




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_tensorflow_text_available():
						from transformers.models.bert import TFBertTokenizer


UpperCamelCase__ :   Union[str, Any]						=							['''bert-base-uncased''', '''bert-base-cased''']
UpperCamelCase__ :   Optional[int]						=							'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():







						class 				lowerCAmelCase_       (					tf.keras.Model							):






										def __init__(			self      ,snake_case__ ):
													super().__init__()
													SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer
													SCREAMING_SNAKE_CASE_   : Any					  =      AutoConfig.from_pretrained(snake_case__ )
													SCREAMING_SNAKE_CASE_   : str					  =      TFAutoModel.from_config(snake_case__ )






										def 		snake_case	(			self      ,snake_case__ ):
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.tokenizer(snake_case__ )
													SCREAMING_SNAKE_CASE_   : str					  =      self.bert(**snake_case__ )
													return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 				lowerCAmelCase_       (					unittest.TestCase							):






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

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [
							    BertTokenizer.from_pretrained(snake_case__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
							]  # repeat for when fast_bert_tokenizer=false
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [TFBertTokenizer.from_pretrained(snake_case__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
							    TFBertTokenizer.from_pretrained(snake_case__      ,use_fast_bert_tokenizer=snake_case__ )
							    for checkpoint in TOKENIZER_CHECKPOINTS
							]
							assert len(self.tokenizers ) == len(self.tf_tokenizers )

							SCREAMING_SNAKE_CASE_   : Dict					  =      [
							    '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',
							]
							SCREAMING_SNAKE_CASE_   : Dict					  =      list(zip(self.test_sentences      ,self.test_sentences[::-1] ) )






				def 		snake_case	(			self ):
							for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers ):
										for test_inputs in (self.test_sentences, self.paired_sentences):
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer(snake_case__      ,return_tensors='tf'      ,padding='longest' )
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tf_tokenizer(snake_case__ )

													for key in python_outputs.keys():
																self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
																self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]      ,tf.intaa ) == tf_outputs[key] ) )






				@slow
				def 		snake_case	(			self ):
							for tf_tokenizer in self.tf_tokenizers:
										SCREAMING_SNAKE_CASE_   : int					  =      tf_tokenizer(self.paired_sentences )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      tf_tokenizer(
										    text=[sentence[0] for sentence in self.paired_sentences]      ,text_pair=[sentence[1] for sentence in self.paired_sentences]      ,)
										for key in merged_outputs.keys():
													self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]      ,tf.intaa ) == separated_outputs[key] ) )






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

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






				@slow
				def 		snake_case	(			self ):
							for tf_tokenizer in self.tf_tokenizers:
										SCREAMING_SNAKE_CASE_   : int					  =      ModelToSave(tokenizer=snake_case__ )
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      tf.convert_to_tensor(self.test_sentences )
										SCREAMING_SNAKE_CASE_   : List[Any]					  =      model(snake_case__ )  # Build model with some sample inputs
										with TemporaryDirectory() as tempdir:
													SCREAMING_SNAKE_CASE_   : Any					  =      Path(snake_case__ ) / 'saved.model'
													model.save(snake_case__ )
													SCREAMING_SNAKE_CASE_   : Any					  =      tf.keras.models.load_model(snake_case__ )
										SCREAMING_SNAKE_CASE_   : Tuple					  =      loaded_model(snake_case__ )
										# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
										self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) )      ,1E-5 )








'''simple docstring'''

import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		# Initialise PyTorch model
		_a	: int								=				BigBirdConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )

		if is_trivia_qa:
				_a	: Union[str, Any]								=				BigBirdForQuestionAnswering(lowerCAmelCase_    )
		else:
				_a	: Any								=				BigBirdForPreTraining(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tf_weights_in_big_bird(lowerCAmelCase_		,     lowerCAmelCase_		,     is_trivia_qa=lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  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(
							    '''--big_bird_config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        '''The config json file corresponding to the pre-trained BERT 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.'''
							)
							parser.add_argument(
							    '''--is_trivia_qa''', action='''store_true''', help='''Whether to convert a model with a trivia_qa head.'''
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tf_checkpoint_to_pytorch(
							    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
							)




