'''simple docstring'''

import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


A_     :						str    							='''http://www.mocksite.com/file1.txt'''
A_     :						Dict    							='''"text": ["foo", "foo"]'''
A_     :						List[Any]    							='''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''


class     __UpperCAmelCase       :
  __A :				List[Any]											= 2_00
  __A :				Tuple											= {'Content-Length': '100'}
  __A :				Any											= {}





  def 						UpperCAmelCase_	(	self    ,     **_lowerCamelCase ):
     return [bytes(_lowerCamelCase    ,     '''utf-8''' )]






def snake_case_      (					*__snake_case  :							str	,   **__snake_case  :							Any)			->					Any:
   return MockResponse()



@pytest.mark.parametrize('''urls_type'''	,   [str, list, dict])
def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Dict	,   __snake_case  :							List[str])			->					List[Any]:
   import requests

   monkeypatch.setattr(__snake_case	,   '''request'''	,   __snake_case)

   lowerCAmelCase_			=      URL
   if issubclass(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      url
   elif issubclass(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      [url]
   elif issubclass(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      {'''train''': url}
   lowerCAmelCase_			=      '''dummy'''
   lowerCAmelCase_			=      '''downloads'''
   lowerCAmelCase_			=      tmp_path
   lowerCAmelCase_			=      DownloadConfig(
       cache_dir=os.path.join(__snake_case	,   __snake_case)	,   use_etag=__snake_case	,   )
   lowerCAmelCase_			=      DownloadManager(dataset_name=__snake_case	,   download_config=__snake_case)
   lowerCAmelCase_			=      dl_manager.download(__snake_case)
   lowerCAmelCase_			=      urls
   for downloaded_paths in [downloaded_paths]:
      if isinstance(__snake_case	,   __snake_case):
         lowerCAmelCase_			=      [downloaded_paths]
         lowerCAmelCase_			=      [urls]
      elif isinstance(__snake_case	,   __snake_case):
         assert "train" in downloaded_paths.keys()
         lowerCAmelCase_			=      downloaded_paths.values()
         lowerCAmelCase_			=      urls.values()
      assert downloaded_paths
      for downloaded_path, input_url in zip(__snake_case	,   __snake_case):
         assert downloaded_path == dl_manager.downloaded_paths[input_url]
         lowerCAmelCase_			=      Path(__snake_case)
         lowerCAmelCase_			=      downloaded_path.parts
         assert parts[-1] == HASH
         assert parts[-2] == cache_subdir
         assert downloaded_path.exists()
         lowerCAmelCase_			=      downloaded_path.read_text()
         assert content == CONTENT
         lowerCAmelCase_			=      downloaded_path.with_suffix('''.json''')
         assert metadata_downloaded_path.exists()
         lowerCAmelCase_			=      json.loads(metadata_downloaded_path.read_text())
         assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('''paths_type'''	,   [str, list, dict])
def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Tuple	,   __snake_case  :							List[Any])			->					List[str]:
   lowerCAmelCase_			=      str(__snake_case)
   if issubclass(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      filename
   elif issubclass(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      [filename]
   elif issubclass(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      {'''train''': filename}
   lowerCAmelCase_			=      '''dummy'''
   lowerCAmelCase_			=      xz_file.parent
   lowerCAmelCase_			=      '''extracted'''
   lowerCAmelCase_			=      DownloadConfig(
       cache_dir=__snake_case	,   use_etag=__snake_case	,   )
   lowerCAmelCase_			=      DownloadManager(dataset_name=__snake_case	,   download_config=__snake_case)
   lowerCAmelCase_			=      dl_manager.extract(__snake_case)
   lowerCAmelCase_			=      paths
   for extracted_paths in [extracted_paths]:
      if isinstance(__snake_case	,   __snake_case):
         lowerCAmelCase_			=      [extracted_paths]
         lowerCAmelCase_			=      [paths]
      elif isinstance(__snake_case	,   __snake_case):
         assert "train" in extracted_paths.keys()
         lowerCAmelCase_			=      extracted_paths.values()
         lowerCAmelCase_			=      paths.values()
      assert extracted_paths
      for extracted_path, input_path in zip(__snake_case	,   __snake_case):
         assert extracted_path == dl_manager.extracted_paths[input_path]
         lowerCAmelCase_			=      Path(__snake_case)
         lowerCAmelCase_			=      extracted_path.parts
         assert parts[-1] == hash_url_to_filename(__snake_case	,   etag=__snake_case)
         assert parts[-2] == extracted_subdir
         assert extracted_path.exists()
         lowerCAmelCase_			=      extracted_path.read_text()
         lowerCAmelCase_			=      text_file.read_text()
         assert extracted_file_content == expected_file_content



