




from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class __snake_case			(			a ,  a ,  a		):
   UpperCAmelCase__  :    Optional[int]          =  [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']







   @register_to_config
   def __init__(      self		:						int					,						_snake_case		:						int					,						_snake_case		:						int					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						int = 50257					,						_snake_case		:						int = 1024					,						_snake_case		:						int = 768					,						_snake_case		:						int = 12					,						_snake_case		:						int = 12					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						str = "gelu_new"					,						_snake_case		:						float = 0.1					,						_snake_case		:						float = 0.1					,						_snake_case		:						float = 0.1					,						_snake_case		:						float = 1e-5					,						_snake_case		:						float = 0.0_2					,						_snake_case		:						bool = True					,						_snake_case		:						bool = True					,						_snake_case		:						bool = False					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__()

        UpperCAmelCase_          =							prefix_length

        if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
             raise ValueError(
                 F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
                 F""" `n_embd`: {n_embd} are not equal.""")

        UpperCAmelCase_          =							prefix_inner_dim
        UpperCAmelCase_          =							prefix_hidden_dim

        UpperCAmelCase_          =							(
            nn.Linear(self.prefix_inner_dim					,						self.prefix_hidden_dim)
            if self.prefix_hidden_dim is not None
            else nn.Identity()
        )
        UpperCAmelCase_          =							(
            nn.Linear(self.prefix_hidden_dim					,						_snake_case) if self.prefix_hidden_dim is not None else nn.Identity()
        )

        UpperCAmelCase_          =							GPTaConfig(
            vocab_size=_snake_case					,						n_positions=_snake_case					,						n_embd=_snake_case					,						n_layer=_snake_case					,						n_head=_snake_case					,						n_inner=_snake_case					,						activation_function=_snake_case					,						resid_pdrop=_snake_case					,						embd_pdrop=_snake_case					,						attn_pdrop=_snake_case					,						layer_norm_epsilon=_snake_case					,						initializer_range=_snake_case					,						scale_attn_weights=_snake_case					,						use_cache=_snake_case					,						scale_attn_by_inverse_layer_idx=_snake_case					,						reorder_and_upcast_attn=_snake_case					,						)
        UpperCAmelCase_          =							GPTaLMHeadModel(_snake_case)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						torch.Tensor					,						_snake_case		:						torch.Tensor					,						_snake_case		:						Optional[torch.Tensor] = None					,						_snake_case		:						Optional[torch.Tensor] = None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							self.transformer.transformer.wte(_snake_case)
        UpperCAmelCase_          =							self.encode_prefix(_snake_case)
        UpperCAmelCase_          =							self.decode_prefix(_snake_case)
        UpperCAmelCase_          =							torch.cat((prefix_embeds, embedding_text)					,						dim=1)

        if labels is not None:
             UpperCAmelCase_          =							self.get_dummy_token(input_ids.shape[0]					,						input_ids.device)
             UpperCAmelCase_          =							torch.cat((dummy_token, input_ids)					,						dim=1)
        UpperCAmelCase_          =							self.transformer(inputs_embeds=_snake_case					,						labels=_snake_case					,						attention_mask=_snake_case)
        if self.prefix_hidden_dim is not None:
             return out, hidden
        else:
             return out







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						int					,						_snake_case		:						torch.device):

        """simple docstring"""


        return torch.zeros(_snake_case					,						self.prefix_length					,						dtype=torch.intaa					,						device=_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        return self.encode_prefix(_snake_case)







   @torch.no_grad()
   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Dict					,						_snake_case		:						Optional[int]					,						_snake_case		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							torch.split(_snake_case					,						1					,						dim=0)
        UpperCAmelCase_          =							[]
        UpperCAmelCase_          =							[]
        for feature in features:
             UpperCAmelCase_          =							self.decode_prefix(feature.to(_snake_case))  # back to the clip feature
             # Only support beam search for now
             UpperCAmelCase_     ,       UpperCAmelCase_          =							self.generate_beam(
                 input_embeds=_snake_case					,						device=_snake_case					,						eos_token_id=_snake_case)
             generated_tokens.append(output_tokens[0])
             generated_seq_lengths.append(seq_lengths[0])
        UpperCAmelCase_          =							torch.stack(_snake_case)
        UpperCAmelCase_          =							torch.stack(_snake_case)
        return generated_tokens, generated_seq_lengths







