


"""simple docstring"""



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


__snake_case         =					logging.get_logger(__name__)

__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       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''trajectory_transformer'''
   __UpperCAmelCase				: List[Any]													=						['''past_key_values''']
   __UpperCAmelCase				: List[str]													=						{
       '''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__=0.0006		,			UpperCamelCase__=512		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=True		,			UpperCamelCase__=1		,			UpperCamelCase__=5_0256		,			UpperCamelCase__=5_0256		,			**UpperCamelCase__		,			)			->	int:

       '''simple docstring'''




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









"""simple docstring"""



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
  from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




       for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["bs"]		,			model_result["ss"]		):
               snake_case					:   Optional[int]         =					model_result["result"][batch_size][sequence_length]
               self.assertIsNotNone(UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Dict         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   str         =					"sgugger/tiny-distilbert-classification"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			only_pretrain_model=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Tuple         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			torchscript=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   int         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   @unittest.skipIf(torch_device == "cpu"		,			"Cant do half precision"		)
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			fpaa=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   str         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tiny-gpt2"
       snake_case					:   int         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       # set architectures equal to `None`
       snake_case					:   str         =					None
       snake_case					:   List[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   Optional[Any]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   List[str]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   Dict         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   @unittest.skipIf(torch_device == "cpu"		,			"Can't do half precision"		)
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   Optional[Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			fpaa=UpperCamelCase__		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		)
       snake_case					:   str         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   Tuple         =					"sshleifer/tiny-gpt2"
       snake_case					:   List[Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					"sshleifer/tinier_bart"
       snake_case					:   List[str]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Optional[int]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   Optional[Any]         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_inference_result		)
       self.check_results_dict_not_empty(results.memory_inference_result		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					"sshleifer/tiny-gpt2"
       snake_case					:   str         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Optional[int]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''simple docstring'''




       snake_case					:   str         =					"sshleifer/tinier_bart"
       snake_case					:   Union[str, Any]         =					AutoConfig.from_pretrained(UpperCamelCase__		)
       snake_case					:   Any         =					PyTorchBenchmarkArguments(
           models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			multi_process=UpperCamelCase__		,			)
       snake_case					:   Union[str, Any]         =					PyTorchBenchmark(UpperCamelCase__		,			configs=[config]		)
       snake_case					:   int         =					benchmark.run()
       self.check_results_dict_not_empty(results.time_train_result		)
       self.check_results_dict_not_empty(results.memory_train_result		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Any         =					"sshleifer/tiny-gpt2"
       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case					:   Optional[int]         =					PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			save_to_csv=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			inference_time_csv_file=os.path.join(UpperCamelCase__		,			"inf_time.csv"		)		,			train_memory_csv_file=os.path.join(UpperCamelCase__		,			"train_mem.csv"		)		,			inference_memory_csv_file=os.path.join(UpperCamelCase__		,			"inf_mem.csv"		)		,			train_time_csv_file=os.path.join(UpperCamelCase__		,			"train_time.csv"		)		,			env_info_csv_file=os.path.join(UpperCamelCase__		,			"env.csv"		)		,			multi_process=UpperCamelCase__		,			)
           snake_case					:   List[Any]         =					PyTorchBenchmark(UpperCamelCase__		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"inf_time.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"train_time.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"inf_mem.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"train_mem.csv"		)		).exists()		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"env.csv"		)		).exists()		)







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					"sshleifer/tiny-gpt2"

       def _check_summary_is_not_empty(UpperCamelCase__		):
           self.assertTrue(hasattr(UpperCamelCase__		,			"sequential"		)		)
           self.assertTrue(hasattr(UpperCamelCase__		,			"cumulative"		)		)
           self.assertTrue(hasattr(UpperCamelCase__		,			"current"		)		)
           self.assertTrue(hasattr(UpperCamelCase__		,			"total"		)		)

       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case					:   Tuple         =					PyTorchBenchmarkArguments(
               models=[MODEL_ID]		,			training=UpperCamelCase__		,			inference=UpperCamelCase__		,			sequence_lengths=[8]		,			batch_sizes=[1]		,			log_filename=os.path.join(UpperCamelCase__		,			"log.txt"		)		,			log_print=UpperCamelCase__		,			trace_memory_line_by_line=UpperCamelCase__		,			multi_process=UpperCamelCase__		,			)
           snake_case					:   Dict         =					PyTorchBenchmark(UpperCamelCase__		)
           snake_case					:   List[Any]         =					benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           _check_summary_is_not_empty(result.train_summary		)
           self.assertTrue(Path(os.path.join(UpperCamelCase__		,			"log.txt"		)		).exists()		)





