




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 							_UpperCAmelCase  (UpperCamelCase_ : Dict      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : Any       ):
				'''simple docstring'''
				return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : Union[str, Any]      ,    UpperCamelCase_ : Any      ,    UpperCamelCase_ : Union[str, Any]="attention"       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[Any]										=      np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]       )
				_lowerCAmelCase    :    Union[str, Any]										=      k_tmp.reshape(k_tmp.shape[0]      ,    k_tmp.shape[1] * k_tmp.shape[2]       )
				_lowerCAmelCase    :    Union[str, Any]										=      np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]       )
				_lowerCAmelCase    :    Optional[Any]										=      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,    o_tmp.shape[2]       )
				_lowerCAmelCase    :    int										=      np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]       )
				_lowerCAmelCase    :    Any										=      q_tmp.reshape(q_tmp.shape[0]      ,    q_tmp.shape[1] * q_tmp.shape[2]       )
				_lowerCAmelCase    :    int										=      np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]       )
				_lowerCAmelCase    :    Optional[Any]										=      v_tmp.reshape(v_tmp.shape[0]      ,    v_tmp.shape[1] * v_tmp.shape[2]       )
				return k, o, q, v




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : List[str]=False       ):
				'''simple docstring'''
				if split_mlp_wi:
								_lowerCAmelCase    :    List[Any]										=      params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
								_lowerCAmelCase    :    Tuple										=      params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
								_lowerCAmelCase    :    Union[str, Any]										=      (wi_a, wi_a)
				else:
								_lowerCAmelCase    :    Any										=      params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

				_lowerCAmelCase    :    Any										=      params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
				return wi, wo




def 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Any      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]




def 							_UpperCAmelCase  (UpperCamelCase_ : dict      ,    *, UpperCamelCase_ : int      ,    UpperCamelCase_ : bool      ,    UpperCamelCase_ : bool = False       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      traverse_util.flatten_dict(variables["""target"""]       )
				_lowerCAmelCase    :    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
				_lowerCAmelCase    :    Optional[Any]										=      """encoder/encoder/mlp/wi_0/kernel""" in old
				print("""Split MLP:"""      ,    UpperCamelCase_       )

				_lowerCAmelCase    :    Any										=      collections.OrderedDict()

				# Shared embeddings.
				_lowerCAmelCase    :    List[Any]										=      old["""token_embedder/embedding"""]

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

								# Block i, layer 1 (MLP).
								_lowerCAmelCase    :    Tuple										=      tax_layer_norm_lookup(UpperCamelCase_      ,    UpperCamelCase_      ,    """encoder"""      ,    """pre_mlp_layer_norm"""       )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Optional[int]										=      tax_mlp_lookup(UpperCamelCase_      ,    UpperCamelCase_      ,    """encoder"""      ,    UpperCamelCase_       )
								_lowerCAmelCase    :    Optional[Any]										=      layer_norm
								if split_mlp_wi:
												_lowerCAmelCase    :    str										=      wi[0].T
												_lowerCAmelCase    :    Optional[int]										=      wi[1].T
								else:
												_lowerCAmelCase    :    str										=      wi.T
								_lowerCAmelCase    :    Dict										=      wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												_lowerCAmelCase    :    List[Any]										=      tax_relpos_bias_lookup(
												    UpperCamelCase_      ,    UpperCamelCase_      ,    """encoder"""       ).T

				_lowerCAmelCase    :    str										=      old["""encoder/encoder_norm/scale"""]

				if not scalable_attention:
								_lowerCAmelCase    :    Dict										=      tax_relpos_bias_lookup(
								    UpperCamelCase_      ,    0      ,    """encoder"""       ).T
								_lowerCAmelCase    :    List[str]										=      tax_relpos_bias_lookup(
								    UpperCamelCase_      ,    0      ,    """decoder"""       ).T

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

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

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

												if scalable_attention:
																# convert the rel_embedding of each layer
																_lowerCAmelCase    :    int										=      tax_relpos_bias_lookup(UpperCamelCase_      ,    UpperCamelCase_      ,    """decoder"""       ).T

								_lowerCAmelCase    :    Union[str, Any]										=      old["""decoder/decoder_norm/scale"""]

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

				return new




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : bool       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      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:
								_lowerCAmelCase    :    Dict										=      state_dict["""shared.weight"""]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												_lowerCAmelCase    :    Dict										=      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."""       )
												_lowerCAmelCase    :    Union[str, Any]										=      state_dict["""shared.weight"""]

				return state_dict




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : Any       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Tuple										=      checkpoints.load_tax_checkpoint(UpperCamelCase_       )
				_lowerCAmelCase    :    Dict										=      convert_tax_to_pytorch(
				    UpperCamelCase_      ,    num_layers=config.num_layers      ,    is_encoder_only=UpperCamelCase_      ,    scalable_attention=UpperCamelCase_       )
				_lowerCAmelCase    :    List[str]										=      make_state_dict(UpperCamelCase_      ,    UpperCamelCase_       )
				model.load_state_dict(UpperCamelCase_      ,    strict=UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : Dict      ,    UpperCamelCase_ : List[Any]      ,    UpperCamelCase_ : bool = False      ,    UpperCamelCase_ : bool = False      ,    ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      MTaConfig.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:
								_lowerCAmelCase    :    Any										=      UMTaEncoderModel(UpperCamelCase_       )
				else:
								_lowerCAmelCase    :    List[Any]										=      UMTaForConditionalGeneration(UpperCamelCase_       )

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(UpperCamelCase_      ,    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__":
	_lowerCamelCase      :  Optional[Any]      						=						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
	)
	parser.add_argument(
	    "--scalable_attention",
	    action="store_true",
	    help="Whether the model uses scaled attention (umt5 model)",
	    default=False,
	)
	_lowerCamelCase      :  str      						=						parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)







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        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    List[str]:





								'''simple docstring'''




								for model_result in results.values():
												for batch_size, sequence_length in zip(model_result["""bs"""]		,	model_result["""ss"""]      ):
																_lowerCAmelCase    :    List[Any]										=      model_result["""result"""][batch_size][sequence_length]
																self.assertIsNotNone(_UpperCAmelCase      )



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





								'''simple docstring'''




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





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      """sgugger/tiny-distilbert-classification"""
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	only_pretrain_model=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	torchscript=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      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      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	fpaa=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    int										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								# set architectures equal to `None`
								_lowerCAmelCase    :    Optional[int]										=      None
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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      :		Dict      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    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      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	fpaa=_UpperCAmelCase		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Union[str, Any]										=      PyTorchBenchmark(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[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      :		List[Any]      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    List[Any]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    List[str]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Tuple										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Tuple										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      """sshleifer/tiny-gpt2"""
								_lowerCAmelCase    :    Optional[int]										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      """sshleifer/tinier_bart"""
								_lowerCAmelCase    :    Dict										=      AutoConfig.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,	training=_UpperCAmelCase		,	inference=_UpperCAmelCase		,	sequence_lengths=[8]		,	batch_sizes=[1]		,	multi_process=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    Optional[Any]										=      PyTorchBenchmark(_UpperCAmelCase		,	configs=[config]      )
								_lowerCAmelCase    :    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      :		int      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      """sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
												_lowerCAmelCase    :    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		,	)
												_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      """sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(_UpperCAmelCase      :		int      ):
												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:
												_lowerCAmelCase    :    Any										=      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		,	)
												_lowerCAmelCase    :    str										=      PyTorchBenchmark(_UpperCAmelCase      )
												_lowerCAmelCase    :    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()      )