   @torch.no_grad()
   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Tuple=None					,						_snake_case		:						Optional[int]=None					,						_snake_case		:						List[Any]=None					,						_snake_case		:						int = 5					,						_snake_case		:						int = 67					,						_snake_case		:						float = 1.0					,						_snake_case		:						Optional[int] = None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							eos_token_id
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							torch.ones(_snake_case					,						device=_snake_case					,						dtype=torch.int)
        UpperCAmelCase_          =							torch.zeros(_snake_case					,						device=_snake_case					,						dtype=torch.bool)

        if input_embeds is not None:
             UpperCAmelCase_          =							input_embeds
        else:
             UpperCAmelCase_          =							self.transformer.transformer.wte(_snake_case)

        for i in range(_snake_case):
             UpperCAmelCase_          =							self.transformer(inputs_embeds=_snake_case)
             UpperCAmelCase_          =							outputs.logits
             UpperCAmelCase_          =							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
             UpperCAmelCase_          =							logits.softmax(-1).log()

             if scores is None:
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							logits.topk(_snake_case					,						-1)
                  UpperCAmelCase_          =							generated.expand(_snake_case					,						*generated.shape[1:])
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							next_tokens.permute(1					,						0), scores.squeeze(0)
                  if tokens is None:
                       UpperCAmelCase_          =							next_tokens
                  else:
                       UpperCAmelCase_          =							tokens.expand(_snake_case					,						*tokens.shape[1:])
                       UpperCAmelCase_          =							torch.cat((tokens, next_tokens)					,						dim=1)
             else:
                  UpperCAmelCase_          =							-float(np.inf)
                  UpperCAmelCase_          =							0
                  UpperCAmelCase_          =							scores[:, None] + logits
                  seq_lengths[~is_stopped] += 1
                  UpperCAmelCase_          =							scores_sum / seq_lengths[:, None]
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							scores_sum_average.view(-1).topk(_snake_case					,						-1)
                  UpperCAmelCase_          =							next_tokens // scores_sum.shape[1]
                  UpperCAmelCase_          =							seq_lengths[next_tokens_source]
                  UpperCAmelCase_          =							next_tokens % scores_sum.shape[1]
                  UpperCAmelCase_          =							next_tokens.unsqueeze(1)
                  UpperCAmelCase_          =							tokens[next_tokens_source]
                  UpperCAmelCase_          =							torch.cat((tokens, next_tokens)					,						dim=1)
                  UpperCAmelCase_          =							generated[next_tokens_source]
                  UpperCAmelCase_          =							scores_sum_average * seq_lengths
                  UpperCAmelCase_          =							is_stopped[next_tokens_source]

             UpperCAmelCase_          =							self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]					,						1					,						-1)
             UpperCAmelCase_          =							torch.cat((generated, next_token_embed)					,						dim=1)
             UpperCAmelCase_          =							is_stopped + next_tokens.eq(_snake_case).squeeze()
             if is_stopped.all():
                  break

        UpperCAmelCase_          =							scores / seq_lengths
        UpperCAmelCase_          =							scores.argsort(descending=_snake_case)
        # tokens tensors are already padded to max_seq_length
        UpperCAmelCase_          =							[tokens[i] for i in order]
        UpperCAmelCase_          =							torch.stack(_snake_case					,						dim=0)
        UpperCAmelCase_          =							torch.tensor([seq_lengths[i] for i in order]					,						dtype=seq_lengths.dtype)
        return output_texts, seq_lengths












from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Optional[int]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






