





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 		=						["""gpt2"""]
_SCREAMING_SNAKE_CASE 		=						"""gpt2"""

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






												def __init__( self : str						,				_A : Optional[int]		)							->      Union[str, Any]:







													"""simple docstring"""


													super().__init__()
													snake_case_						:     Optional[Any]     =		tokenizer
													snake_case_						:     str     =		AutoConfig.from_pretrained(_A		)
													snake_case_						:     Dict     =		TFGPTaLMHeadModel.from_config(_A		)






												@tf.function(input_signature=(tf.TensorSpec((None,)						,				tf.string						,				name='text'		),)		)
												def UpperCAmelCase_			( self : Optional[Any]						,				_A : Any		)							->      Tuple:







													"""simple docstring"""


													snake_case_						:     str     =		self.tokenizer(_A		)
													snake_case_						:     Union[str, Any]     =		tokenized['input_ids'].to_tensor()

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

													snake_case_						:     Dict     =		self.model(input_ids=_A						,				attention_mask=_A		)['logits']

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






						def UpperCAmelCase_			( self : List[str]		)							->      str:







							"""simple docstring"""


							super().setUp()

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

							snake_case_						:     Optional[int]     =		[
							    '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_						:     Union[str, Any]     =		list(zip(self.test_sentences						,				self.test_sentences[::-1]		)		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Any:







							"""simple docstring"""


							for tokenizer, tf_tokenizer in zip(self.tokenizers						,				self.tf_tokenizers		):
								for test_inputs in self.test_sentences:
									snake_case_						:     List[Any]     =		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_						:     List[Any]     =		python_outputs[key].numpy()
										snake_case_						:     Union[str, 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 UpperCAmelCase_			( self : List[Any]		)							->      str:







							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
								snake_case_						:     str     =		tf.function(_A		)
								for test_inputs in self.test_sentences:
									snake_case_						:     Union[str, Any]     =		tf.constant(_A		)
									snake_case_						:     Any     =		compiled_tokenizer(_A		)
									snake_case_						:     int     =		tf_tokenizer(_A		)

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






						@slow
						def UpperCAmelCase_			( self : Any		)							->      Dict:







							"""simple docstring"""


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







							"""simple docstring"""


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

								snake_case_						:     Union[str, Any]     =		tf_tokenizer.get_config()
								snake_case_						:     Union[str, Any]     =		TFGPTaTokenizer.from_config(_A		)
								snake_case_						:     int     =		model_from_config(_A		)

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






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







							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
								# for the test to run
								snake_case_						:     List[str]     =		123123

								for max_length in [3, 5, 1024]:
									snake_case_						:     List[str]     =		tf.convert_to_tensor([self.test_sentences[0]]		)
									snake_case_						:     str     =		tf_tokenizer(_A						,				max_length=_A		)

									snake_case_						:     Dict     =		out['input_ids'].numpy().shape[1]

									assert out_length == max_length








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

