import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		List[Any]						,  lowercase_:		int						,  lowercase_:		Any						,  lowercase_:		int = 4096			):
       _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    List[str]					  =					key.shape[-3:]
       _UpperCamelCase       :    Optional[int]					  =					value.shape[-1]
       _UpperCamelCase       :    int					  =					min(lowercase_						,  lowercase_			)
       _UpperCamelCase       :    Tuple					  =					query / jnp.sqrt(lowercase_			)

       @functools.partial(jax.checkpoint						,  prevent_cse=lowercase_			)
       def summarize_chunk(lowercase_:		Tuple						,  lowercase_:		Any						,  lowercase_:		int			):
              _UpperCamelCase       :    List[str]					  =					jnp.einsum('...qhd,...khd->...qhk'						,  lowercase_						,  lowercase_						,  precision=lowercase_			)

              _UpperCamelCase       :    Optional[int]					  =					jnp.max(lowercase_						,  axis=-1						,  keepdims=lowercase_			)
              _UpperCamelCase       :    Union[str, Any]					  =					jax.lax.stop_gradient(lowercase_			)
              _UpperCamelCase       :    Union[str, Any]					  =					jnp.exp(attn_weights - max_score			)

              _UpperCamelCase       :    Optional[int]					  =					jnp.einsum('...vhf,...qhv->...qhf'						,  lowercase_						,  lowercase_						,  precision=lowercase_			)
              _UpperCamelCase       :    Union[str, Any]					  =					jnp.einsum('...qhk->...qh'						,  lowercase_			)

              return (exp_values, exp_weights.sum(axis=-1			), max_score)

       def chunk_scanner(lowercase_:		int			):
              # julienne key array
              _UpperCamelCase       :    str					  =					jax.lax.dynamic_slice(
                  operand=lowercase_						,  start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]						,  slice_sizes=list(key.shape[:-3]			) + [key_chunk_size, num_heads, k_features]						,  )

              # julienne value array
              _UpperCamelCase       :    Any					  =					jax.lax.dynamic_slice(
                  operand=lowercase_						,  start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]						,  slice_sizes=list(value.shape[:-3]			) + [key_chunk_size, num_heads, v_features]						,  )

              return summarize_chunk(lowercase_						,  lowercase_						,  lowercase_			)

       _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    int					  =					jax.lax.map(f=lowercase_						,  xs=jnp.arange(0						,  lowercase_						,  lowercase_			)			)

       _UpperCamelCase       :    Optional[int]					  =					jnp.max(lowercase_						,  axis=0						,  keepdims=lowercase_			)
       _UpperCamelCase       :    Dict					  =					jnp.exp(chunk_max - global_max			)

       chunk_values *= jnp.expand_dims(lowercase_						,  axis=-1			)
       chunk_weights *= max_diffs

       _UpperCamelCase       :    int					  =					chunk_values.sum(axis=0			)
       _UpperCamelCase       :    List[str]					  =					jnp.expand_dims(lowercase_						,  -1			).sum(axis=0			)

       return all_values / all_weights


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[Any]						,  lowercase_:		int						,  lowercase_:		Optional[int]						,  lowercase_:		Tuple=jax.lax.Precision.HIGHEST						,  lowercase_:		int = 1024						,  lowercase_:		int = 4096			):
       _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    int					  =					query.shape[-3:]

       def chunk_scanner(lowercase_:		List[str]						,  lowercase_:		Tuple			):
              # julienne query array
              _UpperCamelCase       :    int					  =					jax.lax.dynamic_slice(
                  operand=lowercase_						,  start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]						,  slice_sizes=list(query.shape[:-3]			) + [min(lowercase_						,  lowercase_			), num_heads, q_features]						,  )

              return (
                  chunk_idx + query_chunk_size,  # unused ignore it
                  _query_chunk_attention(
                      query=lowercase_						,  key=lowercase_						,  value=lowercase_						,  precision=lowercase_						,  key_chunk_size=lowercase_			),
              )

