


"""simple docstring"""



from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      AutoTokenizer,
      TFAutoModelForCausalLM,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSpeechSeqaSeq,
      TFAutoModelForVisionaSeq,
      TFBartForConditionalGeneration,
      TFLogitsProcessorList,
      TFMinLengthLogitsProcessor,
      tf_top_k_top_p_filtering,
  )

if is_tensorflow_text_available():
  import tensorflow_text as text







@require_tf
class _lowerCAmelCase       (   unittest.TestCase			):







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

       '''simple docstring'''




       snake_case					:   Any         =					tf.convert_to_tensor(
           [
               [
                   8.2220991,  # 3rd highest value; idx. 0
                   -0.5620044,
                   5.23229752,
                   4.0386393,
                   -6.8798378,
                   -0.54785802,
                   -3.2012153,
                   2.92777176,
                   1.88171953,
                   7.35341276,  # 5th highest value; idx. 9
                   8.43207833,  # 2nd highest value; idx. 10
                   -9.85711836,
                   -5.96209236,
                   -1.13039161,
                   -7.1115294,
                   -0.8369633,
                   -5.3186408,
                   7.06427407,
                   0.81369344,
                   -0.82023817,
                   -5.9179796,
                   0.58813443,
                   -6.99778438,
                   4.71551189,
                   -0.18771637,
                   7.44020759,  # 4th highest value; idx. 25
                   9.38450987,  # 1st highest value; idx. 26
                   2.12662941,
                   -9.32562038,
                   2.35652522,
               ],  # cummulative prob of 5 highest values <= 0.6
               [
                   0.58425518,
                   4.53139238,
                   -5.57510464,
                   -6.28030699,
                   -7.19529503,
                   -4.02122551,
                   1.39337037,
                   -6.06707057,
                   1.59480517,
                   -9.643119,
                   0.03907799,
                   0.67231762,
                   -8.88206726,
                   6.27115922,  # 4th highest value; idx. 13
                   2.28520723,
                   4.82767506,
                   4.30421368,
                   8.8275313,  # 2nd highest value; idx. 17
                   5.44029958,  # 5th highest value; idx. 18
                   -4.4735794,
                   7.38579536,  # 3rd highest value; idx. 20
                   -2.91051663,
                   2.61946077,
                   -2.5674762,
                   -9.48959302,
                   -4.02922645,
                   -1.35416918,
                   9.67702323,  # 1st highest value; idx. 27
                   -5.89478553,
                   1.85370467,
               ],  # cummulative prob of 5 highest values <= 0.6
           ]		,			dtype=tf.floataa		,			)

       snake_case					:   Union[str, Any]         =					tf.convert_to_tensor(
           [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]		,			dtype=tf.intaa		,			)  # expected non filtered idx as noted above

       snake_case					:   str         =					tf.convert_to_tensor(
           [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023]		,			dtype=tf.floataa		,			)  # expected non filtered values as noted above

       snake_case					:   Union[str, Any]         =					tf_top_k_top_p_filtering(UpperCamelCase__		,			top_k=10		,			top_p=0.6		,			min_tokens_to_keep=4		)

       snake_case					:   Optional[int]         =					output[output != -float("inf"		)]
       snake_case					:   Any         =					tf.cast(
           tf.where(tf.not_equal(UpperCamelCase__		,			tf.constant(-float("inf"		)		,			dtype=tf.floataa		)		)		)		,			dtype=tf.intaa		,			)

       tf.debugging.assert_near(UpperCamelCase__		,			UpperCamelCase__		,			rtol=1e-12		)
       tf.debugging.assert_equal(UpperCamelCase__		,			UpperCamelCase__		)







@require_tf
class _lowerCAmelCase       (   unittest.TestCase					,							snake_case_			):
   # setting framework_dependent_parameters needs to be gated, just like its contents' imports
   if is_tf_available():
      __UpperCAmelCase				: int													=						{
          '''AutoModelForCausalLM''': TFAutoModelForCausalLM,
          '''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
          '''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
          '''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
          '''LogitsProcessorList''': TFLogitsProcessorList,
          '''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
          '''create_tensor_fn''': tf.convert_to_tensor,
          '''floats_tensor''': floats_tensor,
          '''return_tensors''': '''tf''',
      }







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

       '''simple docstring'''




       snake_case					:   List[str]         =					TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
       snake_case					:   List[Any]         =					2
       snake_case					:   Dict         =					2







       class _lowerCAmelCase       (   tf.Module			):







          def __init__(			self		,			UpperCamelCase__		)			->	int:

