



"""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


A    =			['gpt2']
A    =			'gpt2'

if is_tf_available():






							class      UpperCAmelCase__		(      tf.Module     ):



									def __init__( self  :    str      ,							snake_case  :    str				)			->						Dict:






																'''simple docstring'''

																super().__init__()
																A         =					tokenizer
																A         =					AutoConfig.from_pretrained(snake_case				)
																A         =					TFGPTaLMHeadModel.from_config(snake_case				)



									@tf.function(input_signature=(tf.TensorSpec((None,)      ,							tf.string      ,							name='text'				),)				)
									def 					A_		( self  :    Dict      ,							snake_case  :    Optional[int]				)			->						List[Any]:






																'''simple docstring'''

																A         =					self.tokenizer(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=snake_case      ,							attention_mask=snake_case				)['logits']

																return outputs






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



		def 					A_		( self  :    str				)			->						Tuple:






									'''simple docstring'''

									super().setUp()

									A         =					[GPTaTokenizer.from_pretrained(snake_case				) for checkpoint in (TOKENIZER_CHECKPOINTS)]
									A         =					[TFGPTaTokenizer.from_pretrained(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 					A_		( self  :    Optional[int]				)			->						Any:






									'''simple docstring'''

									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(snake_case      ,							tf.intaa				) == tf_outputs_values				)				)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						str:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.function(snake_case				)
																for test_inputs in self.test_sentences:
																							A         =					tf.constant(snake_case				)
																							A         =					compiled_tokenizer(snake_case				)
																							A         =					tf_tokenizer(snake_case				)

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



		@slow
		def 					A_		( self  :    str				)			->						List[str]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					ModelToSave(tokenizer=snake_case				)
																A         =					tf.convert_to_tensor([self.test_sentences[0]]				)
																A         =					model.serving(snake_case				)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																							A         =					Path(snake_case				) / 'saved.model'
																							tf.saved_model.save(snake_case      ,							snake_case      ,							signatures={'serving_default': model.serving}				)
																							A         =					tf.saved_model.load(snake_case				)
																A         =					loaded_model.signatures['serving_default'](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 					A_		( self  :    str				)			->						Optional[Any]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.convert_to_tensor([self.test_sentences[0]]				)
																A         =					tf_tokenizer(snake_case				)  # Build model with some sample inputs

																A         =					tf_tokenizer.get_config()
																A         =					TFGPTaTokenizer.from_config(snake_case				)
																A         =					model_from_config(snake_case				)

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



		@slow
		def 					A_		( self  :    str				)			->						str:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																A         =					123_123

																for max_length in [3, 5, 1_024]:
																							A         =					tf.convert_to_tensor([self.test_sentences[0]]				)
																							A         =					tf_tokenizer(snake_case      ,							max_length=snake_case				)

																							A         =					out['input_ids'].numpy().shape[1]

																							assert out_length == max_length








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A    =			logging.get_logger(__name__)

A    =			{
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	List[Any] 							=     """fnet"""



		def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]=32_000      ,							snake_case  :    Optional[Any]=768      ,							snake_case  :    Union[str, Any]=12      ,							snake_case  :    List[str]=3_072      ,							snake_case  :    str="gelu_new"      ,							snake_case  :    Any=0.1      ,							snake_case  :    List[str]=512      ,							snake_case  :    Any=4      ,							snake_case  :    str=0.02      ,							snake_case  :    Union[str, Any]=1E-1_2      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    List[Any]=512      ,							snake_case  :    int=3      ,							snake_case  :    Any=1      ,							snake_case  :    Union[str, Any]=2      ,							**snake_case  :    Union[str, Any]      ,							)			->						Dict:






									'''simple docstring'''

									super().__init__(pad_token_id=snake_case      ,							bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							**snake_case				)

									A         =					vocab_size
									A         =					max_position_embeddings
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					initializer_range
									A         =					type_vocab_size
									A         =					layer_norm_eps
									A         =					use_tpu_fourier_optimizations
									A         =					tpu_short_seq_length



