'''simple docstring'''


import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__snake_case						  =		trt.Logger(trt.Logger.WARNING)
__snake_case						  =		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__snake_case						  =		logging.getLogger(__name__)

__snake_case						  =		argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    """--onnx_model_path""",
    default=None,
    type=str,
    required=True,
    help="""Path to ONNX model: """,
)

parser.add_argument(
    """--output_dir""",
    default=None,
    type=str,
    required=True,
    help="""The output directory where the model checkpoints and predictions will be written.""",
)

# Other parameters

parser.add_argument(
    """--tokenizer_name""",
    default="""""",
    type=str,
    required=True,
    help="""Pretrained tokenizer name or path if not the same as model_name""",
)

parser.add_argument(
    """--version_2_with_negative""",
    action="""store_true""",
    help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
    """--null_score_diff_threshold""",
    type=float,
    default=0.0,
    help="""If null_score - best_non_null is greater than the threshold predict null.""",
)

parser.add_argument(
    """--max_seq_length""",
    default=384,
    type=int,
    help=(
        """The maximum total input sequence length after WordPiece tokenization. Sequences """
        """longer than this will be truncated, and sequences shorter than this will be padded."""
    ),
)
parser.add_argument(
    """--doc_stride""",
    default=128,
    type=int,
    help="""When splitting up a long document into chunks, how much stride to take between chunks.""",
)

parser.add_argument("""--per_device_eval_batch_size""", default=8, type=int, help="""Batch size per GPU/CPU for evaluation.""")

parser.add_argument(
    """--n_best_size""",
    default=20,
    type=int,
    help="""The total number of n-best predictions to generate in the nbest_predictions.json output file.""",
)
parser.add_argument(
    """--max_answer_length""",
    default=30,
    type=int,
    help=(
        """The maximum length of an answer that can be generated. This is needed because the start """
        """and end predictions are not conditioned on one another."""
    ),
)

parser.add_argument("""--seed""", type=int, default=42, help="""random seed for initialization""")

parser.add_argument(
    """--dataset_name""",
    type=str,
    default=None,
    required=True,
    help="""The name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--dataset_config_name""",
    type=str,
    default=None,
    help="""The configuration name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--preprocessing_num_workers""", type=int, default=4, help="""A csv or a json file containing the training data."""
)
parser.add_argument("""--overwrite_cache""", action="""store_true""", help="""Overwrite the cached training and evaluation sets""")
parser.add_argument(
    """--fp16""",
    action="""store_true""",
    help="""Whether to use 16-bit (mixed) precision instead of 32-bit""",
)
parser.add_argument(
    """--int8""",
    action="""store_true""",
    help="""Whether to use INT8""",
)

__snake_case						  =		parser.parse_args()

if args.tokenizer_name:
       __snake_case						  =		AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
       raise ValueError(
           """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
           """You can do it from another script, save it, and load it from here, using --tokenizer_name."""
       )

logger.info("""Training/evaluation parameters %s""", args)

__snake_case						  =		args.per_device_eval_batch_size

__snake_case						  =		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__snake_case						  =		True

__snake_case						  =		"temp_engine/bert-fp32.engine"
if args.fpaa:
       __snake_case						  =		"temp_engine/bert-fp16.engine"
if args.inta:
       __snake_case						  =		"temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("""temp_engine"""):
       os.makedirs("""temp_engine""")

__snake_case						  =		1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
       with open(args.onnx_model_path, """rb""") as model:
              if not parser.parse(model.read()):
                     for error in range(parser.num_errors):
                            print(parser.get_error(error))

                            # Query input names and shapes from parsed TensorRT network
       __snake_case						  =		[network.get_input(i) for i in range(network.num_inputs)]
       __snake_case						  =		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

       with builder.create_builder_config() as config:
              __snake_case						  =		1 << 50
              if STRICT_TYPES:
                     config.set_flag(trt.BuilderFlag.STRICT_TYPES)
              if args.fpaa:
                     config.set_flag(trt.BuilderFlag.FPaa)
              if args.inta:
                     config.set_flag(trt.BuilderFlag.INTa)
              __snake_case						  =		builder.create_optimization_profile()
              config.add_optimization_profile(profile)
              for i in range(len(input_names)):
                     profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
              __snake_case						  =		builder.build_engine(network, config)

              # serialize_engine and store in file (can be directly loaded and deserialized):
              with open(engine_name, """wb""") as f:
                     f.write(engine.serialize())

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       np.asarray(inputs["""input_ids"""] ,	dtype=np.intaa						)
  lowercase_     =       np.asarray(inputs["""attention_mask"""] ,	dtype=np.intaa						)
  lowercase_     =       np.asarray(inputs["""token_type_ids"""] ,	dtype=np.intaa						)

