >>> text = "My friends are cool but they eat too many carbs."
              >>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
              >>> encoder_outputs = model.encode(**inputs)
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.return_dict
  
          if attention_mask is None:
              attention_mask = jnp.ones_like(input_ids)
          if position_ids is None:
              batch_size, sequence_length = input_ids.shape
              position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
  
          # Handle any PRNG if needed
          rngs = {}
          if dropout_rng is not None:
              rngs["dropout"] = dropout_rng
  
          def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs):
              encode_module = module._get_encoder_module()
              return encode_module(input_ids, attention_mask, position_ids, **kwargs)
  
          return self.module.apply(
              {"params": params or self.params},
              input_ids=jnp.array(input_ids, dtype="i4"),
              attention_mask=jnp.array(attention_mask, dtype="i4"),
              position_ids=jnp.array(position_ids, dtype="i4"),
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=not train,
              rngs=rngs,
              method=_encoder_forward,
          )
  
      @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MBartConfig)
      def decode(
          self,
          decoder_input_ids,
          encoder_outputs,
          encoder_attention_mask: Optional[jnp.ndarray] = None,
          decoder_attention_mask: Optional[jnp.ndarray] = None,
          decoder_position_ids: Optional[jnp.ndarray] = None,
          past_key_values: dict = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          train: bool = False,
          params: dict = None,
          dropout_rng: PRNGKey = None,
      ):
          r"""
          Returns:
  
          Example::
  
              >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
  
              >>> model = FlaxMBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
              >>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
  
              >>> text = "My friends are cool but they eat too many carbs."
              >>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
              >>> encoder_outputs = model.encode(**inputs)
  
              >>> decoder_start_token_id = model.config.decoder_start_token_id
              >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
  
              >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
              >>> last_decoder_hidden_states = outputs.last_hidden_state
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.return_dict
  
          encoder_hidden_states = encoder_outputs[0]
          if encoder_attention_mask is None:
              batch_size, sequence_length = encoder_hidden_states.shape[:2]
              encoder_attention_mask = jnp.ones((batch_size, sequence_length))
  
          batch_size, sequence_length = decoder_input_ids.shape
          if decoder_attention_mask is None:
              decoder_attention_mask = jnp.ones((batch_size, sequence_length))
  
          if decoder_position_ids is None:
              if past_key_values is not None:
                  raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
  
              decoder_position_ids = jnp.broadcast_to(
                  jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
              )
  
          # Handle any PRNG if needed
          rngs = {}
          if dropout_rng is not None:
              rngs["dropout"] = dropout_rng
  
          inputs = {"params": params or self.params}
  
          # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
          # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
          # it can be changed by FlaxMBartAttention module
          if past_key_values:
              inputs["cache"] = past_key_values
              mutable = ["cache"]
          else:
              mutable = False
  
          def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
              decoder_module = module._get_decoder_module()
              return decoder_module(
                  decoder_input_ids,
                  decoder_attention_mask,
                  decoder_position_ids,
                  **kwargs,
              )
  
          outputs = self.module.apply(
              inputs,
              decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
              decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
              decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=not train,
              rngs=rngs,
              mutable=mutable,
              method=_decoder_forward,
          )
  
          # add updated cache to model output
          if past_key_values is not None and return_dict:
              outputs, past = outputs
              outputs["past_key_values"] = unfreeze(past["cache"])
              return outputs
          elif past_key_values is not None and not return_dict:
              outputs, past = outputs
              outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
  
          return outputs
  
      @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING)
      def __call__(
          self,
          input_ids: jnp.ndarray,
          attention_mask: Optional[jnp.ndarray] = None,
          decoder_input_ids: Optional[jnp.ndarray] = None,
          decoder_attention_mask: Optional[jnp.ndarray] = None,
          position_ids: Optional[jnp.ndarray] = None,
          decoder_position_ids: Optional[jnp.ndarray] = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          train: bool = False,
          params: dict = None,
          dropout_rng: PRNGKey = None,
      ):
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.return_dict
  
          # prepare encoder inputs
          if attention_mask is None:
              attention_mask = jnp.ones_like(input_ids)
          if position_ids is None:
              batch_size, sequence_length = input_ids.shape
              position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
  
