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


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		["""gpt2"""]
SCREAMING_SNAKE_CASE			:						List[Any]				  =		"""gpt2"""

if is_tf_available():
  class  A_			(							tf.Module			):

    def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
     super().__init__()
     __a							=	tokenizer
     __a							=	AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
     __a							=	TFGPTaLMHeadModel.from_config(__SCREAMING_SNAKE_CASE							)

    @tf.function(input_signature=(tf.TensorSpec((None,)  ,				tf.string  ,				name="text"							),)							)
    def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
     __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE							)
     __a							=	tokenized["input_ids"].to_tensor()

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

     __a							=	self.model(input_ids=__SCREAMING_SNAKE_CASE  ,				attention_mask=__SCREAMING_SNAKE_CASE							)["logits"]

     return outputs
@require_tf
@require_keras_nlp
class  A_			(							unittest.TestCase			):

  def 		_UpperCAmelCase (		self			:   Tuple							):
   super().setUp()

   __a							=	[GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __a							=	[TFGPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE							) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers							) == len(self.tf_tokenizers							)

   __a							=	[
       "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",
   ]
   __a							=	list(zip(self.test_sentences  ,				self.test_sentences[::-1]							)							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   for tokenizer, tf_tokenizer in zip(self.tokenizers  ,				self.tf_tokenizers							):
    for test_inputs in self.test_sentences:
     __a							=	tokenizer([test_inputs]  ,				return_tensors="tf"							)
     __a							=	tf_tokenizer([test_inputs]							)

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

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

  @slow
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   for tf_tokenizer in self.tf_tokenizers:
    __a							=	tf.function(__SCREAMING_SNAKE_CASE							)
    for test_inputs in self.test_sentences:
     __a							=	tf.constant(__SCREAMING_SNAKE_CASE							)
     __a							=	compiled_tokenizer(__SCREAMING_SNAKE_CASE							)
     __a							=	tf_tokenizer(__SCREAMING_SNAKE_CASE							)

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

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   for tf_tokenizer in self.tf_tokenizers:
    __a							=	ModelToSave(tokenizer=__SCREAMING_SNAKE_CASE							)
    __a							=	tf.convert_to_tensor([self.test_sentences[0]]							)
    __a							=	model.serving(__SCREAMING_SNAKE_CASE							)  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __a							=	Path(__SCREAMING_SNAKE_CASE							) / "saved.model"
     tf.saved_model.save(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				signatures={"serving_default": model.serving}							)
     __a							=	tf.saved_model.load(__SCREAMING_SNAKE_CASE							)
    __a							=	loaded_model.signatures["serving_default"](__SCREAMING_SNAKE_CASE							)["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]							):
   for tf_tokenizer in self.tf_tokenizers:
    __a							=	tf.convert_to_tensor([self.test_sentences[0]]							)
    __a							=	tf_tokenizer(__SCREAMING_SNAKE_CASE							)  # Build model with some sample inputs

    __a							=	tf_tokenizer.get_config()
    __a							=	TFGPTaTokenizer.from_config(__SCREAMING_SNAKE_CASE							)
    __a							=	model_from_config(__SCREAMING_SNAKE_CASE							)

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

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __a							=	12_31_23

    for max_length in [3, 5, 10_24]:
     __a							=	tf.convert_to_tensor([self.test_sentences[0]]							)
     __a							=	tf_tokenizer(__SCREAMING_SNAKE_CASE  ,				max_length=__SCREAMING_SNAKE_CASE							)

     __a							=	out["input_ids"].numpy().shape[1]

     assert out_length == max_length



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 return (abs(source - target       ) / target) < 0.01






@pytest.mark.integration
def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	_TestCommandArgs(dataset=_A				,					all_configs=_A				,					save_infos=_A       )
 __a							=	TestCommand(*_A       )
 test_command.run()
 __a							=	os.path.join(_A				,					"README.md"       )
 assert os.path.exists(_A       )
 __a							=	DatasetInfosDict.from_directory(_A       )
 __a							=	DatasetInfosDict(
     {
         "default": DatasetInfo(
             features=Features(
                 {
                     "tokens": Sequence(Value("string"       )       ),
                     "ner_tags": Sequence(
                         ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]       )       ),
                     "langs": Sequence(Value("string"       )       ),
                     "spans": Sequence(Value("string"       )       ),
                 }       )				,					splits=[
                 {
                     "name": "train",
                     "num_bytes": 235_1563,
                     "num_examples": 1_0000,
                 },
                 {
                     "name": "validation",
                     "num_bytes": 23_8418,
                     "num_examples": 1000,
                 },
             ]				,					download_size=394_0680				,					dataset_size=258_9981				,					)
     }       )
 assert dataset_infos.keys() == expected_dataset_infos.keys()
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  __a							,  __a							=	getattr(dataset_infos["default"]				,					_A       ), getattr(expected_dataset_infos["default"]				,					_A       )
  if key == "num_bytes":
   assert is_apercent_close(_A				,					_A       )
  elif key == "splits":
   assert list(_A       ) == list(_A       )
   for split in result:
    assert result[split].name == expected[split].name
    assert result[split].num_examples == expected[split].num_examples
    assert is_apercent_close(result[split].num_bytes				,					expected[split].num_bytes       )
  else:
   result == expected


