text: # coding=utf-8
  # Copyright 2021 The Marian Team Authors and The Google Flax Team Authors 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 Marian 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,
  )
  from ...modeling_flax_utils import (
      ACT2FN,
      FlaxPreTrainedModel,
      append_call_sample_docstring,
      append_replace_return_docstrings,
      overwrite_call_docstring,
  )
  from ...utils import logging
  from .configuration_marian import MarianConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "Helsinki-NLP/opus-mt-en-de"
  _CONFIG_FOR_DOC = "MarianConfig"
  _TOKENIZER_FOR_DOC = "MarianTokenizer"
  
  
  MARIAN_START_DOCSTRING = r"""
      This model inherits from [`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 ([`MarianConfig`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
          dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
              The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
              `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 [`~FlaxPreTrainedModel.to_fp16`] and
              [`~FlaxPreTrainedModel.to_bf16`].
  """
  
  MARIAN_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`jnp.ndarray` of shape `(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 [`MarianTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`jnp.ndarray` of shape `(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#attention-mask)
          decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
              Indices of decoder input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`MarianTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are decoder input IDs?](../glossary#decoder-input-ids)
  
              For translation and summarization training, `decoder_input_ids` should be provided. If no
              `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
              for denoising pre-training following the paper.
          decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
              Default behavior: generate a tensor that ignores pad tokens in `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 (`numpy.ndarray` of shape `(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 (`numpy.ndarray` of shape `(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 (`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 (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  MARIAN_ENCODE_INPUTS_DOCSTRING = r"""
      Args:
          input_ids (`jnp.ndarray` of shape `(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 [`MarianTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          attention_mask (`jnp.ndarray` of shape `(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#attention-mask)
          position_ids (`numpy.ndarray` of shape `(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 (`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 (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
  """
  
  MARIAN_DECODE_INPUTS_DOCSTRING = r"""
      Args:
          decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`):
              Indices of decoder input sequence tokens in the vocabulary.
  
              Indices can be obtained using [`MarianTokenizer`]. See [`PreTrainedTokenizer.encode`] and
              [`PreTrainedTokenizer.__call__`] for details.
  
              [What are decoder input IDs?](../glossary#decoder-input-ids)
  
              For translation and summarization training, `decoder_input_ids` should be provided. If no
              `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
              for denoising pre-training following the paper.
          encoder_outputs (`tuple(tuple(jnp.ndarray)`):
              Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
              `last_hidden_state` of shape `(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 (`jnp.ndarray` of shape `(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#attention-mask)
          decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
              Default behavior: generate a tensor that ignores pad tokens in `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 (`numpy.ndarray` of shape `(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 (`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 (`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 (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  def create_sinusoidal_positions(n_pos, dim):
      position_enc = np.array([[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)])
      sentinel = dim // 2 + dim % 2
      out = np.zeros_like(position_enc)
      out[:, 0:sentinel] = np.sin(position_enc[:, 0::2])
      out[:, sentinel:] = np.cos(position_enc[:, 1::2])
  
      return jnp.array(out)
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right
  def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
      """
      Shift input ids one token to the right.
      """
      shifted_input_ids = np.zeros_like(input_ids)
      shifted_input_ids[:, 1:] = input_ids[:, :-1]
      shifted_input_ids[:, 0] = decoder_start_token_id
  
      shifted_input_ids = np.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
      return shifted_input_ids
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->Marian
  class FlaxMarianAttention(nn.Module):
      config: MarianConfig
      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
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayer with Bart->Marian
  class FlaxMarianEncoderLayer(nn.Module):
      config: MarianConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self) -> None:
          self.embed_dim = self.config.d_model
          self.self_attn = FlaxMarianAttention(
              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, 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
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          residual = 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
          hidden_states = self.final_layer_norm(hidden_states)
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (attn_weights,)
  
          return outputs
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->Marian
  class FlaxMarianEncoderLayerCollection(nn.Module):
      config: MarianConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layers = [
              FlaxMarianEncoderLayer(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
          )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayer with Bart->Marian
  class FlaxMarianDecoderLayer(nn.Module):
      config: MarianConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self) -> None:
          self.embed_dim = self.config.d_model
          self.self_attn = FlaxMarianAttention(
              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 = FlaxMarianAttention(
              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
  
          # 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
          hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Cross-Attention Block
          cross_attn_weights = None
          if encoder_hidden_states is not None:
              residual = 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
              hidden_states = self.encoder_attn_layer_norm(hidden_states)
  
          # Fully Connected
          residual = 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
          hidden_states = self.final_layer_norm(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->Marian
  class FlaxMarianDecoderLayerCollection(nn.Module):
      config: MarianConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layers = [
              FlaxMarianDecoderLayer(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,
          )
  
  
  class FlaxMarianEncoder(nn.Module):
      config: MarianConfig
      embed_tokens: nn.Embed
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
  
          embed_dim = self.config.d_model
          self.max_source_positions = self.config.max_position_embeddings
          self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
  
          self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, embed_dim)
          self.layers = FlaxMarianEncoderLayerCollection(self.config, self.dtype)
  
