



"""simple docstring"""
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def    _snake_case		(		UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Optional[Any]="attention"		):
 UpperCAmelCase			:      List[Any]			=			params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
 UpperCAmelCase			:      Dict			=			params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
 UpperCAmelCase			:      Optional[int]			=			params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
 UpperCAmelCase			:      int			=			params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
 return k, o, q, v

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Optional[int]=False		):
 if split_mlp_wi:
  UpperCAmelCase			:      str			=			params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
  UpperCAmelCase			:      List[str]			=			params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
  UpperCAmelCase			:      List[str]			=			(wi_a, wi_a)
 else:
  UpperCAmelCase			:      Tuple			=			params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

 UpperCAmelCase			:      int			=			params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
 return wi, wo

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	int    ,	UpperCamelCase		:	Any		):
 return params[F"{prefix}/layers_{i}/{layer_name}/scale"]

def    _snake_case		(		UpperCamelCase		:	dict    ,	*, UpperCamelCase		:	int    ,	UpperCamelCase		:	bool		):
 UpperCAmelCase			:      List[Any]			=			traverse_util.flatten_dict(variables["""target"""]		)
 UpperCAmelCase			:      Union[str, Any]			=			{"""/""".join(UpperCamelCase		): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 UpperCAmelCase			:      List[Any]			=			"""encoder/layers_0/mlp/wi_0/kernel""" in old
 print("""Split MLP:"""    ,	UpperCamelCase		)

 UpperCAmelCase			:      List[Any]			=			collections.OrderedDict()

 # Shared embeddings.
 UpperCAmelCase			:      str			=			old["""token_embedder/embedding"""]

 # Encoder.
 for i in range(UpperCamelCase		):
  # Block i, layer 0 (Self Attention).
  UpperCAmelCase			:      Optional[int]			=			tax_layer_norm_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""encoder"""    ,	"""pre_attention_layer_norm"""		)
  UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      List[str]			=			tax_attention_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""encoder"""    ,	"""attention"""		)
  UpperCAmelCase			:      Tuple			=			layer_norm
  UpperCAmelCase			:      List[str]			=			k.T
  UpperCAmelCase			:      Optional[int]			=			o.T
  UpperCAmelCase			:      int			=			q.T
  UpperCAmelCase			:      Optional[int]			=			v.T

  # Block i, layer 1 (MLP).
  UpperCAmelCase			:      int			=			tax_layer_norm_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""encoder"""    ,	"""pre_mlp_layer_norm"""		)
  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			tax_mlp_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""encoder"""    ,	UpperCamelCase		)
  UpperCAmelCase			:      Any			=			layer_norm
  if split_mlp_wi:
   UpperCAmelCase			:      Tuple			=			wi[0].T
   UpperCAmelCase			:      Optional[int]			=			wi[1].T
  else:
   UpperCAmelCase			:      int			=			wi.T
  UpperCAmelCase			:      Union[str, Any]			=			wo.T

 UpperCAmelCase			:      int			=			old[
     """encoder/relpos_bias/rel_embedding"""
 ].T
 UpperCAmelCase			:      List[str]			=			old["""encoder/encoder_norm/scale"""]

 if not is_encoder_only:
  # Decoder.
  for i in range(UpperCamelCase		):
   # Block i, layer 0 (Self Attention).
   UpperCAmelCase			:      str			=			tax_layer_norm_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""decoder"""    ,	"""pre_self_attention_layer_norm"""		)
   UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			tax_attention_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""decoder"""    ,	"""self_attention"""		)
   UpperCAmelCase			:      int			=			layer_norm
   UpperCAmelCase			:      List[str]			=			k.T
   UpperCAmelCase			:      str			=			o.T
   UpperCAmelCase			:      Optional[Any]			=			q.T
   UpperCAmelCase			:      Optional[Any]			=			v.T

   # Block i, layer 1 (Cross Attention).
   UpperCAmelCase			:      Any			=			tax_layer_norm_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""decoder"""    ,	"""pre_cross_attention_layer_norm"""		)
   UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Any			=			tax_attention_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""decoder"""    ,	"""encoder_decoder_attention"""		)
   UpperCAmelCase			:      List[str]			=			layer_norm
   UpperCAmelCase			:      Optional[int]			=			k.T
   UpperCAmelCase			:      List[Any]			=			o.T
   UpperCAmelCase			:      Optional[int]			=			q.T
   UpperCAmelCase			:      Tuple			=			v.T

