





import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
  if model.config.model_type == "gpt2":
    return model.transformer.h[0].mlp.c_fc
  return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
      import torch
      import torch.nn as nn







      class     __snake_case       (							nn.Module			):





             '''simple docstring'''







             def __init__(  self :    Optional[Any]							,				A :    nn.Module							,				A :    int		):
               super().__init__()
               __snake_case:						Optional[int]   =						module
               __snake_case:						Optional[Any]   =						nn.Sequential(
                   nn.Linear(module.in_features							,				A							,				bias=A		)							,				nn.Linear(A							,				module.out_features							,				bias=A		)							,				)
               __snake_case:						List[Any]   =						(2.0 / (5 * min(module.in_features							,				module.out_features		))) ** 0.5
               nn.init.normal_(self.adapter[0].weight							,				std=A		)
               nn.init.zeros_(self.adapter[1].weight		)
               self.adapter.to(module.weight.device		)






             def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    int							,				*A :    Dict							,				**A :    int		):
               return self.module(A							,				*A							,				**A		) + self.adapter(A		)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """bigscience/bloom-1b7"""

       # Constant values
       lowerCAmelCase__      		=     2.109_659_552_692_574

       lowerCAmelCase__      		=     """Hello my name is"""
       lowerCAmelCase__      		=     set()
       EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""			)
       EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""			)
       EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""			)
       lowerCAmelCase__      		=     10




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         # Models and tokenizer
         __snake_case:						Tuple   =						AutoTokenizer.from_pretrained(self.model_name		)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    str		):
         super().setUp()

         # Models and tokenizer
         __snake_case:						Tuple   =						AutoModelForCausalLM.from_pretrained(
             self.model_name							,				torch_dtype=torch.floataa							,				device_map="""auto"""		)
         __snake_case:						List[str]   =						AutoModelForCausalLM.from_pretrained(self.model_name							,				load_in_abit=A							,				device_map="""auto"""		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         del self.model_fpaa
         del self.model_abit

         gc.collect()
         torch.cuda.empty_cache()




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						str   =						self.model_abit.config

         self.assertTrue(hasattr(A							,				"""quantization_config"""		)		)

         __snake_case:						int   =						config.to_dict()
         __snake_case:						str   =						config.to_diff_dict()

         __snake_case:						List[Any]   =						config.to_json_string()




       def 							UpperCAmelCase__ (  self :    Any		):
         from bitsandbytes.nn import Paramsabit

         __snake_case:						Dict   =						self.model_fpaa.get_memory_footprint()
         __snake_case:						Tuple   =						self.model_abit.get_memory_footprint()

         self.assertAlmostEqual(mem_fpaa / mem_abit							,				self.EXPECTED_RELATIVE_DIFFERENCE		)
         __snake_case:						Optional[int]   =						get_some_linear_layer(self.model_abit		)
         self.assertTrue(linear.weight.__class__ == Paramsabit		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         from transformers import TaPreTrainedModel

         self.model_fpaa.get_memory_footprint()
         self.model_abit.get_memory_footprint()

         for name, module in self.model_abit.named_modules():
           if isinstance(A							,				torch.nn.Linear		):
             if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
               # 4-bit parameters are packed in uint8 variables
               self.assertTrue(module.weight.dtype == torch.uinta		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[Any]   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		)
         __snake_case:						Dict   =						self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0		)							,				max_new_tokens=10		)

         self.assertIn(self.tokenizer.decode(output_sequences[0]							,				skip_special_tokens=A		)							,				self.EXPECTED_OUTPUTS		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						BitsAndBytesConfig()
         __snake_case:						Any   =						True

         __snake_case:						Optional[Any]   =						AutoModelForCausalLM.from_pretrained(
             self.model_name							,				quantization_config=A							,				device_map="""auto"""		)

