

'''simple docstring'''



from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin







@dataclass
class 		lowerCAmelCase__     (   _a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   42







class 		lowerCAmelCase__     (   _a				,      _a      ):



 """simple docstring"""



 @register_to_config
 def __init__(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : int = 16      ,							__SCREAMING_SNAKE_CASE      : int = 88      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : int = 1      ,							__SCREAMING_SNAKE_CASE      : float = 0.0      ,							__SCREAMING_SNAKE_CASE      : int = 32      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : str = "geglu"      ,							__SCREAMING_SNAKE_CASE      : Optional[int] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : str = "layer_norm"      ,							__SCREAMING_SNAKE_CASE      : bool = True      ,							)						->				List[str]:


      """simple docstring"""




      super().__init__()
      __SCREAMING_SNAKE_CASE						     =   use_linear_projection
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   attention_head_dim
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads * attention_head_dim

      # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
      # Define whether input is continuous or discrete depending on configuration
      __SCREAMING_SNAKE_CASE						     =   (in_channels is not None) and (patch_size is None)
      __SCREAMING_SNAKE_CASE						     =   num_vector_embeds is not None
      __SCREAMING_SNAKE_CASE						     =   in_channels is not None and patch_size is not None

      if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
           __SCREAMING_SNAKE_CASE						     =   (
               f'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
               """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
               """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
               """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
               """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
           )
           deprecate("""norm_type!=num_embeds_ada_norm"""      ,							"""1.0.0"""      ,							__SCREAMING_SNAKE_CASE      ,							standard_warn=__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   """ada_norm"""

      if self.is_input_continuous and self.is_input_vectorized:
           raise ValueError(
               f'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
               """ sure that either `in_channels` or `num_vector_embeds` is None."""    )
      elif self.is_input_vectorized and self.is_input_patches:
           raise ValueError(
               f'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
               """ sure that either `num_vector_embeds` or `num_patches` is None."""    )
      elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
           raise ValueError(
               f'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
               f' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'    )

      # 2. Define input layers
      if self.is_input_continuous:
           __SCREAMING_SNAKE_CASE						     =   in_channels

           __SCREAMING_SNAKE_CASE						     =   torch.nn.GroupNorm(num_groups=__SCREAMING_SNAKE_CASE      ,							num_channels=__SCREAMING_SNAKE_CASE      ,							eps=1E-6      ,							affine=__SCREAMING_SNAKE_CASE    )
           if use_linear_projection:
                __SCREAMING_SNAKE_CASE						     =   nn.Linear(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
           else:
                __SCREAMING_SNAKE_CASE						     =   nn.Convad(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							kernel_size=1      ,							stride=1      ,							padding=0    )
      elif self.is_input_vectorized:
           assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
           assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

           __SCREAMING_SNAKE_CASE						     =   sample_size
           __SCREAMING_SNAKE_CASE						     =   sample_size
           __SCREAMING_SNAKE_CASE						     =   num_vector_embeds
           __SCREAMING_SNAKE_CASE						     =   self.height * self.width

           __SCREAMING_SNAKE_CASE						     =   ImagePositionalEmbeddings(
               num_embed=__SCREAMING_SNAKE_CASE      ,							embed_dim=__SCREAMING_SNAKE_CASE      ,							height=self.height      ,							width=self.width    )
      elif self.is_input_patches:
           assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

           __SCREAMING_SNAKE_CASE						     =   sample_size
           __SCREAMING_SNAKE_CASE						     =   sample_size

           __SCREAMING_SNAKE_CASE						     =   patch_size
           __SCREAMING_SNAKE_CASE						     =   PatchEmbed(
               height=__SCREAMING_SNAKE_CASE      ,							width=__SCREAMING_SNAKE_CASE      ,							patch_size=__SCREAMING_SNAKE_CASE      ,							in_channels=__SCREAMING_SNAKE_CASE      ,							embed_dim=__SCREAMING_SNAKE_CASE      ,							)

