text: # coding=utf-8
  # Copyright 2022 The Fairseq 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 OPT model."""
  
  from functools import partial
  from typing import Optional, Tuple
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
  from flax.linen import combine_masks, make_causal_mask
  from flax.linen.attention import dot_product_attention_weights
  from flax.traverse_util import flatten_dict, unflatten_dict
  from jax import lax
  from jax.random import PRNGKey
  
  from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput
  from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
  from ...utils import add_start_docstrings, logging
  from .configuration_opt import OPTConfig
  
  
  logger = logging.get_logger(__name__)
  
  _CHECKPOINT_FOR_DOC = "facebook/opt-350m"
  _CONFIG_FOR_DOC = "OPTConfig"
  _TOKENIZER_FOR_DOC = "GPT2Tokenizer"
  
  
  OPT_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 ([`OPTConfig`]): 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`].
  """
  
  OPT_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 [`GPT2Tokenizer`]. 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 [`~utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->OPT
  class FlaxOPTAttention(nn.Module):
      config: OPTConfig
      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 FlaxOPTDecoderLayer(nn.Module):
      config: OPTConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self) -> None:
          self.embed_dim = self.config.hidden_size
          self.self_attn = FlaxOPTAttention(
              config=self.config,
              embed_dim=self.embed_dim,
              num_heads=self.config.num_attention_heads,
              dropout=self.config.attention_dropout,
              causal=True,
              dtype=self.dtype,
          )
          self.do_layer_norm_before = self.config.do_layer_norm_before
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
          self.activation_fn = ACT2FN[self.config.activation_function]
  
          self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          self.fc1 = nn.Dense(
              self.config.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,
          init_cache: bool = False,
          output_attentions: bool = True,
          deterministic: bool = True,
      ) -> Tuple[jnp.ndarray]:
  
          residual = hidden_states
  
          # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
          if self.do_layer_norm_before:
              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,
              deterministic=deterministic,
          )
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
          hidden_states = residual + hidden_states
          # 350m applies layer norm AFTER attention
          if not self.do_layer_norm_before:
              hidden_states = self.self_attn_layer_norm(hidden_states)
  
          # Fully Connected
          hidden_states_shape = hidden_states.shape
          hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
          residual = hidden_states
  
          # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
          if self.do_layer_norm_before:
              hidden_states = self.final_layer_norm(hidden_states)
  
          hidden_states = self.fc1(hidden_states)
          hidden_states = self.activation_fn(hidden_states)
  
          hidden_states = self.fc2(hidden_states)
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
  
          hidden_states = (residual + hidden_states).reshape(hidden_states_shape)
  
          # 350m applies layer norm AFTER attention
          if not self.do_layer_norm_before:
              hidden_states = self.final_layer_norm(hidden_states)
  
          outputs = (hidden_states,)
  
          if output_attentions:
              outputs += (self_attn_weights,)
  
          return outputs
  
  
  class FlaxOPTDecoderLayerCollection(nn.Module):
      config: OPTConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.layers = [
              FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype)
              for i in range(self.config.num_hidden_layers)
          ]
          self.layerdrop = self.config.layerdrop
  
      def __call__(
          self,
          hidden_states,
          attention_mask,
          deterministic: bool = True,
          init_cache: bool = False,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
      ):
          # decoder layers
          all_hidden_states = () if output_hidden_states else None
          all_self_attns = () if output_attentions else None
  
          for decoder_layer in self.layers:
              if output_hidden_states:
                  all_hidden_states += (hidden_states,)
  
              layer_outputs = decoder_layer(
                  hidden_states,
                  attention_mask=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],)
  
          outputs = [hidden_states, all_hidden_states, all_self_attns]
          return outputs
  
  
  class FlaxOPTLearnedPositionalEmbedding(nn.Embed):
      """
      This module learns positional embeddings up to a fixed maximum size.
      """
  
      def setup(self):
          self.offset = 2
          self.embedding = self.param(
              "embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features), self.param_dtype
          )
  
      def __call__(self, positions):
          """`input_ids_shape` is expected to be [bsz x seqlen]."""
  