  # Copy inputs
  cuda.memcpy_htod_async(d_inputs[0] ,	input_ids.ravel() ,	_SCREAMING_SNAKE_CASE						)
  cuda.memcpy_htod_async(d_inputs[1] ,	attention_mask.ravel() ,	_SCREAMING_SNAKE_CASE						)
  cuda.memcpy_htod_async(d_inputs[2] ,	token_type_ids.ravel() ,	_SCREAMING_SNAKE_CASE						)
  # start time
  lowercase_     =       time.time()
  # Run inference
  context.execute_async(
      bindings=[int(_SCREAMING_SNAKE_CASE						) for d_inp in d_inputs] + [int(_SCREAMING_SNAKE_CASE						), int(_SCREAMING_SNAKE_CASE						)] ,	stream_handle=stream.handle						)
  # Transfer predictions back from GPU
  cuda.memcpy_dtoh_async(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE						)
  cuda.memcpy_dtoh_async(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE						)
  # Synchronize the stream and take time
  stream.synchronize()
  # end time
  lowercase_     =       time.time()
  lowercase_     =       end_time - start_time
  lowercase_     =       (h_outputa, h_outputa)
  # print(outputs)
  return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__snake_case						  =		Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
       datasets.utils.logging.set_verbosity_warning()
       transformers.utils.logging.set_verbosity_info()
else:
       datasets.utils.logging.set_verbosity_error()
       transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
       set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
       # Downloading and loading a dataset from the hub.
       __snake_case						  =		load_dataset(args.dataset_name, args.dataset_config_name)
else:
       raise ValueError("""Evaluation requires a dataset name""")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__snake_case						  =		raw_datasets["validation"].column_names

__snake_case						  =		"question" if "question" in column_names else column_names[0]
__snake_case						  =		"context" if "context" in column_names else column_names[1]
__snake_case						  =		"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__snake_case						  =		tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
       logger.warning(
           f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
           f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
       )

__snake_case						  =		min(args.max_seq_length, tokenizer.model_max_length)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Tuple:
  lowercase_     =       [q.lstrip() for q in examples[question_column_name]]

  # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
  # in one example possible giving several features when a context is long, each of those features having a
  # context that overlaps a bit the context of the previous feature.
  lowercase_     =       tokenizer(
      examples[question_column_name if pad_on_right else context_column_name] ,	examples[context_column_name if pad_on_right else question_column_name] ,	truncation="""only_second""" if pad_on_right else """only_first""" ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=args.doc_stride ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	padding="""max_length""" ,	)

  # Since one example might give us several features if it has a long context, we need a map from a feature to
  # its corresponding example. This key gives us just that.
  lowercase_     =       tokenized_examples.pop("""overflow_to_sample_mapping"""						)

  # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
  # corresponding example_id and we will store the offset mappings.
  lowercase_     =       []

  for i in range(len(tokenized_examples["""input_ids"""]						)						):
    # Grab the sequence corresponding to that example (to know what is the context and what is the question).
    lowercase_     =       tokenized_examples.sequence_ids(_SCREAMING_SNAKE_CASE						)
    lowercase_     =       1 if pad_on_right else 0

    # One example can give several spans, this is the index of the example containing this span of text.
    lowercase_     =       sample_mapping[i]
    tokenized_examples["example_id"].append(examples["""id"""][sample_index]						)

    # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
    # position is part of the context or not.
    lowercase_     =       [
        (o if sequence_ids[k] == context_index else None)
        for k, o in enumerate(tokenized_examples["""offset_mapping"""][i]						)
    ]

  return tokenized_examples


__snake_case						  =		raw_datasets["validation"]
# Validation Feature Creation
__snake_case						  =		eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="""Running tokenizer on validation dataset""",
)

__snake_case						  =		default_data_collator

__snake_case						  =		eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
__snake_case						  =		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_="eval"						)			->Optional[Any]:
  lowercase_     =       postprocess_qa_predictions(
      examples=_SCREAMING_SNAKE_CASE ,	features=_SCREAMING_SNAKE_CASE ,	predictions=_SCREAMING_SNAKE_CASE ,	version_2_with_negative=args.version_2_with_negative ,	n_best_size=args.n_best_size ,	max_answer_length=args.max_answer_length ,	null_score_diff_threshold=args.null_score_diff_threshold ,	output_dir=args.output_dir ,	prefix=_SCREAMING_SNAKE_CASE ,	)
  # Format the result to the format the metric expects.
  if args.version_2_with_negative:
    lowercase_     =       [
        {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
    ]
  else:
    lowercase_     =       [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

  lowercase_     =       [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
  return EvalPrediction(predictions=_SCREAMING_SNAKE_CASE ,	label_ids=_SCREAMING_SNAKE_CASE						)


__snake_case						  =		load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

# Evaluation!
logger.info("""Loading ONNX model %s for evaluation""", args.onnx_model_path)
with open(engine_name, """rb""") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
       # setup for TRT inferrence
       for i in range(len(input_names)):
              context.set_binding_shape(i, INPUT_SHAPE)
       assert context.all_binding_shapes_specified

       def   A_      (       SCREAMING_SNAKE_CASE_						)			->Tuple:
         return trt.volume(engine.get_binding_shape(_SCREAMING_SNAKE_CASE						)						) * engine.get_binding_dtype(_SCREAMING_SNAKE_CASE						).itemsize

