
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'''


			_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    ,							__UpperCAmelCase=100    ,							__UpperCAmelCase=5    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase=249    ,							__UpperCAmelCase=6    ,							__UpperCAmelCase=17    ,							__UpperCAmelCase=25    ,							__UpperCAmelCase=4    ,							__UpperCAmelCase=4    ,							__UpperCAmelCase=128    ,							__UpperCAmelCase=0.1    ,							__UpperCAmelCase=0.1    ,							__UpperCAmelCase=0.1    ,							__UpperCAmelCase=0.0_006    ,							__UpperCAmelCase=512    ,							__UpperCAmelCase=0.02    ,							__UpperCAmelCase=1E-12    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase=True    ,							__UpperCAmelCase=1    ,							__UpperCAmelCase=50_256    ,							__UpperCAmelCase=50_256    ,							**__UpperCAmelCase    ,							):
										SCREAMING_SNAKE_CASE_	:							Any											=vocab_size
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=action_weight
										SCREAMING_SNAKE_CASE_	:							List[str]											=reward_weight
										SCREAMING_SNAKE_CASE_	:							int											=value_weight
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=max_position_embeddings
										SCREAMING_SNAKE_CASE_	:							str											=block_size
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=action_dim
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=observation_dim
										SCREAMING_SNAKE_CASE_	:							Any											=transition_dim
										SCREAMING_SNAKE_CASE_	:							int											=learning_rate
										SCREAMING_SNAKE_CASE_	:							Dict											=n_layer
										SCREAMING_SNAKE_CASE_	:							Tuple											=n_head
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=n_embd
										SCREAMING_SNAKE_CASE_	:							Optional[Any]											=embd_pdrop
										SCREAMING_SNAKE_CASE_	:							Optional[int]											=attn_pdrop
										SCREAMING_SNAKE_CASE_	:							Dict											=resid_pdrop
										SCREAMING_SNAKE_CASE_	:							Dict											=initializer_range
										SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=layer_norm_eps
										SCREAMING_SNAKE_CASE_	:							List[Any]											=kaiming_initializer_range
										SCREAMING_SNAKE_CASE_	:							int											=use_cache
										super().__init__(pad_token_id=__UpperCAmelCase    ,							bos_token_id=__UpperCAmelCase    ,							eos_token_id=__UpperCAmelCase    ,							**__UpperCAmelCase  )









'''simple docstring'''


import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     *lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(*lowerCamelCase			,     **lowerCamelCase		)
          __A	:       Tuple      =							eval_examples
          __A	:       List[str]      =							post_process_function


     def lowerCAmelCase__  (   self			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase = "eval"		):


          '''simple docstring'''





          __A	:       Any      =							self.eval_dataset if eval_dataset is None else eval_dataset
          __A	:       Dict      =							self.get_eval_dataloader(lowerCamelCase		)
          __A	:       str      =							self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Any      =							self.compute_metrics
          __A	:       Optional[int]      =							None
          __A	:       Tuple      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Dict      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Evaluation"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       List[str]      =							compute_metrics
          __A	:       List[Any]      =							self.args.eval_batch_size * self.args.world_size
          if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
               start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
          output.metrics.update(
              speed_metrics(
                  lowerCamelCase			,     lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size		)			,     )		)
          if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __A	:       List[Any]      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions		)
               __A	:       List[str]      =							self.compute_metrics(lowerCamelCase		)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()		):
                    if not key.startswith(f"{metric_key_prefix}_"		):
                         __A	:       Union[str, Any]      =							metrics.pop(lowerCamelCase		)
               metrics.update(output.metrics		)
          else:
               __A	:       Any      =							output.metrics

          if self.args.should_log:
               # Only the main node log the results by default
               self.log(lowerCamelCase		)

          if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report()		)

          __A	:       str      =							self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     lowerCamelCase		)
          return metrics


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase = "test"		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_test_dataloader(lowerCamelCase		)

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Union[str, Any]      =							self.compute_metrics
          __A	:       Union[str, Any]      =							None
          __A	:       Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Tuple      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Prediction"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       int      =							compute_metrics
          __A	:       List[Any]      =							self.args.eval_batch_size * self.args.world_size
          if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
               start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
          output.metrics.update(
              speed_metrics(
                  lowerCamelCase			,     lowerCamelCase			,     num_samples=output.num_samples			,     num_steps=math.ceil(output.num_samples / total_batch_size		)			,     )		)

          if self.post_process_function is None or self.compute_metrics is None:
               return output

          __A	:       Dict      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions			,     "predict"		)
          __A	:       Tuple      =							self.compute_metrics(lowerCamelCase		)

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()		):
               if not key.startswith(f"{metric_key_prefix}_"		):
                    __A	:       Any      =							metrics.pop(lowerCamelCase		)
          metrics.update(output.metrics		)
          return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=lowerCamelCase		)

