


'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_						:       Any      			=				logging.get_logger(__name__)

a_						:       Tuple      			=				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
a_						:       List[str]      			=				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def __snake_case      (   UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Optional[int]	):
 for attribute in key.split("."	):
  lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	)

 if weight_type is not None:
  lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	).shape
 else:
  lowerCamelCase_  				=						hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
      F''' {value.shape} for {full_name}'''	)

 if weight_type == "weight":
  lowerCamelCase_  				=						value
 elif weight_type == "weight_g":
  lowerCamelCase_  				=						value
 elif weight_type == "weight_v":
  lowerCamelCase_  				=						value
 elif weight_type == "bias":
  lowerCamelCase_  				=						value
 elif weight_type == "running_mean":
  lowerCamelCase_  				=						value
 elif weight_type == "running_var":
  lowerCamelCase_  				=						value
 elif weight_type == "num_batches_tracked":
  lowerCamelCase_  				=						value
 elif weight_type == "inv_freq":
  lowerCamelCase_  				=						value
 else:
  lowerCamelCase_  				=						value

 logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict	):
 lowerCamelCase_  				=						[]
 lowerCamelCase_  				=						fairseq_model.state_dict()

 lowerCamelCase_  				=						hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase_  				=						False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       hf_model.config.feat_extract_norm == "group"      ,       )
   lowerCamelCase_  				=						True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase_  				=						"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("w2v_model."	)[-1] == name.split("."	)[0]:
     lowerCamelCase_  				=						True
     if "*" in mapped_key:
      lowerCamelCase_  				=						name.split(UpperCAmelCase_	)[0].split("."	)[-2]
      lowerCamelCase_  				=						mapped_key.replace("*"      ,       UpperCAmelCase_	)
     if "pos_bias_u" in name:
      lowerCamelCase_  				=						None
     elif "pos_bias_v" in name:
      lowerCamelCase_  				=						None
     elif "weight_g" in name:
      lowerCamelCase_  				=						"weight_g"
     elif "weight_v" in name:
      lowerCamelCase_  				=						"weight_v"
     elif "bias" in name:
      lowerCamelCase_  				=						"bias"
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      lowerCamelCase_  				=						"weight"
     elif "running_mean" in name:
      lowerCamelCase_  				=						"running_mean"
     elif "inv_freq" in name:
      lowerCamelCase_  				=						"inv_freq"
     elif "running_var" in name:
      lowerCamelCase_  				=						"running_var"
     elif "num_batches_tracked" in name:
      lowerCamelCase_  				=						"num_batches_tracked"
     else:
      lowerCamelCase_  				=						None
     set_recursively(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)
    continue
  if not is_used:
   unused_weights.append(UpperCAmelCase_	)

 logger.warning(F'''Unused weights: {unused_weights}'''	)





def __snake_case      (   UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_  				=						full_name.split("conv_layers."	)[-1]
 lowerCamelCase_  				=						name.split("."	)
 lowerCamelCase_  				=						int(items[0]	)
 lowerCamelCase_  				=						int(items[1]	)

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
   lowerCamelCase_  				=						value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
   lowerCamelCase_  				=						value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''	)
   lowerCamelCase_  				=						value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''	)
   lowerCamelCase_  				=						value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
 else:
  unused_weights.append(UpperCAmelCase_	)





@torch.no_grad()
def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		str=None      ,       UpperCAmelCase_		:		Any=None      ,       UpperCAmelCase_		:		Any=True	):
 if config_path is not None:
  lowerCamelCase_  				=						WavaVecaConformerConfig.from_pretrained(UpperCAmelCase_      ,       hidden_act="swish"	)
 else:
  lowerCamelCase_  				=						WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  lowerCamelCase_  				=						"rotary"

 if is_finetuned:
  if dict_path:
   lowerCamelCase_  				=						Dictionary.load(UpperCAmelCase_	)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase_  				=						target_dict.pad_index
   lowerCamelCase_  				=						target_dict.bos_index
   lowerCamelCase_  				=						target_dict.eos_index
   lowerCamelCase_  				=						len(target_dict.symbols	)
   lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "vocab.json"	)
   if not os.path.isdir(UpperCAmelCase_	):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_	)	)
    return
   os.makedirs(UpperCAmelCase_      ,       exist_ok=UpperCAmelCase_	)
   lowerCamelCase_  				=						target_dict.indices