      # 3. Define transformers blocks
      __SCREAMING_SNAKE_CASE						     =   nn.ModuleList(
          [
              BasicTransformerBlock(
                  __SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							dropout=__SCREAMING_SNAKE_CASE      ,							cross_attention_dim=__SCREAMING_SNAKE_CASE      ,							activation_fn=__SCREAMING_SNAKE_CASE      ,							num_embeds_ada_norm=__SCREAMING_SNAKE_CASE      ,							attention_bias=__SCREAMING_SNAKE_CASE      ,							only_cross_attention=__SCREAMING_SNAKE_CASE      ,							upcast_attention=__SCREAMING_SNAKE_CASE      ,							norm_type=__SCREAMING_SNAKE_CASE      ,							norm_elementwise_affine=__SCREAMING_SNAKE_CASE      ,							)
              for d in range(__SCREAMING_SNAKE_CASE    )
          ]    )

      # 4. Define output layers
      __SCREAMING_SNAKE_CASE						     =   in_channels if out_channels is None else out_channels
      if self.is_input_continuous:
           # TODO: should use out_channels for continuous projections
           if use_linear_projection:
                __SCREAMING_SNAKE_CASE						     =   nn.Linear(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
           else:
                __SCREAMING_SNAKE_CASE						     =   nn.Convad(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							kernel_size=1      ,							stride=1      ,							padding=0    )
      elif self.is_input_vectorized:
           __SCREAMING_SNAKE_CASE						     =   nn.LayerNorm(__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   nn.Linear(__SCREAMING_SNAKE_CASE      ,							self.num_vector_embeds - 1    )
      elif self.is_input_patches:
           __SCREAMING_SNAKE_CASE						     =   nn.LayerNorm(__SCREAMING_SNAKE_CASE      ,							elementwise_affine=__SCREAMING_SNAKE_CASE      ,							eps=1E-6    )
           __SCREAMING_SNAKE_CASE						     =   nn.Linear(__SCREAMING_SNAKE_CASE      ,							2 * inner_dim    )
           __SCREAMING_SNAKE_CASE						     =   nn.Linear(__SCREAMING_SNAKE_CASE      ,							patch_size * patch_size * self.out_channels    )


 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : torch.Tensor      ,							__SCREAMING_SNAKE_CASE      : Optional[torch.Tensor] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[torch.LongTensor] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[torch.LongTensor] = None      ,							__SCREAMING_SNAKE_CASE      : Dict[str, Any] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[torch.Tensor] = None      ,							__SCREAMING_SNAKE_CASE      : Optional[torch.Tensor] = None      ,							__SCREAMING_SNAKE_CASE      : bool = True      ,							)						->				List[Any]:


      """simple docstring"""




      if attention_mask is not None and attention_mask.ndim == 2:
           # assume that mask is expressed as:
           #   (1 = keep,      0 = discard)
           # convert mask into a bias that can be added to attention scores:
           #       (keep = +0,     discard = -10000.0)
           __SCREAMING_SNAKE_CASE						     =   (1 - attention_mask.to(hidden_states.dtype    )) * -10_000.0
           __SCREAMING_SNAKE_CASE						     =   attention_mask.unsqueeze(1    )

      # convert encoder_attention_mask to a bias the same way we do for attention_mask
      if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
           __SCREAMING_SNAKE_CASE						     =   (1 - encoder_attention_mask.to(hidden_states.dtype    )) * -10_000.0
           __SCREAMING_SNAKE_CASE						     =   encoder_attention_mask.unsqueeze(1    )

      # 1. Input
      if self.is_input_continuous:
           __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   hidden_states.shape
           __SCREAMING_SNAKE_CASE						     =   hidden_states

           __SCREAMING_SNAKE_CASE						     =   self.norm(__SCREAMING_SNAKE_CASE    )
           if not self.use_linear_projection:
                __SCREAMING_SNAKE_CASE						     =   self.proj_in(__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   hidden_states.shape[1]
                __SCREAMING_SNAKE_CASE						     =   hidden_states.permute(0      ,							2      ,							3      ,							1    ).reshape(__SCREAMING_SNAKE_CASE      ,							height * width      ,							__SCREAMING_SNAKE_CASE    )
           else:
                __SCREAMING_SNAKE_CASE						     =   hidden_states.shape[1]
                __SCREAMING_SNAKE_CASE						     =   hidden_states.permute(0      ,							2      ,							3      ,							1    ).reshape(__SCREAMING_SNAKE_CASE      ,							height * width      ,							__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   self.proj_in(__SCREAMING_SNAKE_CASE    )
      elif self.is_input_vectorized:
           __SCREAMING_SNAKE_CASE						     =   self.latent_image_embedding(__SCREAMING_SNAKE_CASE    )
      elif self.is_input_patches:
           __SCREAMING_SNAKE_CASE						     =   self.pos_embed(__SCREAMING_SNAKE_CASE    )