              '''simple docstring'''




              super(UpperCamelCase__		,			self		).__init__()
              snake_case					:   str         =					model







          @tf.function(
              input_signature=(
                  tf.TensorSpec((None, input_length)		,			tf.intaa		,			name="input_ids"		),
                  tf.TensorSpec((None, input_length)		,			tf.intaa		,			name="attention_mask"		),
              )		,			jit_compile=UpperCamelCase__		,			)
          def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Any:

              '''simple docstring'''




              snake_case					:   List[str]         =					self.model.generate(
                  input_ids=UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			max_new_tokens=UpperCamelCase__		,			return_dict_in_generate=UpperCamelCase__		,			)
              return {"sequences": outputs["sequences"]}



       snake_case					:   Optional[Any]         =					[[2, 0], [102, 103]]
       snake_case					:   str         =					[[1, 0], [1, 1]]
       snake_case					:   Tuple         =					DummyModel(model=UpperCamelCase__		)
       with tempfile.TemporaryDirectory() as tmp_dir:
           tf.saved_model.save(UpperCamelCase__		,			UpperCamelCase__		,			signatures={"serving_default": dummy_model.serving}		)
           snake_case					:   List[Any]         =					tf.saved_model.load(UpperCamelCase__		).signatures["serving_default"]
           for batch_size in range(1		,			len(UpperCamelCase__		) + 1		):
               snake_case					:   Optional[Any]         =					{
                   "input_ids": tf.constant(dummy_input_ids[:batch_size]		),
                   "attention_mask": tf.constant(dummy_attention_masks[:batch_size]		),
               }
               snake_case					:   int         =					serving_func(**UpperCamelCase__		)["sequences"]
               snake_case					:   Optional[Any]         =					test_model.generate(**UpperCamelCase__		,			max_new_tokens=UpperCamelCase__		)
               tf.debugging.assert_equal(UpperCamelCase__		,			UpperCamelCase__		)







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

       '''simple docstring'''




       snake_case					:   str         =					TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
       snake_case					:   List[Any]         =					1
       snake_case					:   int         =					2







       class _lowerCAmelCase       (   tf.Module			):







          def __init__(			self		,			UpperCamelCase__		)			->	Optional[int]:

              '''simple docstring'''




              super(UpperCamelCase__		,			self		).__init__()
              snake_case					:   Union[str, Any]         =					model







          @tf.function(
              input_signature=(
                  tf.TensorSpec((batch_size, None)		,			tf.intaa		,			name="input_ids"		),
                  tf.TensorSpec((batch_size, None)		,			tf.intaa		,			name="attention_mask"		),
              )		,			jit_compile=UpperCamelCase__		,			)
          def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Any:

              '''simple docstring'''




              snake_case					:   Dict         =					self.model.generate(
                  input_ids=UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			max_new_tokens=UpperCamelCase__		,			return_dict_in_generate=UpperCamelCase__		,			)
              return {"sequences": outputs["sequences"]}



       snake_case					:   List[str]         =					[[2], [102, 103]]
       snake_case					:   Optional[Any]         =					[[1], [1, 1]]
       snake_case					:   str         =					DummyModel(model=UpperCamelCase__		)
       with tempfile.TemporaryDirectory() as tmp_dir:
           tf.saved_model.save(UpperCamelCase__		,			UpperCamelCase__		,			signatures={"serving_default": dummy_model.serving}		)
           snake_case					:   Tuple         =					tf.saved_model.load(UpperCamelCase__		).signatures["serving_default"]
           for input_row in range(len(UpperCamelCase__		)		):
               snake_case					:   Dict         =					{
                   "input_ids": tf.constant([dummy_input_ids[input_row]]		),
                   "attention_mask": tf.constant([dummy_attention_masks[input_row]]		),
               }
               snake_case					:   Dict         =					serving_func(**UpperCamelCase__		)["sequences"]
               snake_case					:   Optional[int]         =					test_model.generate(**UpperCamelCase__		,			max_new_tokens=UpperCamelCase__		)
               tf.debugging.assert_equal(UpperCamelCase__		,			UpperCamelCase__		)







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

       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
           # file needed to load the TF tokenizer
           hf_hub_download(repo_id="google/flan-t5-small"		,			filename="spiece.model"		,			local_dir=UpperCamelCase__		)







           class _lowerCAmelCase       (   tf.keras.layers.Layer			):







              def __init__(			self		)			->	str:

                  '''simple docstring'''




                  super().__init__()
                  snake_case					:   List[str]         =					text.SentencepieceTokenizer(
                      model=tf.io.gfile.GFile(os.path.join(UpperCamelCase__		,			"spiece.model"		)		,			"rb"		).read()		)
                  snake_case					:   Tuple         =					TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5"		)







              def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	Tuple:

                  '''simple docstring'''




                  snake_case					:   Union[str, Any]         =					self.tokenizer.tokenize(UpperCamelCase__		)
                  snake_case      ,snake_case					:   List[str]         =					text.pad_model_inputs(
                      UpperCamelCase__		,			max_seq_length=64		,			pad_value=self.model.config.pad_token_id		)
                  snake_case					:   List[Any]         =					self.model.generate(input_ids=UpperCamelCase__		,			attention_mask=UpperCamelCase__		)
                  return self.tokenizer.detokenize(UpperCamelCase__		)



           snake_case					:   str         =					CompleteSentenceTransformer()
           snake_case					:   Any         =					tf.keras.layers.Input(shape=(1,)		,			dtype=tf.string		,			name="inputs"		)
           snake_case					:   List[Any]         =					complete_model(UpperCamelCase__		)
           snake_case					:   Union[str, Any]         =					tf.keras.Model(UpperCamelCase__		,			UpperCamelCase__		)
           keras_model.save(UpperCamelCase__		)







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

       '''simple docstring'''




       snake_case					:   str         =					{
           "do_sample": True,
           "num_beams": 1,
           "top_p": 0.7,
           "top_k": 10,
           "temperature": 0.7,
       }
       snake_case					:   Tuple         =					14

       snake_case					:   Tuple         =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
       snake_case					:   Optional[int]         =					"Hello, my dog is cute and"
       snake_case					:   Union[str, Any]         =					tokenizer(UpperCamelCase__		,			return_tensors="tf"		)
       snake_case					:   Optional[Any]         =					TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)

       snake_case					:   List[Any]         =					638
       # forces the generation to happen on CPU, to avoid GPU-related quirks
       with tf.device(":/CPU:0"		):
           tf.random.set_seed(0		)
           snake_case					:   List[Any]         =					model.generate(**UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)
       self.assertTrue(expectation == len(generated_tokens[0]		)		)

       snake_case					:   List[Any]         =					[638, 198]
       with tf.device(":/CPU:0"		):
           tf.random.set_seed(0		)
           snake_case					:   Union[str, Any]         =					model.generate(**UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)
       self.assertTrue(expectation == len(generated_tokens[0]		)		)







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case					:   List[Any]         =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart"		)
       snake_case					:   Union[str, Any]         =					"Hugging Face is a technology company based in New York and Paris."
       snake_case					:   Optional[int]         =					bart_tokenizer(UpperCamelCase__		,			return_tensors="tf"		).input_ids
       snake_case					:   Dict         =					TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"		)
       snake_case					:   List[Any]         =					bart_model.generate(UpperCamelCase__		).numpy()







       class _lowerCAmelCase       (   snake_case_			):







          def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=None		,			**UpperCamelCase__		)			->	List[Any]:

              '''simple docstring'''




              return super().call(UpperCamelCase__		,			**UpperCamelCase__		)



       snake_case					:   Any         =					FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart"		)
       snake_case					:   List[str]         =					bart_model.generate(UpperCamelCase__		,			foo="bar"		).numpy()
       self.assertTrue(np.array_equal(UpperCamelCase__		,			UpperCamelCase__		)		)







       class _lowerCAmelCase       (   bart_model.model.encoder.__class__			):







          def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			**UpperCamelCase__		)			->	Any:

              '''simple docstring'''




              return super().call(UpperCamelCase__		,			**UpperCamelCase__		)



       snake_case					:   str         =					FakeEncoder(bart_model.config		,			bart_model.model.shared		)
       snake_case					:   List[str]         =					fake_encoder

       # Normal generation still works (the output will be different because the encoder weights are different)
       snake_case					:   str         =					bart_model.generate(UpperCamelCase__		).numpy()
       with self.assertRaises(UpperCamelCase__		):
           # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
           bart_model.generate(UpperCamelCase__		,			foo="bar"		)









"""simple docstring"""



import os
import tempfile
import unittest
from pathlib import Path

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


if is_torch_available():
  from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments







@require_torch
class _lowerCAmelCase       (   unittest.TestCase			):







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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







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

       '''simple docstring'''




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







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

       '''simple docstring'''




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







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

       '''simple docstring'''




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







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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




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







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

       '''simple docstring'''




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







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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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







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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




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

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

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