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Any)			->					int:
   assert path.endswith('''.jsonl''')
   for num_items, line in enumerate(__snake_case	,   start=1):
      lowerCAmelCase_			=      json.loads(line.decode('''utf-8'''))
      assert item.keys() == {"col_1", "col_2", "col_3"}
   assert num_items == 4



@pytest.mark.parametrize('''archive_jsonl'''	,   ['''tar_jsonl_path''', '''zip_jsonl_path'''])
def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							List[str])			->					Optional[int]:
   lowerCAmelCase_			=      request.getfixturevalue(__snake_case)
   lowerCAmelCase_			=      DownloadManager()
   for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__snake_case)	,   start=1):
      _test_jsonl(__snake_case	,   __snake_case)
   assert num_jsonl == 2



@pytest.mark.parametrize('''archive_nested_jsonl'''	,   ['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path'''])
def snake_case_      (					__snake_case  :							int	,   __snake_case  :							Tuple)			->					Dict:
   lowerCAmelCase_			=      request.getfixturevalue(__snake_case)
   lowerCAmelCase_			=      DownloadManager()
   for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__snake_case)	,   start=1):
      for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__snake_case)	,   start=1):
         _test_jsonl(__snake_case	,   __snake_case)
   assert num_tar == 1
   assert num_jsonl == 2






def snake_case_      (					__snake_case  :							List[str])			->					Any:
   lowerCAmelCase_			=      DownloadManager()
   for num_file, file in enumerate(dl_manager.iter_files(__snake_case)	,   start=1):
      assert os.path.basename(__snake_case) == ("test.txt" if num_file == 1 else "train.txt")
   assert num_file == 2






'''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     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     for model_result in results.values():
        for batch_size, sequence_length in zip(model_result['''bs''']    ,     model_result['''ss'''] ):
           lowerCAmelCase_			=      model_result['''result'''][batch_size][sequence_length]
           self.assertIsNotNone(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sgugger/tiny-distilbert-classification'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     only_pretrain_model=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     torchscript=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     fpaa=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     # set architectures equal to `None`
     lowerCAmelCase_			=      None
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     fpaa=_lowerCamelCase    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tinier_bart'''
     lowerCAmelCase_			=      AutoConfig.from_pretrained(_lowerCamelCase )
     lowerCAmelCase_			=      PyTorchBenchmarkArguments(
         models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     multi_process=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase    ,     configs=[config] )
     lowerCAmelCase_			=      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 ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''
     with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase_			=      PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,     training=_lowerCamelCase    ,     inference=_lowerCamelCase    ,     save_to_csv=_lowerCamelCase    ,     sequence_lengths=[8]    ,     batch_sizes=[1]    ,     inference_time_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' )    ,     train_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' )    ,     inference_memory_csv_file=os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' )    ,     train_time_csv_file=os.path.join(_lowerCamelCase    ,     '''train_time.csv''' )    ,     env_info_csv_file=os.path.join(_lowerCamelCase    ,     '''env.csv''' )    ,     multi_process=_lowerCamelCase    ,     )
        lowerCAmelCase_			=      PyTorchBenchmark(_lowerCamelCase )
        benchmark.run()
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_time.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''inf_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''train_mem.csv''' ) ).exists() )
        self.assertTrue(Path(os.path.join(_lowerCamelCase    ,     '''env.csv''' ) ).exists() )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      '''sshleifer/tiny-gpt2'''

     def _check_summary_is_not_empty(_lowerCamelCase ):
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''sequential''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''cumulative''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''current''' ) )
        self.assertTrue(hasattr(_lowerCamelCase    ,     '''total''' ) )

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





