


"""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__						)	->		str:

       '''simple docstring'''




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







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Dict      					=    "sshleifer/tiny-gpt2"
       snake_case    :      Optional[Any]      					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,	training=UpperCamelCase__						,	inference=UpperCamelCase__						,	sequence_lengths=[8]						,	batch_sizes=[1]						,	multi_process=UpperCamelCase__						,	)
       snake_case    :      Optional[Any]      					=    PyTorchBenchmark(UpperCamelCase__						)
       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  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      int      					=    "sgugger/tiny-distilbert-classification"
       snake_case    :      Optional[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    :      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						)	->		str:

       '''simple docstring'''




       snake_case    :      Any      					=    "sshleifer/tiny-gpt2"
       snake_case    :      List[Any]      					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,	training=UpperCamelCase__						,	inference=UpperCamelCase__						,	torchscript=UpperCamelCase__						,	sequence_lengths=[8]						,	batch_sizes=[1]						,	multi_process=UpperCamelCase__						,	)
       snake_case    :      Optional[Any]      					=    PyTorchBenchmark(UpperCamelCase__						)
       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						)







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

       '''simple docstring'''




       snake_case    :      str      					=    "sshleifer/tiny-gpt2"
       snake_case    :      Tuple      					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,	training=UpperCamelCase__						,	inference=UpperCamelCase__						,	fpaa=UpperCamelCase__						,	sequence_lengths=[8]						,	batch_sizes=[1]						,	multi_process=UpperCamelCase__						,	)
       snake_case    :      Tuple      					=    PyTorchBenchmark(UpperCamelCase__						)
       snake_case    :      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						)	->		List[Any]:

       '''simple docstring'''




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

       '''simple docstring'''




       snake_case    :      Tuple      					=    "sshleifer/tinier_bart"
       snake_case    :      Union[str, Any]      					=    AutoConfig.from_pretrained(UpperCamelCase__						)
       snake_case    :      Tuple      					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,	training=UpperCamelCase__						,	inference=UpperCamelCase__						,	sequence_lengths=[8]						,	batch_sizes=[1]						,	multi_process=UpperCamelCase__						,	)
       snake_case    :      Tuple      					=    PyTorchBenchmark(UpperCamelCase__						,	configs=[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  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Any      					=    "sshleifer/tiny-gpt2"
       snake_case    :      str      					=    AutoConfig.from_pretrained(UpperCamelCase__						)
       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__						,	configs=[config]						)
       snake_case    :      Optional[Any]      					=    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]:

       '''simple docstring'''




       snake_case    :      Dict      					=    "sshleifer/tinier_bart"
       snake_case    :      Union[str, Any]      					=    AutoConfig.from_pretrained(UpperCamelCase__						)
       snake_case    :      List[Any]      					=    PyTorchBenchmarkArguments(
           models=[MODEL_ID]						,	training=UpperCamelCase__						,	inference=UpperCamelCase__						,	sequence_lengths=[8]						,	batch_sizes=[1]						,	multi_process=UpperCamelCase__						,	)
       snake_case    :      Tuple      					=    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    :      Tuple      					=    "sshleifer/tiny-gpt2"
       with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case    :      Optional[Any]      					=    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						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[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    :      List[Any]      					=    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    :      Optional[int]      					=    PyTorchBenchmark(UpperCamelCase__						)
           snake_case    :      Union[str, 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()						)









"""simple docstring"""



from typing import List

import numpy as np







def   __lowerCAmelCase						(						lowercase  :  dict )	->						int:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase						,							lowercase )}
    if len(set(lists_lengths.values() ) ) > 1:
        raise RuntimeError(
            (
                "Sharding is ambiguous for this dataset: "
                + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
                + "\n".join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
                + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
                + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
            ) )
    snake_case    :      int      					=    max(lists_lengths.values()						,							default=0 )
    return max(1						,							lowercase )







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int )	->						List[range]:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    []
    for group_idx in range(lowercase ):
        snake_case    :      Union[str, Any]      					=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        snake_case    :      int      					=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        snake_case    :      Dict      					=    range(lowercase						,							start + num_shards_to_add )
        shards_indices_per_group.append(lowercase )
    return shards_indices_per_group







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  int )	->						List[dict]:


    """simple docstring"""





    snake_case    :      int      					=    _number_of_shards_in_gen_kwargs(lowercase )
    if num_shards == 1:
        return [dict(lowercase )]
    else:
        snake_case    :      Optional[int]      					=    _distribute_shards(num_shards=lowercase						,							max_num_jobs=lowercase )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(lowercase						,							lowercase )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(lowercase ) )
        ]







def   __lowerCAmelCase						(						lowercase  :  List[dict] )	->						dict:


    """simple docstring"""





    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key]						,							lowercase )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }







def   __lowerCAmelCase						(						lowercase  :  np.random.Generator						,							lowercase  :  dict )	->						dict:


    """simple docstring"""





    snake_case    :      Tuple      					=    {len(lowercase ) for value in gen_kwargs.values() if isinstance(lowercase						,							lowercase )}
    snake_case    :      str      					=    {}
    for size in list_sizes:
        snake_case    :      Optional[int]      					=    list(range(lowercase ) )
        rng.shuffle(indices_per_size[size] )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    snake_case    :      Dict      					=    dict(lowercase )
    for key, value in shuffled_kwargs.items():
        if isinstance(lowercase						,							lowercase ):
            snake_case    :      Dict      					=    [value[i] for i in indices_per_size[len(lowercase )]]
    return shuffled_kwargs





