





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
      import tensorflow as tf

if is_keras_nlp_available():
      from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase_						   =    ["""gpt2"""]
lowerCAmelCase_						   =    """gpt2"""

if is_tf_available():



      class        _lowerCAmelCase    (			tf.Module							):


        '''simple docstring'''







        def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Dict							):


          '''simple docstring'''





          super().__init__()
          _snake_case   :				Optional[int]														=	tokenizer
          _snake_case   :				Union[str, Any]														=	AutoConfig.from_pretrained(UpperCamelCase							)
          _snake_case   :				int														=	TFGPTaLMHeadModel.from_config(UpperCamelCase							)







        @tf.function(input_signature=(tf.TensorSpec((None,)      ,				tf.string      ,				name='text'							),)							)
        def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Optional[int]							):


          '''simple docstring'''





          _snake_case   :				Dict														=	self.tokenizer(UpperCamelCase							)
          _snake_case   :				Union[str, Any]														=	tokenized['input_ids'].to_tensor()

          _snake_case   :				Any														=	tf.cast(input_ids_dense > 0      ,				tf.intaa							)
          # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

          _snake_case   :				Tuple														=	self.model(input_ids=UpperCamelCase      ,				attention_mask=UpperCamelCase							)['logits']

          return outputs



@require_tf
@require_keras_nlp
class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    super().setUp()

    _snake_case   :				Optional[int]														=	[GPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
    _snake_case   :				Tuple														=	[TFGPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
    assert len(self.tokenizers							) == len(self.tf_tokenizers							)

    _snake_case   :				Any														=	[
        'This is a straightforward English test sentence.',
        'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
        'Now we\'re going to add some Chinese: 一 二 三 一二三',
        'And some much more rare Chinese: 齉 堃 齉堃',
        'Je vais aussi écrire en français pour tester les accents',
        'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
    ]
    _snake_case   :				Tuple														=	list(zip(self.test_sentences      ,				self.test_sentences[::-1]							)							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    for tokenizer, tf_tokenizer in zip(self.tokenizers      ,				self.tf_tokenizers							):
      for test_inputs in self.test_sentences:
        _snake_case   :				Optional[int]														=	tokenizer([test_inputs]      ,				return_tensors='tf'							)
        _snake_case   :				Tuple														=	tf_tokenizer([test_inputs]							)

        for key in python_outputs.keys():
          # convert them to numpy to avoid messing with ragged tensors
          _snake_case   :				Dict														=	python_outputs[key].numpy()
          _snake_case   :				Optional[Any]														=	tf_outputs[key].numpy()

          self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
          self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase      ,				tf.intaa							) == tf_outputs_values							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				str														=	tf.function(UpperCamelCase							)
      for test_inputs in self.test_sentences:
        _snake_case   :				int														=	tf.constant(UpperCamelCase							)
        _snake_case   :				Tuple														=	compiled_tokenizer(UpperCamelCase							)
        _snake_case   :				int														=	tf_tokenizer(UpperCamelCase							)

        for key in eager_outputs.keys():
          self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Union[str, Any]														=	ModelToSave(tokenizer=UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Tuple														=	model.serving(UpperCamelCase							)  # Build model with some sample inputs
      with TemporaryDirectory() as tempdir:
        _snake_case   :				str														=	Path(UpperCamelCase							) / 'saved.model'
        tf.saved_model.save(UpperCamelCase      ,				UpperCamelCase      ,				signatures={'serving_default': model.serving}							)
        _snake_case   :				Optional[int]														=	tf.saved_model.load(UpperCamelCase							)
      _snake_case   :				List[str]														=	loaded_model.signatures['serving_default'](UpperCamelCase							)['output_0']
      # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
      self.assertTrue(tf.reduce_all(out == loaded_output							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Any														=	tf_tokenizer(UpperCamelCase							)  # Build model with some sample inputs

      _snake_case   :				Optional[Any]														=	tf_tokenizer.get_config()
      _snake_case   :				Tuple														=	TFGPTaTokenizer.from_config(UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	model_from_config(UpperCamelCase							)

      for key in from_config_output.keys():
        self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      # for the test to run
      _snake_case   :				Union[str, Any]														=	12_31_23

      for max_length in [3, 5, 10_24]:
        _snake_case   :				Union[str, Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
        _snake_case   :				List[str]														=	tf_tokenizer(UpperCamelCase      ,				max_length=UpperCamelCase							)

        _snake_case   :				int														=	out['input_ids'].numpy().shape[1]

        assert out_length == max_length











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        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       int      					=200
  a_							:       List[str]      					={"""Content-Length""": """100"""}
  a_							:       Tuple      					={}







  def UpperCamelCase_ (      self      :					Any      ,				**UpperCamelCase      :					Any							):


    '''simple docstring'''





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




def     lowerCamelCase_	(      *lowerCAmelCase:	Tuple    ,							**lowerCAmelCase:	Tuple				)->		str:
  return MockResponse()


@pytest.mark.parametrize('urls_type'    ,							[str, list, dict]				)
def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict				)->		Optional[Any]:
  import requests

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

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


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


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	List[Any]				)->		Dict:
  assert path.endswith('.jsonl'				)
  for num_items, line in enumerate(lowerCAmelCase    ,							start=1				):
    _snake_case   :				Dict														=	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_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	List[str]				)->		Dict:
  _snake_case   :				List[str]														=	request.getfixturevalue(lowerCAmelCase				)
  _snake_case   :				Optional[Any]														=	DownloadManager()
  for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCAmelCase				)    ,							start=1				):
    _test_jsonl(lowerCAmelCase    ,							lowerCAmelCase				)
  assert num_jsonl == 2


@pytest.mark.parametrize('archive_nested_jsonl'    ,							['tar_nested_jsonl_path', 'zip_nested_jsonl_path']				)
def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	int				)->		str:
  _snake_case   :				List[Any]														=	request.getfixturevalue(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	DownloadManager()
  for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCAmelCase				)    ,							start=1				):
    for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCAmelCase				)    ,							start=1				):
      _test_jsonl(lowerCAmelCase    ,							lowerCAmelCase				)
  assert num_tar == 1
  assert num_jsonl == 2







def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		int:
  _snake_case   :				Tuple														=	DownloadManager()
  for num_file, file in enumerate(dl_manager.iter_files(lowerCAmelCase				)    ,							start=1				):
    assert os.path.basename(lowerCAmelCase				) == ("test.txt" if num_file == 1 else "train.txt")
  assert num_file == 2




