


'''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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):

			'''simple docstring'''





			UpperCAmelCase__  :					Union[str, Any]        =     BigBirdConfig.from_json_file(UpperCamelCase__					)
			print(f'''Building PyTorch model from configuration: {config}'''					)

			if is_trivia_qa:
						UpperCAmelCase__  :					str        =     BigBirdForQuestionAnswering(UpperCamelCase__					)
			else:
						UpperCAmelCase__  :					Optional[Any]        =     BigBirdForPreTraining(UpperCamelCase__					)

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

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


if __name__ == "__main__":
		__A	=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.'
		)
		__A	=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
		)



'''simple docstring'''



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))