          return super().__call__(positions + self.offset)
  
  
  class FlaxOPTDecoder(nn.Module):
      config: OPTConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      offset: int = 2
  
      def setup(self):
          self.dropout_layer = nn.Dropout(rate=self.config.dropout)
  
          embed_dim = self.config.hidden_size
          self.padding_idx = self.config.pad_token_id
          self.max_target_positions = self.config.max_position_embeddings
  
          self.embed_tokens = nn.Embed(
              self.config.vocab_size,
              self.config.word_embed_proj_dim,
              embedding_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
          self.embed_positions = FlaxOPTLearnedPositionalEmbedding(
              self.config.max_position_embeddings,
              embed_dim,
              embedding_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
          if self.config.word_embed_proj_dim != self.config.hidden_size:
              self.project_in = nn.Dense(self.config.hidden_size, use_bias=False)
              self.project_out = nn.Dense(self.config.word_embed_proj_dim, use_bias=False)
  
          else:
              self.project_in = None
              self.project_out = None
  
          # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
          # with checkpoints that have been fine-tuned before transformers v4.20.1
          # see https://github.com/facebookresearch/metaseq/pull/164
          if self.config.do_layer_norm_before and not self.config._remove_final_layer_norm:
              self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
          else:
              self.final_layer_norm = None
  
          self.layers = FlaxOPTDecoderLayerCollection(self.config, self.dtype)
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          position_ids,
          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)
          if self.project_in is not None:
              inputs_embeds = self.project_in(inputs_embeds)
  
          positions = self.embed_positions(position_ids)
  
          hidden_states = inputs_embeds + positions
  
          hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
  
          hidden_state, all_hidden_states, attentions = self.layers(
              hidden_states,
              attention_mask,
              deterministic=deterministic,
              init_cache=init_cache,
              output_attentions=output_attentions,
              output_hidden_states=output_hidden_states,
          )
  
          if self.final_layer_norm is not None:
              hidden_state = self.final_layer_norm(hidden_state)
  
          if self.project_out is not None:
              hidden_state = self.project_out(hidden_state)
  
          if output_hidden_states:
              all_hidden_states += (hidden_state,)
  
          outputs = [hidden_state, all_hidden_states, attentions]
  
          if not return_dict:
              return tuple(v for v in outputs if v is not None)
  
          return FlaxBaseModelOutput(
              last_hidden_state=hidden_state,
              hidden_states=all_hidden_states,
              attentions=attentions,
          )
  
  
  class FlaxOPTPreTrainedModel(FlaxPreTrainedModel):
      config_class = OPTConfig
      base_model_prefix: str = "model"
      module_class: nn.Module = None
  
      def __init__(
          self,
          config: OPTConfig,
          input_shape: Tuple[int] = (1, 1),
          seed: int = 0,
          dtype: jnp.dtype = jnp.float32,
          _do_init: bool = True,
          **kwargs
      ):
          module = self.module_class(config=config, dtype=dtype, **kwargs)
          super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
  
      def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
          # init input tensors
          input_ids = jnp.zeros(input_shape, dtype="i4")
          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))
  
          params_rng, dropout_rng = jax.random.split(rng)
          rngs = {"params": params_rng, "dropout": dropout_rng}
  
          module_init_outputs = self.module.init(
              rngs,
              input_ids,
              attention_mask,
              position_ids,
              return_dict=False,
          )
  
          random_params = module_init_outputs["params"]
          if params is not None:
              random_params = flatten_dict(unfreeze(random_params))
              params = flatten_dict(unfreeze(params))
              for missing_key in self._missing_keys:
                  params[missing_key] = random_params[missing_key]
              self._missing_keys = set()
              return freeze(unflatten_dict(params))
          else:
              return random_params
  
      def init_cache(self, batch_size, max_length):
          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.
          """
          # init input variables to retrieve cache
          input_ids = jnp.ones((batch_size, max_length), dtype="i4")
          attention_mask = jnp.ones_like(input_ids, dtype="i4")
          position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
  
          init_variables = self.module.init(
              jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
          )
          return unfreeze(init_variables["cache"])
  
      def __call__(
          self,
          input_ids: jnp.ndarray,
          attention_mask: Optional[jnp.ndarray] = None,
          position_ids: Optional[jnp.ndarray] = None,
          params: dict = None,
          past_key_values: dict = None,
          output_attentions: Optional[bool] = None,
          output_hidden_states: Optional[bool] = None,
          return_dict: Optional[bool] = None,
          dropout_rng: PRNGKey = None,
          deterministic: bool = True,
      ):
          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:
              position_ids = (attention_mask.cumsum(axis=1) * attention_mask) - 1
  