   # fairseq has the <pad> and <s> switched
   lowerCamelCase_  				=						0
   lowerCamelCase_  				=						1
   with open(UpperCAmelCase_      ,       "w"      ,       encoding="utf-8"	) as vocab_handle:
    json.dump(UpperCAmelCase_      ,       UpperCAmelCase_	)
   lowerCamelCase_  				=						WavaVecaCTCTokenizer(
       UpperCAmelCase_      ,       unk_token=target_dict.unk_word      ,       pad_token=target_dict.pad_word      ,       bos_token=target_dict.bos_word      ,       eos_token=target_dict.eos_word      ,       word_delimiter_token="|"      ,       do_lower_case=UpperCAmelCase_      ,       )
   lowerCamelCase_  				=						True if config.feat_extract_norm == "layer" else False
   lowerCamelCase_  				=						WavaVecaFeatureExtractor(
       feature_size=1      ,       sampling_rate=16000      ,       padding_value=0      ,       do_normalize=UpperCAmelCase_      ,       return_attention_mask=UpperCAmelCase_      ,       )
   lowerCamelCase_  				=						WavaVecaProcessor(feature_extractor=UpperCAmelCase_      ,       tokenizer=UpperCAmelCase_	)
   processor.save_pretrained(UpperCAmelCase_	)

  lowerCamelCase_  				=						WavaVecaConformerForCTC(UpperCAmelCase_	)
 else:
  lowerCamelCase_  				=						WavaVecaConformerForPreTraining(UpperCAmelCase_	)

 if is_finetuned:
  lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]      ,       arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	)}	)
 else:
  lowerCamelCase_  				=						argparse.Namespace(task="audio_pretraining"	)
  lowerCamelCase_  				=						fairseq.tasks.setup_task(UpperCAmelCase_	)

  lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,       task=UpperCAmelCase_	)

 lowerCamelCase_  				=						model[0].eval()

 recursively_load_weights(UpperCAmelCase_      ,       UpperCAmelCase_      ,       not is_finetuned	)

 hf_wavavec.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
     a_						:       int      			=				argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     a_						:       Optional[int]      			=				parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )







'''simple docstring'''

import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   for model_result in results.values():
    for batch_size, sequence_length in zip(model_result["bs"]    ,						model_result["ss"]  ):
     lowerCamelCase_  				=						model_result["result"][batch_size][sequence_length]
     self.assertIsNotNone(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sgugger/tiny-distilbert-classification"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						only_pretrain_model=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result  )
   self.check_results_dict_not_empty(results.memory_train_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_train_result  )
   self.check_results_dict_not_empty(results.memory_train_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"patrickvonplaten/t5-tiny-random"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						configs=[config]  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"  )  ) == 0    ,						"Cannot do xla on CPU."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						use_xla=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
   lowerCamelCase_  				=						benchmark.run()
   self.check_results_dict_not_empty(results.time_inference_result  )
   self.check_results_dict_not_empty(results.memory_inference_result  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"sshleifer/tiny-gpt2"
   with tempfile.TemporaryDirectory() as tmp_dir:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						inference=UpperCamelCase    ,						save_to_csv=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(UpperCamelCase    ,						"inf_time.csv"  )    ,						inference_memory_csv_file=os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )    ,						env_info_csv_file=os.path.join(UpperCamelCase    ,						"env.csv"  )    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    benchmark.run()
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_time.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"inf_mem.csv"  )  ).exists()  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"env.csv"  )  ).exists()  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"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:
    lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
        models=[MODEL_ID]    ,						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    ,						eager_mode=UpperCamelCase    ,						multi_process=UpperCamelCase    ,						)
    lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase  )
    lowerCamelCase_  				=						benchmark.run()
    _check_summary_is_not_empty(result.inference_summary  )
    self.assertTrue(Path(os.path.join(UpperCamelCase    ,						"log.txt"  )  ).exists()  )