         __snake_case:						Tuple   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		)
         __snake_case:						List[str]   =						model_abit_from_config.generate(
             input_ids=encoded_input["""input_ids"""].to(0		)							,				max_new_tokens=10		)

         self.assertIn(self.tokenizer.decode(output_sequences[0]							,				skip_special_tokens=A		)							,				self.EXPECTED_OUTPUTS		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         with self.assertRaises(A		), tempfile.TemporaryDirectory() as tmpdirname:
           self.model_abit.save_pretrained(A		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						List[Any]   =						BitsAndBytesConfig()

         with self.assertRaises(A		):
           __snake_case:						str   =						AutoModelForCausalLM.from_pretrained(
               self.model_name							,				quantization_config=A							,				load_in_abit=A							,				device_map="""auto"""							,				bnb_abit_quant_type="""nf4"""							,				)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         with self.assertRaises(A		):
           # Tries with `str`
           self.model_abit.to("""cpu"""		)

         with self.assertRaises(A		):
           # Tries with a `dtype``
           self.model_abit.to(torch.floataa		)

         with self.assertRaises(A		):
           # Tries with a `device`
           self.model_abit.to(torch.device("""cuda:0"""		)		)

         with self.assertRaises(A		):
           # Tries with a `device`
           self.model_abit.float()

         with self.assertRaises(A		):
           # Tries with a `device`
           self.model_abit.half()

         # Test if we did not break anything
         __snake_case:						Optional[Any]   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		)

         __snake_case:						Union[str, Any]   =						self.model_fpaa.to(torch.floataa		)
         __snake_case:						Dict   =						self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0		)							,				max_new_tokens=10		)

         # Check this does not throw an error
         __snake_case:						Union[str, Any]   =						self.model_fpaa.to("""cpu"""		)

         # Check this does not throw an error
         __snake_case:						Tuple   =						self.model_fpaa.half()

         # Check this does not throw an error
         __snake_case:						Any   =						self.model_fpaa.float()






       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						List[str]   =						AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""							,				load_in_abit=A							,				device_map="""auto"""		)
         self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa		)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @classmethod
       def 							UpperCAmelCase__ (  cls :    List[Any]		):
         __snake_case:						str   =						"""t5-small"""
         __snake_case:						Dict   =						"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
         __snake_case:						Union[str, Any]   =						AutoTokenizer.from_pretrained(cls.model_name		)
         __snake_case:						Optional[int]   =						"""Translate in German: Hello, my dog is cute"""




       def 							UpperCAmelCase__ (  self :    Any		):
         gc.collect()
         torch.cuda.empty_cache()




       def 							UpperCAmelCase__ (  self :    str		):
         from transformers import TaForConditionalGeneration

         __snake_case:						str   =						TaForConditionalGeneration._keep_in_fpaa_modules
         __snake_case:						List[str]   =						None

         # test with `t5-small`
         __snake_case:						Tuple   =						TaForConditionalGeneration.from_pretrained(self.model_name							,				load_in_abit=A							,				device_map="""auto"""		)
         __snake_case:						Any   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		).to(0		)
         __snake_case:						str   =						model.generate(**A		)

         # test with `flan-t5-small`
         __snake_case:						List[str]   =						TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name							,				load_in_abit=A							,				device_map="""auto"""		)
         __snake_case:						Dict   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		).to(0		)
         __snake_case:						Union[str, Any]   =						model.generate(**A		)
         __snake_case:						Optional[int]   =						modules






       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         __snake_case:						List[Any]   =						TaForConditionalGeneration.from_pretrained(self.model_name							,				load_in_abit=A							,				device_map="""auto"""		)

         # there was a bug with decoders - this test checks that it is fixed
         self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q							,				bnb.nn.Linearabit		)		)

         __snake_case:						Union[str, Any]   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		).to(0		)
         __snake_case:						Optional[Any]   =						model.generate(**A		)

         # test with `flan-t5-small`
         __snake_case:						Optional[int]   =						TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name							,				load_in_abit=A							,				device_map="""auto"""		)
         __snake_case:						Any   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		).to(0		)
         __snake_case:						Tuple   =						model.generate(**A		)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Dict		):
         super().setUp()
         # model_name
         __snake_case:						List[Any]   =						"""bigscience/bloom-560m"""
         __snake_case:						Any   =						"""t5-small"""

