

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 SCREAMING_SNAKE_CASE  (		tf.Module							):






					"""simple docstring"""




					def __init__(    self ,				__UpperCamelCase							):

						"""simple docstring"""


						super().__init__()
						snake_case_      		= tokenizer
						snake_case_      		= AutoConfig.from_pretrained(__UpperCamelCase							)
						snake_case_      		= TFGPTaLMHeadModel.from_config(__UpperCamelCase							)







					@tf.function(input_signature=(tf.TensorSpec((None,) ,				tf.string ,				name='text'							),)							)
					def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

						"""simple docstring"""


						snake_case_      		= self.tokenizer(__UpperCamelCase							)
						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=__UpperCamelCase ,				attention_mask=__UpperCamelCase							)['logits']

						return outputs


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






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				super().setUp()

				snake_case_      		= [GPTaTokenizer.from_pretrained(__UpperCamelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
				snake_case_      		= [TFGPTaTokenizer.from_pretrained(__UpperCamelCase							) 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for tf_tokenizer in self.tf_tokenizers:
					snake_case_      		= ModelToSave(tokenizer=__UpperCamelCase							)
					snake_case_      		= tf.convert_to_tensor([self.test_sentences[0]]							)
					snake_case_      		= model.serving(__UpperCamelCase							)  # Build model with some sample inputs
					with TemporaryDirectory() as tempdir:
						snake_case_      		= Path(__UpperCamelCase							) / 'saved.model'
						tf.saved_model.save(__UpperCamelCase ,				__UpperCamelCase ,				signatures={'serving_default': model.serving}							)
						snake_case_      		= tf.saved_model.load(__UpperCamelCase							)
					snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

					snake_case_      		= tf_tokenizer.get_config()
					snake_case_      		= TFGPTaTokenizer.from_config(__UpperCamelCase							)
					snake_case_      		= model_from_config(__UpperCamelCase							)

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







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for tf_tokenizer in self.tf_tokenizers:
					# for the test to run
					snake_case_      		= 12_31_23

					for max_length in [3, 5, 10_24]:
						snake_case_      		= tf.convert_to_tensor([self.test_sentences[0]]							)
						snake_case_      		= tf_tokenizer(__UpperCamelCase ,				max_length=__UpperCamelCase							)

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

						assert out_length == max_length



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_      		= TapasConfig.from_json_file(lowercase__	)
	# set absolute/relative position embeddings parameter
	snake_case_      		= reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= False
		# hparam_utils.py hparams
		snake_case_      		= 36.4519
		snake_case_      		= 0.90_3421
		snake_case_      		= 222.088
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= 0.76_3141

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "TABFACT":
		snake_case_      		= TapasForSequenceClassification(config=lowercase__	)
	elif task == "MLM":
		snake_case_      		= TapasForMaskedLM(config=lowercase__	)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_      		= TapasModel(config=lowercase__	)
	else:
		raise ValueError(f"""Task {task} not supported."""	)

	print(f"""Building PyTorch model from configuration: {config}"""	)
	# Load weights from tf checkpoint
	load_tf_weights_in_tapas(lowercase__			, lowercase__			, lowercase__	)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(lowercase__	)

	# Save tokenizer files
	print(f"""Save tokenizer files to {pytorch_dump_path}"""	)
	snake_case_      		= TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'			, model_max_length=512	)
	tokenizer.save_pretrained(lowercase__	)

	print('Used relative position embeddings:'			, model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
		A											=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
		)
		parser.add_argument(
		    '--reset_position_index_per_cell',
		    default=False,
		    action='store_true',
		    help='Whether to use relative position embeddings or not. Defaults to True.',
		)
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--tapas_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained TAPAS model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		A											=     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