          # Handle any PRNG if needed
          rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
  
          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 FlaxOPTAttention module
          if past_key_values:
              inputs["cache"] = past_key_values
              mutable = ["cache"]
          else:
              mutable = False
  
          outputs = self.module.apply(
              inputs,
              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=deterministic,
              rngs=rngs,
              mutable=mutable,
          )
  
          # add updated cache to model output
          if past_key_values is not None and return_dict:
              outputs, past_key_values = outputs
              outputs["past_key_values"] = unfreeze(past_key_values["cache"])
              return outputs
          elif past_key_values is not None and not return_dict:
              outputs, past_key_values = outputs
              outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
  
          return outputs
  
  
  class FlaxOPTModule(nn.Module):
      config: OPTConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
  
      def setup(self):
          self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype)
  
      def _get_decoder_module(self):
          return self.decoder
  
      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,
          init_cache=False,
      ):
  
          decoder_outputs = self.decoder(
              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,
              init_cache=init_cache,
          )
  
          if not return_dict:
              return decoder_outputs
  
          return FlaxBaseModelOutput(
              last_hidden_state=decoder_outputs.last_hidden_state,
              hidden_states=decoder_outputs.hidden_states,
              attentions=decoder_outputs.attentions,
          )
  
  
  # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModel with Bart->OPT
  class FlaxOPTModel(FlaxOPTPreTrainedModel):
      config: OPTConfig
      dtype: jnp.dtype = jnp.float32  # the dtype of the computation
      module_class = FlaxOPTModule
  
  
  append_call_sample_docstring(
      FlaxOPTModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC
  )
  
  
  @add_start_docstrings(
      "The bare OPT Model transformer outputting raw hidden-states without any specific head on top.",
      OPT_START_DOCSTRING,
  )
  class FlaxOPTForCausalLMModule(nn.Module):
      config: OPTConfig
      dtype: jnp.dtype = jnp.float32
  
      def setup(self):
          self.model = FlaxOPTModule(config=self.config, dtype=self.dtype)
          self.lm_head = nn.Dense(
              self.config.vocab_size,
              use_bias=False,
              dtype=self.dtype,
              kernel_init=jax.nn.initializers.normal(self.config.init_std),
          )
  
      def __call__(
          self,
          input_ids,
          attention_mask,
          position_ids,
          init_cache: bool = False,
          output_attentions: bool = False,
          output_hidden_states: bool = False,
          return_dict: bool = True,
          deterministic: bool = True,
      ):
  
          outputs = self.model(
              input_ids,
              attention_mask,
              position_ids,
              init_cache=init_cache,
              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"]["decoder"]["embed_tokens"]["embedding"]
              lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
          else:
              lm_logits = self.lm_head(hidden_states)
  
          if not return_dict:
              return (lm_logits,) + outputs[1:]
  
          return FlaxMaskedLMOutput(
              logits=lm_logits,
              hidden_states=outputs.hidden_states,
              attentions=outputs.attentions,
          )
  
  
  @add_start_docstrings(
      """
      OPT Model with a language modeling head on top (linear layer with weights tied to the input embeddings) e.g for
      autoregressive tasks.
      """,
      OPT_START_DOCSTRING,
  )
  class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel):
      module_class = FlaxOPTForCausalLMModule
  
      def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None):
          # initializing the cache
          batch_size, seq_length = input_ids.shape
  
          past_key_values = self.init_cache(batch_size, max_length)
          # 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 anyway.
          # 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 attention_mask is not None:
              position_ids = attention_mask.cumsum(axis=1) - 1
              extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, 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,
              "attention_mask": extended_attention_mask,
              "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["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
          return model_kwargs
  
  
  append_call_sample_docstring(
      FlaxOPTForCausalLM,
      _TOKENIZER_FOR_DOC,
      _CHECKPOINT_FOR_DOC,
      FlaxBaseModelOutput,
      _CONFIG_FOR_DOC,
  )
  