      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
  
          positions = jnp.take(self.embed_positions, position_ids, axis=0)
          # explictly cast the positions here, since self.embed_positions are not registered as parameters
          positions = positions.astype(inputs_embeds.dtype)
  
          hidden_states = inputs_embeds + positions
          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,
          )
  
          if not return_dict:
              return outputs
  
          return FlaxBaseModelOutput(
              last_hidden_state=outputs.last_hidden_state,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  class FlaxMarianDecoder(nn.Module):
      config: MarianConfig
      embed_tokens: nn.Embed
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
  
          embed_dim = self.config.d_model
          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
  
          self.embed_positions = create_sinusoidal_positions(self.config.max_position_embeddings, embed_dim)
          self.layers = FlaxMarianDecoderLayerCollection(self.config, self.dtype)
  
      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 = jnp.take(self.embed_positions, position_ids, axis=0)
          # explictly cast the positions here, since self.embed_positions are not registered as parameters
          positions = positions.astype(inputs_embeds.dtype)
  
          hidden_states = inputs_embeds + positions
  
          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,
          )
  
          if not return_dict:
              return outputs
  
          return FlaxBaseModelOutputWithPastAndCrossAttentions(
              last_hidden_state=outputs.last_hidden_state,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
              cross_attentions=outputs.cross_attentions,
          )
  
  
  class FlaxMarianModule(nn.Module):
      config: MarianConfig
      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 = FlaxMarianEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
          self.decoder = FlaxMarianDecoder(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 FlaxMarianPreTrainedModel(FlaxPreTrainedModel):
      config_class = MarianConfig
      base_model_prefix: str = "model"
      module_class: nn.Module = None
  
      def __init__(
          self,
          config: MarianConfig,
          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 FlaxMarianForSequenceClassificationModule
          input_ids = input_ids.at[(..., -1)].set(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"]
  
      def init_cache(self, batch_size, max_length, encoder_outputs):
          r"""
          Args:
              batch_size (`int`):
                  batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
              max_length (`int`):
                  maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                  cache.
              encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
                  `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
                  `attentions`). `last_hidden_state` of shape `(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(MARIAN_ENCODE_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MarianConfig)
      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:
  
          ```python
          >>> from transformers import MarianTokenizer, FlaxMarianMTModel
  
          >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
          >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
  
          >>> text = "My friends are cool but they eat too many carbs."
          >>> inputs = tokenizer(text, max_length=64, 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(MARIAN_DECODE_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MarianConfig)
      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:
  
          ```python
          >>> import jax.numpy as jnp
          >>> from transformers import MarianTokenizer, FlaxMarianMTModel
  
          >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
          >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
  
          >>> text = "My friends are cool but they eat too many carbs."
          >>> inputs = tokenizer(text, max_length=64, 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 FlaxMarianAttention 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(MARIAN_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, decoder_start_token_id=self.config.decoder_start_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 Marian Model transformer outputting raw hidden-states without any specific head on top.",
      MARIAN_START_DOCSTRING,
  )
  class FlaxMarianModel(FlaxMarianPreTrainedModel):
      config: MarianConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      module_class = FlaxMarianModule
  
  
  append_call_sample_docstring(
      FlaxMarianModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC
  )
  
  
  class FlaxMarianMTModule(nn.Module):
      config: MarianConfig
      dtype: jnp.dtype = jnp.float32
      bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
  
      def setup(self):
          self.model = FlaxMarianModule(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 MARIAN Model with a language modeling head. Can be used for translation.", MARIAN_START_DOCSTRING
  )
  class FlaxMarianMTModel(FlaxMarianPreTrainedModel):
      module_class = FlaxMarianMTModule
      dtype: jnp.dtype = jnp.float32
  
      @add_start_docstrings(MARIAN_DECODE_INPUTS_DOCSTRING)
      @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MarianConfig)
      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:
  
          ```python
          >>> import jax.numpy as jnp
          >>> from transformers import MarianTokenizer, FlaxMarianMTModel
  
          >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
          >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
  
          >>> text = "My friends are cool but they eat too many carbs."
          >>> inputs = tokenizer(text, max_length=64, 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 FlaxMarianAttention 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 _adapt_logits_for_beam_search(self, logits):
          """This function enforces the padding token never to be generated."""
          logits = logits.at[jax.ops.index[:, :, self.config.pad_token_id]].set(float("-inf"))
          return logits
  
      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_MARIAN_MT_DOCSTRING = """
      Returns:
  
      Example:
  
      ```python
      >>> from transformers import MarianTokenizer, FlaxMarianMTModel
  
      >>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
      >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
  
      >>> text = "My friends are cool but they eat too many carbs."
      >>> input_ids = tokenizer(text, max_length=64, return_tensors="jax").input_ids
  
      >>> sequences = model.generate(input_ids, max_length=64, num_beams=2).sequences
  
      >>> outputs = tokenizer.batch_decode(sequences, skip_special_tokens=True)
      >>> # should give *Meine Freunde sind cool, aber sie essen zu viele Kohlenhydrate.*
      ```
  """
  
  overwrite_call_docstring(
      FlaxMarianMTModel,
      MARIAN_INPUTS_DOCSTRING + FLAX_MARIAN_MT_DOCSTRING,
  )
  append_replace_return_docstrings(FlaxMarianMTModel, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
  