

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


A_       : int         =			['gpt2']
A_       : List[Any]         =			'gpt2'

if is_tf_available():



	class     _a						(tf.Module				):


						'''simple docstring'''






						def __init__( self    ,       A__			):
							super().__init__()
							A__							:    int        =		tokenizer
							A__							:    int        =		AutoConfig.from_pretrained(A__			)
							A__							:    Optional[int]        =		TFGPTaLMHeadModel.from_config(A__			)






						@tf.function(input_signature=(tf.TensorSpec((None,)    ,       tf.string    ,       name="""text"""			),)			)
						def   __A			( self    ,       A__			):
							A__							:    Dict        =		self.tokenizer(A__			)
							A__							:    str        =		tokenized["""input_ids"""].to_tensor()

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

							A__							:    Tuple        =		self.model(input_ids=A__    ,       attention_mask=A__			)["""logits"""]

							return outputs



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


					'''simple docstring'''






					def   __A			( self			):
						super().setUp()

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

						A__							:    str        =		[
						    """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__							:    Tuple        =		list(zip(self.test_sentences    ,       self.test_sentences[::-1]			)			)






					def   __A			( self			):
						for tokenizer, tf_tokenizer in zip(self.tokenizers    ,       self.tf_tokenizers			):
							for test_inputs in self.test_sentences:
								A__							:    Any        =		tokenizer([test_inputs]    ,       return_tensors="""tf"""			)
								A__							:    Tuple        =		tf_tokenizer([test_inputs]			)

								for key in python_outputs.keys():
									# convert them to numpy to avoid messing with ragged tensors
									A__							:    str        =		python_outputs[key].numpy()
									A__							:    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(A__    ,       tf.intaa			) == tf_outputs_values			)			)






					@slow
					def   __A			( self			):
						for tf_tokenizer in self.tf_tokenizers:
							A__							:    Any        =		tf.function(A__			)
							for test_inputs in self.test_sentences:
								A__							:    List[Any]        =		tf.constant(A__			)
								A__							:    Dict        =		compiled_tokenizer(A__			)
								A__							:    Union[str, Any]        =		tf_tokenizer(A__			)

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






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

							A__							:    Dict        =		tf_tokenizer.get_config()
							A__							:    List[Any]        =		TFGPTaTokenizer.from_config(A__			)
							A__							:    str        =		model_from_config(A__			)

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






					@slow
					def   __A			( self			):
						for tf_tokenizer in self.tf_tokenizers:
							# for the test to run
							A__							:    Union[str, Any]        =		12_3123

							for max_length in [3, 5, 1024]:
								A__							:    Union[str, Any]        =		tf.convert_to_tensor([self.test_sentences[0]]			)
								A__							:    Any        =		tf_tokenizer(A__    ,       max_length=A__			)

								A__							:    Tuple        =		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


A_       : 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 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		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""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			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