          # prepare decoder inputs
          if decoder_input_ids is None:
              decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id)
          if decoder_attention_mask is None:
              decoder_attention_mask = jnp.ones_like(decoder_input_ids)
          if decoder_position_ids is None:
              batch_size, sequence_length = decoder_input_ids.shape
              decoder_position_ids = jnp.broadcast_to(
                  jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
              )
  
          # Handle any PRNG if needed
          rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
  
          return self.module.apply(
              {"params": params or self.params},
              input_ids=jnp.array(input_ids, dtype="i4"),
              attention_mask=jnp.array(attention_mask, dtype="i4"),
              position_ids=jnp.array(position_ids, dtype="i4"),
              decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
              decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
              decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=not train,
              rngs=rngs,
          )
  
  
  @add_start_docstrings(
      "The bare MBart Model transformer outputting raw hidden-states without any specific head on top.",
      MBART_START_DOCSTRING,
  )
  class FlaxMBartModel(FlaxMBartPreTrainedModel):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      module_class = FlaxMBartModule
  
  
  append_call_sample_docstring(
      FlaxMBartModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC
  )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->MBart
  class FlaxMBartForConditionalGenerationModule(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32
      bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
  
      def setup(self):
          self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
          self.lm_head = nn.Dense(
              self.model.shared.num_embeddings,
              use_bias=False,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.init_std),
          )
          self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings))
  
      def _get_encoder_module(self):
          return self.model.encoder
  
      def _get_decoder_module(self):
          return self.model.decoder
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          decoder_input_ids,
          decoder_attention_mask,
          position_ids,
          decoder_position_ids,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
          outputs = self.model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              position_ids=position_ids,
              decoder_position_ids=decoder_position_ids,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=deterministic,
          )
  
          hidden_states = outputs[0]
  
          if self.config.tie_word_embeddings:
              shared_embedding = self.model.variables["params"]["shared"]["embedding"]
              lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
          else:
              lm_logits = self.lm_head(hidden_states)
  
          lm_logits += self.final_logits_bias.astype(self.dtype)
  
          if not return_dict:
              output = (lm_logits,) + outputs[1:]
              return output
  
          return FlaxSeq2SeqLMOutput(
              logits=lm_logits,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
  
  @add_start_docstrings(
      "The MMBart Model with a language modeling head. Can be used for summarization.", MBART_START_DOCSTRING
  )
  class FlaxMBartForConditionalGeneration(FlaxMBartPreTrainedModel):
      module_class = FlaxMBartForConditionalGenerationModule
      dtype: jnp.dtype = jnp.float32
  
      @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MBartConfig)
      def decode(
          self,
          decoder_input_ids,
          encoder_outputs,
          encoder_attention_mask: Optional[jnp.ndarray] = None,
          decoder_attention_mask: Optional[jnp.ndarray] = None,
          decoder_position_ids: Optional[jnp.ndarray] = None,
          past_key_values: dict = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          train: bool = False,
          params: dict = None,
          dropout_rng: PRNGKey = None,
      ):
          r"""
          Returns:
  
          Example::
  
              >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
  
              >>> model = FlaxMBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
              >>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
  
              >>> text = "My friends are cool but they eat too many carbs."
              >>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
              >>> encoder_outputs = model.encode(**inputs)
  
              >>> decoder_start_token_id = model.config.decoder_start_token_id
              >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
  
              >>> outputs = model.decode(decoder_input_ids, encoder_outputs)
              >>> logits = outputs.logits
          """
          output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
          output_hidden_states = (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          return_dict = return_dict if return_dict is not None else self.config.return_dict
  
          encoder_hidden_states = encoder_outputs[0]
          if encoder_attention_mask is None:
              batch_size, sequence_length = encoder_hidden_states.shape[:2]
              encoder_attention_mask = jnp.ones((batch_size, sequence_length))
  
          batch_size, sequence_length = decoder_input_ids.shape
          if decoder_attention_mask is None:
              decoder_attention_mask = jnp.ones((batch_size, sequence_length))
  
          if decoder_position_ids is None:
              if past_key_values is not None:
                  raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
  
              decoder_position_ids = jnp.broadcast_to(
                  jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
              )
  
