





'''simple docstring'''




import os

import numpy
import onnx





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Union[str, Any]		)		->  Dict:
   snake_case 						=   a.name
   snake_case 						=   b.name

   snake_case 						=   """"""
   snake_case 						=   """"""

   snake_case 						=   a == b

   snake_case 						=   name_a
   snake_case 						=   name_b

   return res





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							int		)		->  Optional[Any]:
   for i, input_name in enumerate(node_proto.input		):
      if input_name == name:
         node_proto.input.insert(__lowerCAmelCase      ,    __lowerCAmelCase		)
         node_proto.input.pop(i + 1		)

   if node_proto.op_type == "If":
      _graph_replace_input_with(node_proto.attribute[0].g      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
      _graph_replace_input_with(node_proto.attribute[1].g      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   if node_proto.op_type == "Loop":
      _graph_replace_input_with(node_proto.attribute[0].g      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							int		)		->  List[Any]:
   for n in graph_proto.node:
      _node_replace_input_with(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[int]      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Dict		)		->  Tuple:
   snake_case 						=   list(model.graph.initializer		)
   snake_case 						=   list(model_without_ext.graph.initializer		)
   for i, ref_i in ind_to_replace:
      assert inits_with_data[i].name == inits[i].name
      assert inits_with_data[ref_i].name == inits[ref_i].name
      assert i > ref_i

      snake_case 						=   inits[i].name
      snake_case 						=   inits[ref_i].name

      model_without_ext.graph.initializer.remove(inits[i]		)

      # for n in model.graph.node:
      _graph_replace_input_with(model_without_ext.graph      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  Dict:
   snake_case 						=   os.path.dirname(__lowerCAmelCase		)
   snake_case 						=   os.path.basename(__lowerCAmelCase		)

   snake_case 						=   onnx.load(os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)		)

   snake_case 						=   list(model.graph.initializer		)

   snake_case 						=   set()
   snake_case 						=   {}
   snake_case 						=   []

   snake_case 						=   0

   for i in range(len(__lowerCAmelCase		)		):
      if i in dup_set:
         continue

      for j in range(i + 1      ,    len(__lowerCAmelCase		)		):
         if j in dup_set:
            continue
         if _is_equal_tensor_proto(inits[i]      ,    inits[j]		):
            dup_set.add(__lowerCAmelCase		)
            dup_set.add(__lowerCAmelCase		)

            snake_case 						=   inits[j].data_type
            snake_case 						=   numpy.prod(inits[j].dims		)
            if dtype == 1:
               mem_size *= 4
            elif dtype == 6:
               mem_size *= 4
            elif dtype == 7 or dtype == 11:
               mem_size *= 8
            else:
               print("""unexpected data type: """      ,    __lowerCAmelCase		)
            total_reduced_size += mem_size

            snake_case 						=   inits[i].name
            snake_case 						=   inits[j].name

            if name_i in dup_map:
               dup_map[name_i].append(__lowerCAmelCase		)
            else:
               snake_case 						=   [name_j]
            ind_to_replace.append((j, i)		)

   print("""total reduced size: """      ,    total_reduced_size / 10_24 / 10_24 / 10_24      ,    """GB"""		)

   snake_case 						=   sorted(__lowerCAmelCase		)
   _remove_dup_initializers_from_model(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)

   snake_case 						=   """optimized_""" + model_file_name
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    __lowerCAmelCase		)
   onnx.save(__lowerCAmelCase      ,    __lowerCAmelCase		)

   return new_model








'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