       # Allocate device memory for inputs and outputs.
       __snake_case						  =		[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

       # Allocate output buffer
       __snake_case						  =		cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
       __snake_case						  =		cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
       __snake_case						  =		cuda.mem_alloc(h_outputa.nbytes)
       __snake_case						  =		cuda.mem_alloc(h_outputa.nbytes)

       # Create a stream in which to copy inputs/outputs and run inference.
       __snake_case						  =		cuda.Stream()

       # Evaluation
       logger.info("""***** Running Evaluation *****""")
       logger.info(f'''  Num examples = {len(eval_dataset)}''')
       logger.info(f'''  Batch size = {args.per_device_eval_batch_size}''')

       __snake_case						  =		0.0
       __snake_case						  =		0
       __snake_case						  =		timeit.default_timer()

       __snake_case						  =		None
       for step, batch in enumerate(eval_dataloader):
              __snake_case						  =		model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
              total_time += infer_time
              niter += 1

              __snake_case						  =		outputs
              __snake_case						  =		torch.tensor(start_logits)
              __snake_case						  =		torch.tensor(end_logits)

              # necessary to pad predictions and labels for being gathered
              __snake_case						  =		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
              __snake_case						  =		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

              __snake_case						  =		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
              __snake_case						  =		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

       if all_preds is not None:
              __snake_case						  =		nested_truncate(all_preds, len(eval_dataset))

       __snake_case						  =		timeit.default_timer() - start_time
       logger.info("""  Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
       # Inference time from TRT
       logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1000 / niter))
       logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))




       logger.info("""Total Number of Inference =  %d""", niter)

__snake_case						  =		post_processing_function(eval_examples, eval_dataset, all_preds)
__snake_case						  =		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)




logger.info(f'''Evaluation metrics: {eval_metric}''')








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







class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			200
     __magic_name__   =			{'Content-Length': '100'}
     __magic_name__   =			{}




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         return [bytes(snake_case_				,     'utf-8'							)]





def 				__lowerCAmelCase(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    return MockResponse()





@pytest.mark.parametrize('urls_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    import requests

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

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





@pytest.mark.parametrize('paths_type'							,     [str, list, dict]				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							str(_SCREAMING_SNAKE_CASE				)
    if issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							filename
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							[filename]
    elif issubclass(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        _A     =							{'train': filename}
    _A     =							'dummy'
    _A     =							xz_file.parent
    _A     =							'extracted'
    _A     =							DownloadConfig(
        cache_dir=_SCREAMING_SNAKE_CASE							,     use_etag=_SCREAMING_SNAKE_CASE							,     )
    _A     =							DownloadManager(dataset_name=_SCREAMING_SNAKE_CASE							,     download_config=_SCREAMING_SNAKE_CASE				)
    _A     =							dl_manager.extract(_SCREAMING_SNAKE_CASE				)
    _A     =							paths
    for extracted_paths in [extracted_paths]:
        if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            _A     =							[extracted_paths]
            _A     =							[paths]
        elif isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert "train" in extracted_paths.keys()
            _A     =							extracted_paths.values()
            _A     =							paths.values()
        assert extracted_paths
        for extracted_path, input_path in zip(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
            assert extracted_path == dl_manager.extracted_paths[input_path]
            _A     =							Path(_SCREAMING_SNAKE_CASE				)
            _A     =							extracted_path.parts
            assert parts[-1] == hash_url_to_filename(_SCREAMING_SNAKE_CASE							,     etag=_SCREAMING_SNAKE_CASE				)
            assert parts[-2] == extracted_subdir
            assert extracted_path.exists()
            _A     =							extracted_path.read_text()
            _A     =							text_file.read_text()
            assert extracted_file_content == expected_file_content





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Dict:




    """simple docstring"""




    assert path.endswith('.jsonl'				)
    for num_items, line in enumerate(_SCREAMING_SNAKE_CASE							,     start=1				):
        _A     =							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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_jsonl == 2





@pytest.mark.parametrize('archive_nested_jsonl'							,     ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Any:




    """simple docstring"""




    _A     =							request.getfixturevalue(_SCREAMING_SNAKE_CASE				)
    _A     =							DownloadManager()
    for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_SCREAMING_SNAKE_CASE				)							,     start=1				):
            _test_jsonl(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    assert num_tar == 1
    assert num_jsonl == 2





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							DownloadManager()
    for num_file, file in enumerate(dl_manager.iter_files(_SCREAMING_SNAKE_CASE				)							,     start=1				):
        assert os.path.basename(_SCREAMING_SNAKE_CASE				) == ("test.txt" if num_file == 1 else "train.txt")
    assert num_file == 2





