




'''simple docstring'''


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


_lowercase  =			["""gpt2"""]
_lowercase  =			"""gpt2"""

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




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




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

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

								__snake_case						 =   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   lowercase__   (       self     :  Tuple      ):
							super().setUp()

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

							__snake_case						 =   [
							    '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						 =   list(zip(self.test_sentences       ,		self.test_sentences[::-1]      )      )




	def   lowercase__   (       self     :  List[Any]      ):
							for tokenizer, tf_tokenizer in zip(self.tokenizers       ,		self.tf_tokenizers      ):
													for test_inputs in self.test_sentences:
																			__snake_case						 =   tokenizer([test_inputs]       ,		return_tensors='tf'      )
																			__snake_case						 =   tf_tokenizer([test_inputs]      )

																			for key in python_outputs.keys():
																									# convert them to numpy to avoid messing with ragged tensors
																									__snake_case						 =   python_outputs[key].numpy()
																									__snake_case						 =   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   lowercase__   (       self     :  Optional[Any]      ):
							for tf_tokenizer in self.tf_tokenizers:
													__snake_case						 =   tf.function(__SCREAMING_SNAKE_CASE      )
													for test_inputs in self.test_sentences:
																			__snake_case						 =   tf.constant(__SCREAMING_SNAKE_CASE      )
																			__snake_case						 =   compiled_tokenizer(__SCREAMING_SNAKE_CASE      )
																			__snake_case						 =   tf_tokenizer(__SCREAMING_SNAKE_CASE      )

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




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

													__snake_case						 =   tf_tokenizer.get_config()
													__snake_case						 =   TFGPTaTokenizer.from_config(__SCREAMING_SNAKE_CASE      )
													__snake_case						 =   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   lowercase__   (       self     :  str      ):
							for tf_tokenizer in self.tf_tokenizers:
													# for the test to run
													__snake_case						 =   1_2_3_1_2_3

													for max_length in [3, 5, 1_0_2_4]:
																			__snake_case						 =   tf.convert_to_tensor([self.test_sentences[0]]      )
																			__snake_case						 =   tf_tokenizer(__SCREAMING_SNAKE_CASE       ,		max_length=__SCREAMING_SNAKE_CASE      )

																			__snake_case						 =   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


