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


SCREAMING_SNAKE_CASE__		:					Tuple						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					List[Any]						=					{
    "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       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'trajectory_transformer'
  lowercase_			=     ['past_key_values']
  lowercase_			=     {
      'hidden_size': 'n_embd',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }




  def __init__(      self     :	Dict     , a_     :	List[str]=100     , a_     :	str=5     , a_     :	Optional[Any]=1     , a_     :	str=1     , a_     :	List[str]=249     , a_     :	List[Any]=6     , a_     :	Tuple=17     , a_     :	Any=25     , a_     :	Optional[Any]=4     , a_     :	Tuple=4     , a_     :	int=128     , a_     :	Union[str, Any]=0.1     , a_     :	Optional[Any]=0.1     , a_     :	Tuple=0.1     , a_     :	str=0.0006     , a_     :	Optional[Any]=512     , a_     :	Optional[int]=0.02     , a_     :	Optional[Any]=1e-1_2     , a_     :	Any=1     , a_     :	int=True     , a_     :	Optional[int]=1     , a_     :	Optional[Any]=5_0256     , a_     :	Dict=5_0256     , **a_     :	Optional[Any]     , )->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		action_weight
       SCREAMING_SNAKE_CASE__				:				int			=		reward_weight
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		value_weight
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		max_position_embeddings
       SCREAMING_SNAKE_CASE__				:				List[str]			=		block_size
       SCREAMING_SNAKE_CASE__				:				int			=		action_dim
       SCREAMING_SNAKE_CASE__				:				Any			=		observation_dim
       SCREAMING_SNAKE_CASE__				:				Tuple			=		transition_dim
       SCREAMING_SNAKE_CASE__				:				int			=		learning_rate
       SCREAMING_SNAKE_CASE__				:				str			=		n_layer
       SCREAMING_SNAKE_CASE__				:				Dict			=		n_head
       SCREAMING_SNAKE_CASE__				:				List[str]			=		n_embd
       SCREAMING_SNAKE_CASE__				:				int			=		embd_pdrop
       SCREAMING_SNAKE_CASE__				:				str			=		attn_pdrop
       SCREAMING_SNAKE_CASE__				:				Tuple			=		resid_pdrop
       SCREAMING_SNAKE_CASE__				:				str			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				Any			=		layer_norm_eps
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		kaiming_initializer_range
       SCREAMING_SNAKE_CASE__				:				int			=		use_cache
       super().__init__(pad_token_id=a_     , bos_token_id=a_     , eos_token_id=a_     , **a_				)




# Copyright 2023 The HuggingFace 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.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