       _UpperCamelCase  , _UpperCamelCase       :    Dict					  =					jax.lax.scan(
           f=lowercase_						,  init=0						,  xs=lowercase_						,  length=math.ceil(num_q / query_chunk_size			)			)

       return jnp.concatenate(lowercase_						,  axis=-3			)  # fuse the chunked result back




class 				__lowerCAmelCase					(    nn.Module  ):



 '''simple docstring'''
 _lowerCamelCase  :		int
 _lowerCamelCase  :		int			      =	8
 _lowerCamelCase  :		int			      =	64
 _lowerCamelCase  :		float			      =	0.0
 _lowerCamelCase  :		bool			      =	False
 _lowerCamelCase  :		jnp.dtype			      =	jnp.floataa

 def    __snake_case   (     self    :						Optional[int]					)	->							Optional[Any]:
        _UpperCamelCase       :    Dict					  =					self.dim_head * self.heads
        _UpperCamelCase       :    Optional[int]					  =					self.dim_head**-0.5

        # Weights were exported with old names {to_q, to_k, to_v, to_out}
        _UpperCamelCase       :    Dict					  =					nn.Dense(lowerCamelCase    ,     use_bias=lowerCamelCase    ,     dtype=self.dtype    ,     name='to_q'					)
        _UpperCamelCase       :    Optional[Any]					  =					nn.Dense(lowerCamelCase    ,     use_bias=lowerCamelCase    ,     dtype=self.dtype    ,     name='to_k'					)
        _UpperCamelCase       :    Union[str, Any]					  =					nn.Dense(lowerCamelCase    ,     use_bias=lowerCamelCase    ,     dtype=self.dtype    ,     name='to_v'					)

        _UpperCamelCase       :    Optional[Any]					  =					nn.Dense(self.query_dim    ,     dtype=self.dtype    ,     name='to_out_0'					)
        _UpperCamelCase       :    int					  =					nn.Dropout(rate=self.dropout					)

 def    __snake_case   (     self    :						Dict    ,     lowerCamelCase    :						Union[str, Any]					)	->							List[str]:
        _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    Union[str, Any]					  =					tensor.shape
        _UpperCamelCase       :    Optional[int]					  =					self.heads
        _UpperCamelCase       :    Union[str, Any]					  =					tensor.reshape(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     dim // head_size					)
        _UpperCamelCase       :    Dict					  =					jnp.transpose(lowerCamelCase    ,     (0, 2, 1, 3)					)
        _UpperCamelCase       :    int					  =					tensor.reshape(batch_size * head_size    ,     lowerCamelCase    ,     dim // head_size					)
        return tensor

 def    __snake_case   (     self    :						Dict    ,     lowerCamelCase    :						Dict					)	->							Union[str, Any]:
        _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    str					  =					tensor.shape
        _UpperCamelCase       :    Dict					  =					self.heads
        _UpperCamelCase       :    Dict					  =					tensor.reshape(batch_size // head_size    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					jnp.transpose(lowerCamelCase    ,     (0, 2, 1, 3)					)
        _UpperCamelCase       :    List[Any]					  =					tensor.reshape(batch_size // head_size    ,     lowerCamelCase    ,     dim * head_size					)
        return tensor

 def __call__(     self    :						List[Any]    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						List[Any]=None    ,     lowerCamelCase    :						Union[str, Any]=True					)	->							Any:
        _UpperCamelCase       :    Union[str, Any]					  =					hidden_states if context is None else context

        _UpperCamelCase       :    Optional[int]					  =					self.query(lowerCamelCase					)
        _UpperCamelCase       :    List[Any]					  =					self.key(lowerCamelCase					)
        _UpperCamelCase       :    Optional[int]					  =					self.value(lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					self.reshape_heads_to_batch_dim(lowerCamelCase					)
        _UpperCamelCase       :    List[str]					  =					self.reshape_heads_to_batch_dim(lowerCamelCase					)
        _UpperCamelCase       :    Union[str, Any]					  =					self.reshape_heads_to_batch_dim(lowerCamelCase					)

        if self.use_memory_efficient_attention:
               _UpperCamelCase       :    List[str]					  =					query_states.transpose(1    ,     0    ,     2					)
               _UpperCamelCase       :    List[str]					  =					key_states.transpose(1    ,     0    ,     2					)
               _UpperCamelCase       :    List[Any]					  =					value_states.transpose(1    ,     0    ,     2					)

