





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						=   "http://www.mocksite.com/file1.txt"
_a						=   "\"text\": [\"foo\", \"foo\"]"
_a						=   "6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"






class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		200
			lowerCAmelCase_							  =		{"""Content-Length""": """100"""}
			lowerCAmelCase_							  =		{}





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return [bytes(__lowerCAmelCase ,						'''utf-8'''							)]





def lowerCAmelCase__(*__snake_case ,**__snake_case							)       ->   Dict:







	'''simple docstring'''







	return MockResponse()





@pytest.mark.parametrize('''urls_type''' ,[str, list, dict]							)
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Dict:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	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 lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTNeoForCausalLM",
			    "GPTNeoForQuestionAnswering",
			    "GPTNeoForSequenceClassification",
			    "GPTNeoForTokenClassification",
			    "GPTNeoModel",
			    "GPTNeoPreTrainedModel",
			    "load_tf_weights_in_gpt_neo",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FlaxGPTNeoForCausalLM",
			    "FlaxGPTNeoModel",
			    "FlaxGPTNeoPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



