






"""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


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







class     UpperCamelCase_ :
	__magic_name__			   =       2_00
	__magic_name__			   =       {'''Content-Length''': '''100'''}
	__magic_name__			   =       {}





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			**lowerCAmelCase_      :				List[str]		)		->    List[Any]:
				return [bytes(lowerCAmelCase_			,			"utf-8"		)]





def        snake_case   (     *A__			,**A__		):
			return MockResponse()

@pytest.mark.parametrize("urls_type"			,[str, list, dict]		)
def        snake_case   (     A__			,A__			,A__		):
			import requests

			monkeypatch.setattr(A__			,"request"			,A__		)

			UpperCAmelCase_  :				Union[str, Any]      =      URL
			if issubclass(A__			,A__		):
						UpperCAmelCase_  :				int      =      url
			elif issubclass(A__			,A__		):
						UpperCAmelCase_  :				int      =      [url]
			elif issubclass(A__			,A__		):
						UpperCAmelCase_  :				Optional[int]      =      {"train": url}
			UpperCAmelCase_  :				Any      =      "dummy"
			UpperCAmelCase_  :				Tuple      =      "downloads"
			UpperCAmelCase_  :				Tuple      =      tmp_path
			UpperCAmelCase_  :				str      =      DownloadConfig(
			    cache_dir=os.path.join(A__			,A__		)			,use_etag=A__			,)
			UpperCAmelCase_  :				Union[str, Any]      =      DownloadManager(dataset_name=A__			,download_config=A__		)
			UpperCAmelCase_  :				Optional[Any]      =      dl_manager.download(A__		)
			UpperCAmelCase_  :				Tuple      =      urls
			for downloaded_paths in [downloaded_paths]:
						if isinstance(A__			,A__		):
									UpperCAmelCase_  :				int      =      [downloaded_paths]
									UpperCAmelCase_  :				List[Any]      =      [urls]
						elif isinstance(A__			,A__		):
									assert "train" in downloaded_paths.keys()
									UpperCAmelCase_  :				str      =      downloaded_paths.values()
									UpperCAmelCase_  :				List[Any]      =      urls.values()
						assert downloaded_paths
						for downloaded_path, input_url in zip(A__			,A__		):
									assert downloaded_path == dl_manager.downloaded_paths[input_url]
									UpperCAmelCase_  :				Optional[Any]      =      Path(A__		)
									UpperCAmelCase_  :				str      =      downloaded_path.parts
									assert parts[-1] == HASH
									assert parts[-2] == cache_subdir
									assert downloaded_path.exists()
									UpperCAmelCase_  :				Dict      =      downloaded_path.read_text()
									assert content == CONTENT
									UpperCAmelCase_  :				Tuple      =      downloaded_path.with_suffix(".json"		)
									assert metadata_downloaded_path.exists()
									UpperCAmelCase_  :				str      =      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   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Dict      =      str(A__		)
			if issubclass(A__			,A__		):
						UpperCAmelCase_  :				Tuple      =      filename
			elif issubclass(A__			,A__		):
						UpperCAmelCase_  :				Union[str, Any]      =      [filename]
			elif issubclass(A__			,A__		):
						UpperCAmelCase_  :				int      =      {"train": filename}
			UpperCAmelCase_  :				Dict      =      "dummy"
			UpperCAmelCase_  :				Union[str, Any]      =      xz_file.parent
			UpperCAmelCase_  :				List[str]      =      "extracted"
			UpperCAmelCase_  :				Union[str, Any]      =      DownloadConfig(
			    cache_dir=A__			,use_etag=A__			,)
			UpperCAmelCase_  :				Union[str, Any]      =      DownloadManager(dataset_name=A__			,download_config=A__		)
			UpperCAmelCase_  :				Optional[int]      =      dl_manager.extract(A__		)
			UpperCAmelCase_  :				Optional[int]      =      paths
			for extracted_paths in [extracted_paths]:
						if isinstance(A__			,A__		):
									UpperCAmelCase_  :				Tuple      =      [extracted_paths]
									UpperCAmelCase_  :				Tuple      =      [paths]
						elif isinstance(A__			,A__		):
									assert "train" in extracted_paths.keys()
									UpperCAmelCase_  :				Any      =      extracted_paths.values()
									UpperCAmelCase_  :				Any      =      paths.values()
						assert extracted_paths
						for extracted_path, input_path in zip(A__			,A__		):
									assert extracted_path == dl_manager.extracted_paths[input_path]
									UpperCAmelCase_  :				Optional[int]      =      Path(A__		)
									UpperCAmelCase_  :				List[str]      =      extracted_path.parts
									assert parts[-1] == hash_url_to_filename(A__			,etag=A__		)
									assert parts[-2] == extracted_subdir
									assert extracted_path.exists()
									UpperCAmelCase_  :				Tuple      =      extracted_path.read_text()
									UpperCAmelCase_  :				Tuple      =      text_file.read_text()
									assert extracted_file_content == expected_file_content

def        snake_case   (     A__			,A__		):
			assert path.endswith(".jsonl"		)
			for num_items, line in enumerate(A__			,start=1		):
						UpperCAmelCase_  :				Any      =      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   (     A__			,A__		):
			UpperCAmelCase_  :				str      =      request.getfixturevalue(A__		)
			UpperCAmelCase_  :				Dict      =      DownloadManager()
			for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(A__		)			,start=1		):
						_test_jsonl(A__			,A__		)
			assert num_jsonl == 2

@pytest.mark.parametrize("archive_nested_jsonl"			,["tar_nested_jsonl_path", "zip_nested_jsonl_path"]		)
def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				str      =      request.getfixturevalue(A__		)
			UpperCAmelCase_  :				Any      =      DownloadManager()
			for num_tar, (path, file) in enumerate(dl_manager.iter_archive(A__		)			,start=1		):
						for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(A__		)			,start=1		):
									_test_jsonl(A__			,A__		)
			assert num_tar == 1
			assert num_jsonl == 2




def        snake_case   (     A__		):
			UpperCAmelCase_  :				List[Any]      =      DownloadManager()
			for num_file, file in enumerate(dl_manager.iter_files(A__		)			,start=1		):
						assert os.path.basename(A__		) == ("test.txt" if num_file == 1 else "train.txt")
			assert num_file == 2











"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,			dtype=tf.intaa			,			)  # J'aime le camembert !"

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			dtype=tf.floataa			,			)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,			expected_slice.numpy()			,			atol=1e-4		)		)