      # 2. Blocks
      for block in self.transformer_blocks:
           __SCREAMING_SNAKE_CASE						     =   block(
               __SCREAMING_SNAKE_CASE      ,							attention_mask=__SCREAMING_SNAKE_CASE      ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,							encoder_attention_mask=__SCREAMING_SNAKE_CASE      ,							timestep=__SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs=__SCREAMING_SNAKE_CASE      ,							class_labels=__SCREAMING_SNAKE_CASE      ,							)

      # 3. Output
      if self.is_input_continuous:
           if not self.use_linear_projection:
                __SCREAMING_SNAKE_CASE						     =   hidden_states.reshape(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ).permute(0      ,							3      ,							1      ,							2    ).contiguous()
                __SCREAMING_SNAKE_CASE						     =   self.proj_out(__SCREAMING_SNAKE_CASE    )
           else:
                __SCREAMING_SNAKE_CASE						     =   self.proj_out(__SCREAMING_SNAKE_CASE    )
                __SCREAMING_SNAKE_CASE						     =   hidden_states.reshape(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ).permute(0      ,							3      ,							1      ,							2    ).contiguous()

           __SCREAMING_SNAKE_CASE						     =   hidden_states + residual
      elif self.is_input_vectorized:
           __SCREAMING_SNAKE_CASE						     =   self.norm_out(__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   self.out(__SCREAMING_SNAKE_CASE    )
           # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
           __SCREAMING_SNAKE_CASE						     =   logits.permute(0      ,							2      ,							1    )

           # log(p(x_0))
           __SCREAMING_SNAKE_CASE						     =   F.log_softmax(logits.double()      ,							dim=1    ).float()
      elif self.is_input_patches:
           # TODO: cleanup!
           __SCREAMING_SNAKE_CASE						     =   self.transformer_blocks[0].norma.emb(
               __SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							hidden_dtype=hidden_states.dtype    )
           __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   self.proj_out_a(F.silu(__SCREAMING_SNAKE_CASE    )    ).chunk(2      ,							dim=1    )
           __SCREAMING_SNAKE_CASE						     =   self.norm_out(__SCREAMING_SNAKE_CASE    ) * (1 + scale[:, None]) + shift[:, None]
           __SCREAMING_SNAKE_CASE						     =   self.proj_out_a(__SCREAMING_SNAKE_CASE    )

           # unpatchify
           __SCREAMING_SNAKE_CASE						     =   __SCREAMING_SNAKE_CASE						     =   int(hidden_states.shape[1] ** 0.5    )
           __SCREAMING_SNAKE_CASE						     =   hidden_states.reshape(
               shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)    )
           __SCREAMING_SNAKE_CASE						     =   torch.einsum("""nhwpqc->nchpwq"""      ,							__SCREAMING_SNAKE_CASE    )
           __SCREAMING_SNAKE_CASE						     =   hidden_states.reshape(
               shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)    )

      if not return_dict:
           return (output,)

      return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE    )









'''simple docstring'''



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		lowerCAmelCase__     (   utils.Parser      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "halfcheetah-medium-expert-v2"
 lowerCAmelCase__						      =   "config.offline"







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   utils.load_model(a__   ,					a__   ,					a__   ,					epoch=a__   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   TrajectoryTransformerModel(gpt.config		)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
     __SCREAMING_SNAKE_CASE						     =   gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()		)
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()		)
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()		)

     for i, block in enumerate(gpt.blocks		):
          trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
          trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
          trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()		)

          trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()		)
          trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()		)
          trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()		)
          trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()		)

     torch.save(trajectory_transformer.state_dict()   ,					"""pytorch_model.bin"""		)


if __name__ == "__main__":
  UpperCAmelCase				: Dict         =  Parser().parse_args('plan')
  convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
      args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
  )






