




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     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






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





           '''simple docstring'''

           for model_result in results.values():
                  for batch_size, sequence_length in zip(model_result['''bs'''] ,			model_result['''ss''']):
                         A__							=   model_result['''result'''][batch_size][sequence_length]
                         self.assertIsNotNone(UpperCAmelCase__)


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





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


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





           '''simple docstring'''

           A__							=   '''sgugger/tiny-distilbert-classification'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			only_pretrain_model=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


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





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			torchscript=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   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				:     Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			fpaa=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   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)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           # set architectures equal to `None`
           A__							=   None
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   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)						->List[str]:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   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				:     Optional[Any])						->str:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			fpaa=UpperCAmelCase__ ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__)
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)


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





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


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





           '''simple docstring'''

           A__							=   '''sshleifer/tinier_bart'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result)
           self.check_results_dict_not_empty(results.memory_inference_result)


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





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   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[str])						->List[str]:





           '''simple docstring'''

           A__							=   '''sshleifer/tinier_bart'''
           A__							=   AutoConfig.from_pretrained(UpperCAmelCase__)
           A__							=   PyTorchBenchmarkArguments(
               models=[MODEL_ID] ,			training=UpperCAmelCase__ ,			inference=UpperCAmelCase__ ,			sequence_lengths=[8] ,			batch_sizes=[1] ,			multi_process=UpperCAmelCase__ ,			)
           A__							=   PyTorchBenchmark(UpperCAmelCase__ ,			configs=[config])
           A__							=   benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result)
           self.check_results_dict_not_empty(results.memory_train_result)


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





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''
           with tempfile.TemporaryDirectory() as tmp_dir:
                  A__							=   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__ ,			)
                  A__							=   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				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   '''sshleifer/tiny-gpt2'''

           def _check_summary_is_not_empty(UpperCAmelCase__				:     Tuple):
                  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:
                  A__							=   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__ ,			)
                  A__							=   PyTorchBenchmark(UpperCAmelCase__)
                  A__							=   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())








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