         # Different types of model

         __snake_case:						Any   =						AutoModel.from_pretrained(self.model_name							,				load_in_abit=A							,				device_map="""auto"""		)
         # Sequence classification model
         __snake_case:						Any   =						AutoModelForSequenceClassification.from_pretrained(
             self.model_name							,				load_in_abit=A							,				device_map="""auto"""		)
         # CausalLM model
         __snake_case:						List[Any]   =						AutoModelForCausalLM.from_pretrained(self.model_name							,				load_in_abit=A							,				device_map="""auto"""		)
         # Seq2seq model
         __snake_case:						str   =						AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name							,				load_in_abit=A							,				device_map="""auto"""		)




       def 							UpperCAmelCase__ (  self :    str		):
         del self.base_model
         del self.sequence_model
         del self.model_abit
         del self.seq_to_seq_model

         gc.collect()
         torch.cuda.empty_cache()






       def 							UpperCAmelCase__ (  self :    Dict		):
         from bitsandbytes.nn import Paramsabit

         self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit		)

         # Other heads should be nn.Parameter
         self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter		)
         self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter		)
         self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter		)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    List[str]		):
         super().setUp()




       def 							UpperCAmelCase__ (  self :    Tuple		):
         del self.pipe

         gc.collect()
         torch.cuda.empty_cache()






       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						Optional[Any]   =						pipeline(
             """text-generation"""							,				model=self.model_name							,				model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}							,				max_new_tokens=self.MAX_NEW_TOKENS							,				)

         # Real second forward pass
         __snake_case:						int   =						self.pipe(self.input_text		)
         self.assertIn(pipeline_output[0]["""generated_text"""]							,				self.EXPECTED_OUTPUTS		)







@require_torch_multi_gpu
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    int		):
         super().setUp()






       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						List[Any]   =						AutoModelForCausalLM.from_pretrained(
             self.model_name							,				load_in_abit=A							,				device_map="""balanced"""		)

         # Check correct device map
         self.assertEqual(set(model_parallel.hf_device_map.values()		)							,				{0, 1}		)

         # Check that inference pass works on the model
         __snake_case:						List[Any]   =						self.tokenizer(self.input_text							,				return_tensors="""pt"""		)

         # Second real batch
         __snake_case:						Dict   =						model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0		)							,				max_new_tokens=10		)
         self.assertIn(self.tokenizer.decode(output_parallel[0]							,				skip_special_tokens=A		)							,				self.EXPECTED_OUTPUTS		)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Tuple   =						"""facebook/opt-350m"""
         super().setUp()






       def 							UpperCAmelCase__ (  self :    Any		):
         if version.parse(importlib.metadata.version("""bitsandbytes"""		)		) < version.parse("""0.37.0"""		):
           return

         # Step 1: freeze all parameters
         __snake_case:						Tuple   =						AutoModelForCausalLM.from_pretrained(self.model_name							,				load_in_abit=A		)

         self.assertEqual(set(model.hf_device_map.values()		)							,				{torch.cuda.current_device()}		)

         for param in model.parameters():
           __snake_case:						Union[str, Any]   =						False  # freeze the model - train adapters later
           if param.ndim == 1:
             # cast the small parameters (e.g. layernorm) to fp32 for stability
             __snake_case:						List[Any]   =						param.data.to(torch.floataa		)

        # Step 2: add adapters
         for _, module in model.named_modules():
           if "OPTAttention" in repr(type(A		)		):
             __snake_case:						Optional[Any]   =						LoRALayer(module.q_proj							,				rank=16		)
             __snake_case:						int   =						LoRALayer(module.k_proj							,				rank=16		)
             __snake_case:						int   =						LoRALayer(module.v_proj							,				rank=16		)

        # Step 3: dummy batch
         __snake_case:						Any   =						self.tokenizer("""Test batch """							,				return_tensors="""pt"""		).to(0		)

