






import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
							import torch

							from transformers.generation import (
							    MaxLengthCriteria,
							    MaxNewTokensCriteria,
							    MaxTimeCriteria,
							    StoppingCriteriaList,
							    validate_stopping_criteria,
							)






@require_torch
class 			_UpperCamelCase(       unittest.TestCase		):

		def    __lowerCAmelCase  (					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  Any									=     3
					__a						:  int									=     2_5_0

					__a						:  Union[str, Any]									=     ids_tensor((batch_size, length)    ,       SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     torch.ones((batch_size, length)    ,       device=SCREAMING_SNAKE_CASE__    ,       dtype=torch.float    ) / length
					return input_ids, scores

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						,					__a						:  List[str]									=     self._get_tensors(5    )

					__a						:  List[str]									=     StoppingCriteriaList(
					    [
					        MaxLengthCriteria(max_length=1_0    ),
					        MaxTimeCriteria(max_time=0.1    ),
					    ]    )

					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  str									=     self._get_tensors(9    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  List[Any]									=     self._get_tensors(1_0    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  Any									=     MaxLengthCriteria(max_length=1_0    )

					__a						,					__a						:  Optional[Any]									=     self._get_tensors(5    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  Any									=     self._get_tensors(9    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  List[str]									=     self._get_tensors(1_0    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					__a						:  Dict									=     MaxNewTokensCriteria(start_length=5    ,       max_new_tokens=5    )

					__a						,					__a						:  List[Any]									=     self._get_tensors(5    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  Union[str, Any]									=     self._get_tensors(9    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						:  Any									=     self._get_tensors(1_0    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						:  Union[str, Any]									=     StoppingCriteriaList([criteria]    )
					self.assertEqual(criteria_list.max_length    ,       1_0    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ):



					'''simple docstring'''



					__a						,					__a						:  Tuple									=     self._get_tensors(5    )

					__a						:  Dict									=     MaxTimeCriteria(max_time=0.1    )
					self.assertFalse(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )

					__a						:  List[Any]									=     MaxTimeCriteria(max_time=0.1    ,       initial_timestamp=time.time() - 0.2    )
					self.assertTrue(criteria(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )    )



		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0    )]    )    ,       1_0    )

					with self.assertWarns(SCREAMING_SNAKE_CASE__    ):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0    )]    )    ,       1_1    )

					__a						:  List[str]									=     validate_stopping_criteria(StoppingCriteriaList()    ,       1_1    )

					self.assertEqual(len(SCREAMING_SNAKE_CASE__    )    ,       1    )









'''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 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							BigBirdConfig.from_json_file(SCREAMING_SNAKE_CASE							)
     print(f"Building PyTorch model from configuration: {config}"							)

     if is_trivia_qa:
          __A	:       List[str]      =							BigBirdForQuestionAnswering(SCREAMING_SNAKE_CASE							)
     else:
          __A	:       List[Any]      =							BigBirdForPreTraining(SCREAMING_SNAKE_CASE							)

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

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


if __name__ == "__main__":
      _UpperCamelCase 							=  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."""
      )
      _UpperCamelCase 							=  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
      )