          # Handle any PRNG if needed
          rngs = {}
          if dropout_rng is not None:
              rngs["dropout"] = dropout_rng
  
          inputs = {"params": params or self.params}
  
          # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
          # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
          # it can be changed by FlaxMBartAttention module
          if past_key_values:
              inputs["cache"] = past_key_values
              mutable = ["cache"]
          else:
              mutable = False
  
          def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
              decoder_module = module._get_decoder_module()
              outputs = decoder_module(
                  decoder_input_ids,
                  decoder_attention_mask,
                  decoder_position_ids,
                  **kwargs,
              )
              hidden_states = outputs[0]
  
              if self.config.tie_word_embeddings:
                  shared_embedding = module.model.variables["params"]["shared"]["embedding"]
                  lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
              else:
                  lm_logits = module.lm_head(hidden_states)
  
              lm_logits += module.final_logits_bias.astype(self.dtype)
              return lm_logits, outputs
  
          outputs = self.module.apply(
              inputs,
              decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
              decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
              decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
              encoder_hidden_states=encoder_hidden_states,
              encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=not train,
              rngs=rngs,
              mutable=mutable,
              method=_decoder_forward,
          )
  
          if past_key_values is None:
              lm_logits, decoder_outputs = outputs
          else:
              (lm_logits, decoder_outputs), past = outputs
  
          if return_dict:
              outputs = FlaxCausalLMOutputWithCrossAttentions(
                  logits=lm_logits,
                  hidden_states=decoder_outputs.hidden_states,
                  attentions=decoder_outputs.attentions,
                  cross_attentions=decoder_outputs.cross_attentions,
              )
          else:
              outputs = (lm_logits,) + decoder_outputs[1:]
  
          # add updated cache to model output
          if past_key_values is not None and return_dict:
              outputs["past_key_values"] = unfreeze(past["cache"])
              return outputs
          elif past_key_values is not None and not return_dict:
              outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
  
          return outputs
  
      def prepare_inputs_for_generation(
          self,
          decoder_input_ids,
          max_length,
          attention_mask: Optional[jnp.DeviceArray] = None,
          decoder_attention_mask: Optional[jnp.DeviceArray] = None,
          encoder_outputs=None,
          **kwargs
      ):
          # initializing the cache
          batch_size, seq_length = decoder_input_ids.shape
  
          past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
          # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
          # But since the decoder uses a causal mask, those positions are masked anyways.
          # Thus we can create a single static attention_mask here, which is more efficient for compilation
          extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
          if decoder_attention_mask is not None:
              position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
              extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
          else:
              position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
  
          return {
              "past_key_values": past_key_values,
              "encoder_outputs": encoder_outputs,
              "encoder_attention_mask": attention_mask,
              "decoder_attention_mask": extended_attention_mask,
              "decoder_position_ids": position_ids,
          }
  
      def update_inputs_for_generation(self, model_outputs, model_kwargs):
          model_kwargs["past_key_values"] = model_outputs.past_key_values
          model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
          return model_kwargs
  
  
  FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING = r"""
      Returns:
  
      Summarization example::
  
          >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration, MBartConfig
  
          >>> model = FlaxMBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
          >>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
  
          >>> ARTICLE_TO_SUMMARIZE = "Meine Freunde sind cool, aber sie essen zu viel Kuchen."
          >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='np')
  
          >>> # Generate Summary
          >>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True).sequences
          >>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])
  
      Mask filling example::
  
          >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
          >>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
          >>> # de_DE is the language symbol id <LID> for German
          >>> TXT = "</s> Meine Freunde sind <mask> nett aber sie essen zu viel Kuchen. </s> de_DE"
  
          >>> model = FlaxMBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
          >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors='np')['input_ids']
          >>> logits = model(input_ids).logits
  
          >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero()[0].item()
          >>> probs = logits[0, masked_index].softmax(dim=0)
          >>> values, predictions = probs.topk(5)
  