               # this if statement create a chunk size for each layer of the unet
               # the chunk size is equal to the query_length dimension of the deepest layer of the unet

               _UpperCamelCase       :    List[Any]					  =					query_states.shape[-3]
               if flatten_latent_dim % 6_4 == 0:
                      _UpperCamelCase       :    Tuple					  =					int(flatten_latent_dim / 6_4					)
               elif flatten_latent_dim % 1_6 == 0:
                      _UpperCamelCase       :    Tuple					  =					int(flatten_latent_dim / 1_6					)
               elif flatten_latent_dim % 4 == 0:
                      _UpperCamelCase       :    Tuple					  =					int(flatten_latent_dim / 4					)
               else:
                      _UpperCamelCase       :    Optional[int]					  =					int(lowerCamelCase					)

               _UpperCamelCase       :    Dict					  =					jax_memory_efficient_attention(
                   lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     query_chunk_size=lowerCamelCase    ,     key_chunk_size=4_0_9_6 * 4					)

               _UpperCamelCase       :    int					  =					hidden_states.transpose(1    ,     0    ,     2					)
        else:
               # compute attentions
               _UpperCamelCase       :    int					  =					jnp.einsum('b i d, b j d->b i j'    ,     lowerCamelCase    ,     lowerCamelCase					)
               _UpperCamelCase       :    Union[str, Any]					  =					attention_scores * self.scale
               _UpperCamelCase       :    Tuple					  =					nn.softmax(lowerCamelCase    ,     axis=2					)

               # attend to values
               _UpperCamelCase       :    Any					  =					jnp.einsum('b i j, b j d -> b i d'    ,     lowerCamelCase    ,     lowerCamelCase					)

        _UpperCamelCase       :    Tuple					  =					self.reshape_batch_dim_to_heads(lowerCamelCase					)
        _UpperCamelCase       :    Tuple					  =					self.proj_attn(lowerCamelCase					)
        return self.dropout_layer(lowerCamelCase    ,     deterministic=lowerCamelCase					)







class 				__lowerCAmelCase					(    nn.Module  ):



 '''simple docstring'''
 _lowerCamelCase  :		int
 _lowerCamelCase  :		int
 _lowerCamelCase  :		int
 _lowerCamelCase  :		float			      =	0.0
 _lowerCamelCase  :		bool			      =	False
 _lowerCamelCase  :		jnp.dtype			      =	jnp.floataa
 _lowerCamelCase  :		bool			      =	False

 def    __snake_case   (     self    :						Dict					)	->							Tuple:
        # self attention (or cross_attention if only_cross_attention is True)
        _UpperCamelCase       :    Tuple					  =					FlaxAttention(
            self.dim    ,     self.n_heads    ,     self.d_head    ,     self.dropout    ,     self.use_memory_efficient_attention    ,     dtype=self.dtype					)
        # cross attention
        _UpperCamelCase       :    List[str]					  =					FlaxAttention(
            self.dim    ,     self.n_heads    ,     self.d_head    ,     self.dropout    ,     self.use_memory_efficient_attention    ,     dtype=self.dtype					)
        _UpperCamelCase       :    Any					  =					FlaxFeedForward(dim=self.dim    ,     dropout=self.dropout    ,     dtype=self.dtype					)
        _UpperCamelCase       :    Dict					  =					nn.LayerNorm(epsilon=1e-5    ,     dtype=self.dtype					)
        _UpperCamelCase       :    Any					  =					nn.LayerNorm(epsilon=1e-5    ,     dtype=self.dtype					)
        _UpperCamelCase       :    Optional[Any]					  =					nn.LayerNorm(epsilon=1e-5    ,     dtype=self.dtype					)
        _UpperCamelCase       :    str					  =					nn.Dropout(rate=self.dropout					)

