






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 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Optional[Any]					,   __UpperCAmelCase :		int    )       ->List[str]:




       """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  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Dict:




       """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  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Optional[int]:




       """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  __lowerCAmelCase   ( self :		int    )       ->List[Any]:




       """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  __lowerCAmelCase   ( self :		List[Any]    )       ->Optional[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  __lowerCAmelCase   ( self :		str    )       ->str:




       """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  __lowerCAmelCase   ( self :		Optional[int]    )       ->Dict:




       """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  __lowerCAmelCase   ( self :		Optional[int]    )       ->List[Any]:




       """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  __lowerCAmelCase   ( 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  __lowerCAmelCase   ( self :		Any    )       ->Tuple:




       """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  __lowerCAmelCase   ( 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_train_result    )
       self.check_results_dict_not_empty(results.memory_train_result    )







      def  __lowerCAmelCase   ( self :		List[str]    )       ->Dict:




       """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  __lowerCAmelCase   ( self :		List[Any]    )       ->Dict:




       """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  __lowerCAmelCase   ( self :		Any    )       ->Optional[Any]:




       """simple docstring"""


       a						    = '''sshleifer/tiny-gpt2'''

       def _check_summary_is_not_empty(__UpperCAmelCase :		str    ):
        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()    )










from __future__ import annotations






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		int    )       ->None:




       """simple docstring"""


       a						    = order

       # a_{0} ... a_{k}
       a						    = [1.0] + [0.0] * order
       # b_{0} ... b_{k}
       a						    = [1.0] + [0.0] * order

       # x[n-1] ... x[n-k]
       a						    = [0.0] * self.order
       # y[n-1] ... y[n-k]
       a						    = [0.0] * self.order







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		list[float]					,   __UpperCAmelCase :		list[float]    )       ->None:




       """simple docstring"""


       if len(__UpperCAmelCase    ) < self.order:
        a						    = [1.0, *a_coeffs]

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected a_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       if len(__UpperCAmelCase    ) != self.order + 1:
        a						    = (
            F"""Expected b_coeffs to have {self.order + 1} elements """
            F"""for {self.order}-order filter, got {len(__UpperCAmelCase    )}"""
        )
        raise ValueError(__UpperCAmelCase    )

       a						    = a_coeffs
       a						    = b_coeffs







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		float    )       ->float:




       """simple docstring"""


       a						    = 0.0

       # Start at index 1 and do index 0 at the end.
       for i in range(1					,   self.order + 1    ):
        result += (
            self.b_coeffs[i] * self.input_history[i - 1]
            - self.a_coeffs[i] * self.output_history[i - 1]
        )

       a						    = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

       a						    = self.input_history[:-1]
       a						    = self.output_history[:-1]

       a						    = sample
       a						    = result

       return result


