



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


_snake_case	=						['''gpt2''']
_snake_case	=						'''gpt2'''

if is_tf_available():



						class 						UpperCAmelCase_					( tf.Module):







													def __init__(     self,    __a):

																				'''simple docstring'''

																				super().__init__()
																				_lowerCAmelCase  :							Union[str, Any]				     =					tokenizer
																				_lowerCAmelCase  :							Any				     =					AutoConfig.from_pretrained(__UpperCamelCase)
																				_lowerCAmelCase  :							int				     =					TFGPTaLMHeadModel.from_config(__UpperCamelCase)







													@tf.function(input_signature=(tf.TensorSpec((None,),    tf.string,    name="text"),))
													def      snake_case__   (     self,    __a):

																				'''simple docstring'''

																				_lowerCAmelCase  :							int				     =					self.tokenizer(__UpperCamelCase)
																				_lowerCAmelCase  :							Tuple				     =					tokenized["input_ids"].to_tensor()

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

																				_lowerCAmelCase  :							int				     =					self.model(input_ids=__UpperCamelCase,    attention_mask=__UpperCamelCase)["logits"]

																				return outputs



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







							def      snake_case__   (     self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase  :							int				     =					[GPTaTokenizer.from_pretrained(__UpperCamelCase) for checkpoint in (TOKENIZER_CHECKPOINTS)]
														_lowerCAmelCase  :							Union[str, Any]				     =					[TFGPTaTokenizer.from_pretrained(__UpperCamelCase) for checkpoint in TOKENIZER_CHECKPOINTS]
														assert len(self.tokenizers) == len(self.tf_tokenizers)

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







							def      snake_case__   (     self):

														'''simple docstring'''

														for tokenizer, tf_tokenizer in zip(self.tokenizers,    self.tf_tokenizers):
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase  :							str				     =					tokenizer([test_inputs],    return_tensors="tf")
																												_lowerCAmelCase  :							Optional[int]				     =					tf_tokenizer([test_inputs])

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







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							Any				     =					tf.function(__UpperCamelCase)
																					for test_inputs in self.test_sentences:
																												_lowerCAmelCase  :							List[str]				     =					tf.constant(__UpperCamelCase)
																												_lowerCAmelCase  :							Dict				     =					compiled_tokenizer(__UpperCamelCase)
																												_lowerCAmelCase  :							Optional[Any]				     =					tf_tokenizer(__UpperCamelCase)

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







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

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

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							Dict				     =					tf.convert_to_tensor([self.test_sentences[0]])
																					_lowerCAmelCase  :							Any				     =					tf_tokenizer(__UpperCamelCase)  # Build model with some sample inputs

																					_lowerCAmelCase  :							Any				     =					tf_tokenizer.get_config()
																					_lowerCAmelCase  :							Tuple				     =					TFGPTaTokenizer.from_config(__UpperCamelCase)
																					_lowerCAmelCase  :							List[Any]				     =					model_from_config(__UpperCamelCase)

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







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					# for the test to run
																					_lowerCAmelCase  :							str				     =					12_3123

																					for max_length in [3, 5, 1024]:
																												_lowerCAmelCase  :							List[str]				     =					tf.convert_to_tensor([self.test_sentences[0]])
																												_lowerCAmelCase  :							Optional[int]				     =					tf_tokenizer(__UpperCamelCase,    max_length=__UpperCamelCase)

																												_lowerCAmelCase  :							Any				     =					out["input_ids"].numpy().shape[1]

																												assert out_length == max_length





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


_snake_case	=						{
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					XLNetConfig.from_json_file(_lowerCamelCase						)

							_lowerCAmelCase  :							Any				     =					finetuning_task.lower() if finetuning_task is not None else ""
							if finetuning_task in GLUE_TASKS_NUM_LABELS:
														print(F"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"						)
														_lowerCAmelCase  :							Any				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					GLUE_TASKS_NUM_LABELS[finetuning_task]
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetForSequenceClassification(_lowerCamelCase						)
							elif "squad" in finetuning_task:
														_lowerCAmelCase  :							Union[str, Any]				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					XLNetForQuestionAnswering(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetLMHeadModel(_lowerCamelCase						)

							# Load weights from tf checkpoint
							load_tf_weights_in_xlnet(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

							# Save pytorch-model
							_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							print(F"Save PyTorch model to {os.path.abspath(_lowerCamelCase						)}"						)
							torch.save(model.state_dict()       ,				_lowerCamelCase						)
							print(F"Save configuration file to {os.path.abspath(_lowerCamelCase						)}"						)
							with open(_lowerCamelCase       ,				"w"       ,				encoding="utf-8"						) as f:
														f.write(config.to_json_string()						)


if __name__ == "__main__":
						_snake_case	=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--xlnet_config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained XLNet model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the folder to store the PyTorch model or dataset/vocab.",
						)
						parser.add_argument(
						    "--finetuning_task",
						    default=None,
						    type=str,
						    help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
						)
						_snake_case	=						parser.parse_args()
						print(args)

						convert_xlnet_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
						)