 def __call__(     self    :						Optional[int]    ,     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						List[str]    ,     lowerCamelCase    :						Optional[Any]=True					)	->							int:
        # self attention
        _UpperCamelCase       :    Any					  =					hidden_states
        if self.only_cross_attention:
               _UpperCamelCase       :    Any					  =					self.attna(self.norma(lowerCamelCase					)    ,     lowerCamelCase    ,     deterministic=lowerCamelCase					)
        else:
               _UpperCamelCase       :    Union[str, Any]					  =					self.attna(self.norma(lowerCamelCase					)    ,     deterministic=lowerCamelCase					)
        _UpperCamelCase       :    int					  =					hidden_states + residual

        # cross attention
        _UpperCamelCase       :    Union[str, Any]					  =					hidden_states
        _UpperCamelCase       :    Tuple					  =					self.attna(self.norma(lowerCamelCase					)    ,     lowerCamelCase    ,     deterministic=lowerCamelCase					)
        _UpperCamelCase       :    List[Any]					  =					hidden_states + residual

        # feed forward
        _UpperCamelCase       :    int					  =					hidden_states
        _UpperCamelCase       :    Dict					  =					self.ff(self.norma(lowerCamelCase					)    ,     deterministic=lowerCamelCase					)
        _UpperCamelCase       :    List[str]					  =					hidden_states + residual

        return self.dropout_layer(lowerCamelCase    ,     deterministic=lowerCamelCase					)







class 				__lowerCAmelCase					(    nn.Module  ):



 '''simple docstring'''
 _lowerCamelCase  :		int
 _lowerCamelCase  :		int
 _lowerCamelCase  :		int
 _lowerCamelCase  :		int			      =	1
 _lowerCamelCase  :		float			      =	0.0
 _lowerCamelCase  :		bool			      =	False
 _lowerCamelCase  :		bool			      =	False
 _lowerCamelCase  :		jnp.dtype			      =	jnp.floataa
 _lowerCamelCase  :		bool			      =	False

 def    __snake_case   (     self    :						Tuple					)	->							List[Any]:
        _UpperCamelCase       :    List[Any]					  =					nn.GroupNorm(num_groups=3_2    ,     epsilon=1e-5					)

        _UpperCamelCase       :    Tuple					  =					self.n_heads * self.d_head
        if self.use_linear_projection:
               _UpperCamelCase       :    int					  =					nn.Dense(lowerCamelCase    ,     dtype=self.dtype					)
        else:
               _UpperCamelCase       :    Optional[Any]					  =					nn.Conv(
                   lowerCamelCase    ,     kernel_size=(1, 1)    ,     strides=(1, 1)    ,     padding='VALID'    ,     dtype=self.dtype    ,     )

        _UpperCamelCase       :    Any					  =					[
            FlaxBasicTransformerBlock(
                lowerCamelCase    ,     self.n_heads    ,     self.d_head    ,     dropout=self.dropout    ,     only_cross_attention=self.only_cross_attention    ,     dtype=self.dtype    ,     use_memory_efficient_attention=self.use_memory_efficient_attention    ,     )
            for _ in range(self.depth					)
        ]

        if self.use_linear_projection:
               _UpperCamelCase       :    List[str]					  =					nn.Dense(lowerCamelCase    ,     dtype=self.dtype					)
        else:
               _UpperCamelCase       :    Any					  =					nn.Conv(
                   lowerCamelCase    ,     kernel_size=(1, 1)    ,     strides=(1, 1)    ,     padding='VALID'    ,     dtype=self.dtype    ,     )