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
           __snake_case:						Tuple   =						model.forward(**A		)
           out.logits.norm().backward()

         for module in model.modules():
           if isinstance(A							,				A		):
             self.assertTrue(module.adapter[1].weight.grad is not None		)
             self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0		)
           elif isinstance(A							,				nn.Embedding		):
             self.assertTrue(module.weight.grad is None		)







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """gpt2-xl"""
       lowerCAmelCase__      		=     3.3_191_854_854_152_187













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 							UpperCAmelCase__ (  self :    Any							,				A :    Union[str, Any]		):
         for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["""bs"""]							,				model_result["""ss"""]		):
             __snake_case:						Dict   =						model_result["""result"""][batch_size][sequence_length]
             self.assertIsNotNone(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Dict   =						TensorFlowBenchmark(A		)
         __snake_case:						List[str]   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Optional[int]   =						"""sgugger/tiny-distilbert-classification"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				only_pretrain_model=A							,				)
         __snake_case:						Any   =						TensorFlowBenchmark(A		)
         __snake_case:						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 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						int   =						TensorFlowBenchmark(A		)
         __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 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						int   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				eager_mode=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A							,				[config]		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)




       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Optional[int]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Tuple   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						str   =						TensorFlowBenchmark(A							,				[config]		)
         __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 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						Tuple   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						str   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
         __snake_case:						Tuple   =						benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Dict   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						Union[str, Any]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						Optional[int]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						List[str]   =						TensorFlowBenchmark(A							,				[config]		)
         __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		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						List[Any]   =						"""patrickvonplaten/t5-tiny-random"""
         __snake_case:						List[str]   =						AutoConfig.from_pretrained(A		)
         __snake_case:						List[str]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				multi_process=A							,				)
         __snake_case:						Optional[int]   =						TensorFlowBenchmark(A							,				configs=[config]		)
         __snake_case:						Tuple   =						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 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						"""sshleifer/tiny-gpt2"""
         __snake_case:						List[Any]   =						TensorFlowBenchmarkArguments(
             models=[MODEL_ID]							,				training=A							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				use_xla=A							,				multi_process=A							,				)
         __snake_case:						Union[str, Any]   =						TensorFlowBenchmark(A		)
         __snake_case:						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 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						List[str]   =						"""sshleifer/tiny-gpt2"""
         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						int   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				save_to_csv=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				inference_time_csv_file=os.path.join(A							,				"""inf_time.csv"""		)							,				inference_memory_csv_file=os.path.join(A							,				"""inf_mem.csv"""		)							,				env_info_csv_file=os.path.join(A							,				"""env.csv"""		)							,				multi_process=A							,				)
           __snake_case:						Tuple   =						TensorFlowBenchmark(A		)
           benchmark.run()
           self.assertTrue(Path(os.path.join(A							,				"""inf_time.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""inf_mem.csv"""		)		).exists()		)
           self.assertTrue(Path(os.path.join(A							,				"""env.csv"""		)		).exists()		)






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						Any   =						"""sshleifer/tiny-gpt2"""

         def _check_summary_is_not_empty(A :    Union[str, Any]		):
           self.assertTrue(hasattr(A							,				"""sequential"""		)		)
           self.assertTrue(hasattr(A							,				"""cumulative"""		)		)
           self.assertTrue(hasattr(A							,				"""current"""		)		)
           self.assertTrue(hasattr(A							,				"""total"""		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           __snake_case:						Union[str, Any]   =						TensorFlowBenchmarkArguments(
               models=[MODEL_ID]							,				inference=A							,				sequence_lengths=[8]							,				batch_sizes=[1]							,				log_filename=os.path.join(A							,				"""log.txt"""		)							,				log_print=A							,				trace_memory_line_by_line=A							,				eager_mode=A							,				multi_process=A							,				)
           __snake_case:						Dict   =						TensorFlowBenchmark(A		)
           __snake_case:						List[str]   =						benchmark.run()
           _check_summary_is_not_empty(result.inference_summary		)
           self.assertTrue(Path(os.path.join(A							,				"""log.txt"""		)		).exists()		)