   # Block i, layer 2 (MLP).
   UpperCAmelCase			:      str			=			tax_layer_norm_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""decoder"""    ,	"""pre_mlp_layer_norm"""		)
   UpperCAmelCase			,	UpperCAmelCase			:      str			=			tax_mlp_lookup(UpperCamelCase    ,	UpperCamelCase    ,	"""decoder"""    ,	UpperCamelCase		)
   UpperCAmelCase			:      Union[str, Any]			=			layer_norm
   if split_mlp_wi:
    UpperCAmelCase			:      Union[str, Any]			=			wi[0].T
    UpperCAmelCase			:      Union[str, Any]			=			wi[1].T
   else:
    UpperCAmelCase			:      Tuple			=			wi.T
   UpperCAmelCase			:      List[Any]			=			wo.T

  UpperCAmelCase			:      Union[str, Any]			=			old["""decoder/decoder_norm/scale"""]
  UpperCAmelCase			:      Optional[int]			=			old[
      """decoder/relpos_bias/rel_embedding"""
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   UpperCAmelCase			:      Optional[Any]			=			old["""decoder/logits_dense/kernel"""].T

 return new

def    _snake_case		(		UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	bool		):
 UpperCAmelCase			:      Any			=			collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  UpperCAmelCase			:      Tuple			=			state_dict["""shared.weight"""]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   UpperCAmelCase			:      Optional[Any]			=			state_dict["""shared.weight"""]

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print("""Using shared word embeddings as lm_head."""		)
   UpperCAmelCase			:      Optional[int]			=			state_dict["""shared.weight"""]

 return state_dict

def    _snake_case		(		UpperCamelCase		:	Any    ,	UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Any		):
 UpperCAmelCase			:      int			=			checkpoints.load_tax_checkpoint(UpperCamelCase		)
 UpperCAmelCase			:      Dict			=			convert_tax_to_pytorch(UpperCamelCase    ,	num_layers=config.num_layers    ,	is_encoder_only=UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			make_state_dict(UpperCamelCase    ,	UpperCamelCase		)
 model.load_state_dict(UpperCamelCase    ,	strict=UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	bool = False		):
 UpperCAmelCase			:      str			=			TaConfig.from_json_file(UpperCamelCase		)
 print(F"Building PyTorch model from configuration: {config}"		)
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  UpperCAmelCase			:      List[str]			=			TaEncoderModel(UpperCamelCase		)
 else:
  UpperCAmelCase			:      str			=			TaForConditionalGeneration(UpperCamelCase		)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"		)
 model.save_pretrained(UpperCamelCase		)

 # Verify that we can load the checkpoint.
 model.from_pretrained(UpperCamelCase		)
 print("""Done"""		)


if __name__ == "__main__":
      A:     Dict										=	argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
      # Required parameters
      parser.add_argument(
          "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
      )
      A:     List[str]										=	parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )





"""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    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    for model_result in results.values():
     for batch_size, sequence_length in zip(model_result["""bs"""]   ,  model_result["""ss"""]		):
      UpperCAmelCase			:      List[str]			=			model_result["""result"""][batch_size][sequence_length]
      self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Optional[Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  torchscript=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""
    UpperCAmelCase			:      Union[str, Any]			=			PyTorchBenchmarkArguments(
        models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Dict			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




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

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





    '''simple docstring'''




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

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





    '''simple docstring'''




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

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





    '''simple docstring'''




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




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





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			"""sshleifer/tiny-gpt2"""

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

    with tempfile.TemporaryDirectory() as tmp_dir:
     UpperCAmelCase			:      List[str]			=			PyTorchBenchmarkArguments(
         models=[MODEL_ID]   ,  training=_SCREAMING_SNAKE_CASE   ,  inference=_SCREAMING_SNAKE_CASE   ,  sequence_lengths=[8]   ,  batch_sizes=[1]   ,  log_filename=os.path.join(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)   ,  log_print=_SCREAMING_SNAKE_CASE   ,  trace_memory_line_by_line=_SCREAMING_SNAKE_CASE   ,  multi_process=_SCREAMING_SNAKE_CASE   ,  )
     UpperCAmelCase			:      Any			=			PyTorchBenchmark(_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      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(_SCREAMING_SNAKE_CASE   ,  """log.txt"""		)		).exists()		)