        _UpperCamelCase       :    Optional[int]					  =					nn.Dropout(rate=self.dropout					)

 def __call__(     self    :						List[Any]    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Dict=True					)	->							Optional[int]:
        _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase  , _UpperCamelCase       :    Optional[int]					  =					hidden_states.shape
        _UpperCamelCase       :    Optional[Any]					  =					hidden_states
        _UpperCamelCase       :    int					  =					self.norm(lowerCamelCase					)
        if self.use_linear_projection:
               _UpperCamelCase       :    int					  =					hidden_states.reshape(lowerCamelCase    ,     height * width    ,     lowerCamelCase					)
               _UpperCamelCase       :    Tuple					  =					self.proj_in(lowerCamelCase					)
        else:
               _UpperCamelCase       :    Optional[Any]					  =					self.proj_in(lowerCamelCase					)
               _UpperCamelCase       :    List[Any]					  =					hidden_states.reshape(lowerCamelCase    ,     height * width    ,     lowerCamelCase					)

        for transformer_block in self.transformer_blocks:
               _UpperCamelCase       :    Optional[Any]					  =					transformer_block(lowerCamelCase    ,     lowerCamelCase    ,     deterministic=lowerCamelCase					)

        if self.use_linear_projection:
               _UpperCamelCase       :    int					  =					self.proj_out(lowerCamelCase					)
               _UpperCamelCase       :    Tuple					  =					hidden_states.reshape(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)
        else:
               _UpperCamelCase       :    Dict					  =					hidden_states.reshape(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					)
               _UpperCamelCase       :    Optional[int]					  =					self.proj_out(lowerCamelCase					)

        _UpperCamelCase       :    Any					  =					hidden_states + residual
        return self.dropout_layer(lowerCamelCase    ,     deterministic=lowerCamelCase					)







class 				__lowerCAmelCase					(    nn.Module  ):



 '''simple docstring'''
 _lowerCamelCase  :		int
 _lowerCamelCase  :		float			      =	0.0
 _lowerCamelCase  :		jnp.dtype			      =	jnp.floataa

 def    __snake_case   (     self    :						Any					)	->							Union[str, Any]:
        # The second linear layer needs to be called
        # net_2 for now to match the index of the Sequential layer
        _UpperCamelCase       :    Dict					  =					FlaxGEGLU(self.dim    ,     self.dropout    ,     self.dtype					)
        _UpperCamelCase       :    Any					  =					nn.Dense(self.dim    ,     dtype=self.dtype					)

 def __call__(     self    :						int    ,     lowerCamelCase    :						Dict    ,     lowerCamelCase    :						List[str]=True					)	->							Dict:
        _UpperCamelCase       :    List[Any]					  =					self.net_a(lowerCamelCase    ,     deterministic=lowerCamelCase					)
        _UpperCamelCase       :    List[Any]					  =					self.net_a(lowerCamelCase					)
        return hidden_states







class 				__lowerCAmelCase					(    nn.Module  ):



 '''simple docstring'''
 _lowerCamelCase  :		int
 _lowerCamelCase  :		float			      =	0.0
 _lowerCamelCase  :		jnp.dtype			      =	jnp.floataa

 def    __snake_case   (     self    :						Dict					)	->							Dict:
        _UpperCamelCase       :    str					  =					self.dim * 4
        _UpperCamelCase       :    Optional[Any]					  =					nn.Dense(inner_dim * 2    ,     dtype=self.dtype					)
        _UpperCamelCase       :    Tuple					  =					nn.Dropout(rate=self.dropout					)

 def __call__(     self    :						List[str]    ,     lowerCamelCase    :						Tuple    ,     lowerCamelCase    :						Tuple=True					)	->							int:
        _UpperCamelCase       :    Tuple					  =					self.proj(lowerCamelCase					)
        _UpperCamelCase  , _UpperCamelCase       :    str					  =					jnp.split(lowerCamelCase    ,     2    ,     axis=2					)
        return self.dropout_layer(hidden_linear * nn.gelu(lowerCamelCase					)    ,     deterministic=lowerCamelCase					)




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					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     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