          >>> tokenizer.decode(predictions).split()
  """
  
  overwrite_call_docstring(
      FlaxMBartForConditionalGeneration, MBART_INPUTS_DOCSTRING + FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING
  )
  append_replace_return_docstrings(
      FlaxMBartForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC
  )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForSequenceClassificationModule with Bart->MBart
  class FlaxMBartForSequenceClassificationModule(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32
      num_labels: Optional[int] = None
  
      def setup(self):
          self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
          self.classification_head = FlaxMBartClassificationHead(
              config=self.config,
              inner_dim=self.config.d_model,
              num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels,
              pooler_dropout=self.config.classifier_dropout,
          )
  
      def _get_encoder_module(self):
          return self.model.encoder
  
      def _get_decoder_module(self):
          return self.model.decoder
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          decoder_input_ids,
          decoder_attention_mask,
          position_ids,
          decoder_position_ids,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
          outputs = self.model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              position_ids=position_ids,
              decoder_position_ids=decoder_position_ids,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=deterministic,
          )
  
          hidden_states = outputs[0]  # last hidden state
  
          eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0)
  
          # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation
          if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer:
              if len(jnp.unique(eos_mask.sum(1))) > 1:
                  raise ValueError("All examples must have the same number of <eos> tokens.")
  
              if any(eos_mask.sum(1) == 0):
                  raise ValueError("There are missing <eos> tokens in input_ids")
  
              # Ensure to keep 1 only for the last <eos> token for each example
              eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6
              eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0)
  
          sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1)
          logits = self.classification_head(sentence_representation, deterministic=deterministic)
  
          if not return_dict:
              output = (logits,) + outputs[1:]
              return output
  
          return FlaxSeq2SeqSequenceClassifierOutput(
              logits=logits,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
  
  @add_start_docstrings(
      """
      MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
      tasks.
      """,
      MBART_START_DOCSTRING,
  )
  class FlaxMBartForSequenceClassification(FlaxMBartPreTrainedModel):
      module_class = FlaxMBartForSequenceClassificationModule
      dtype = jnp.float32
  
  
  append_call_sample_docstring(
      FlaxMBartForSequenceClassification,
      _TOKENIZER_FOR_DOC,
      _CHECKPOINT_FOR_DOC,
      FlaxSeq2SeqSequenceClassifierOutput,
      _CONFIG_FOR_DOC,
  )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForQuestionAnsweringModule with Bart->MBart
  class FlaxMBartForQuestionAnsweringModule(nn.Module):
      config: MBartConfig
      dtype: jnp.dtype = jnp.float32
      num_labels = 2
  
      def setup(self):
          self.model = FlaxMBartModule(config=self.config, dtype=self.dtype)
          self.qa_outputs = nn.Dense(
              self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
          )
  
      def _get_encoder_module(self):
          return self.model.encoder
  
      def _get_decoder_module(self):
          return self.model.decoder
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          decoder_input_ids,
          decoder_attention_mask,
          position_ids,
          decoder_position_ids,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
          outputs = self.model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              decoder_input_ids=decoder_input_ids,
              decoder_attention_mask=decoder_attention_mask,
              position_ids=position_ids,
              decoder_position_ids=decoder_position_ids,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
              return_dict=return_dict,
              deterministic=deterministic,
          )
  
          sequence_output = outputs[0]
  
          logits = self.qa_outputs(sequence_output)
          start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1)
          start_logits = start_logits.squeeze(-1)
          end_logits = end_logits.squeeze(-1)
  
          if not return_dict:
              output = (start_logits, end_logits) + outputs[1:]
              return output
  
          return FlaxSeq2SeqQuestionAnsweringModelOutput(
              start_logits=start_logits,
              end_logits=end_logits,
              decoder_hidden_states=outputs.decoder_hidden_states,
              decoder_attentions=outputs.decoder_attentions,
              cross_attentions=outputs.cross_attentions,
              encoder_last_hidden_state=outputs.encoder_last_hidden_state,
              encoder_hidden_states=outputs.encoder_hidden_states,
              encoder_attentions=outputs.encoder_attentions,
          )
  
  
  @add_start_docstrings(
      """
      MBart Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
      layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
      """,
      MBART_START_DOCSTRING,
  )
  class FlaxMBartForQuestionAnswering(FlaxMBartPreTrainedModel):
      module_class = FlaxMBartForQuestionAnsweringModule
      dtype = jnp.float32
  
  
  append_call_sample_docstring(
      FlaxMBartForQuestionAnswering,
      _TOKENIZER_FOR_DOC,
      _CHECKPOINT_FOR_DOC,
      FlaxSeq2SeqQuestionAnsweringModelOutput,
      _CONFIG_FOR_DOC,
  )
  