




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__							:	Optional[int]											=		get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase  (							UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  PegasusTokenizer
						lowerCAmelCase_							=  PegasusTokenizerFast
						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  True






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						       =	PegasusTokenizer(__lowercase		)
							tokenizer.save_pretrained(self.tmpdirname		)






						@cached_property
						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return PegasusTokenizer.from_pretrained("google/pegasus-large"		)






						def 			snake_case__		(		self				:	Optional[Any]			,      **__lowercase				:	Optional[Any]		):






							"""simple docstring"""






							return PegasusTokenizer.from_pretrained(self.tmpdirname			,      **__lowercase		)






						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	Dict		):






							"""simple docstring"""






							return ("This is a test", "This is a test")






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	"</s>"
							snake_case_						       =	1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase		)			,      __lowercase		)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase		)			,      __lowercase		)






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	list(self.get_tokenizer().get_vocab().keys()		)

							self.assertEqual(vocab_keys[0]			,      "<pad>"		)
							self.assertEqual(vocab_keys[1]			,      "</s>"		)
							self.assertEqual(vocab_keys[-1]			,      "v"		)
							self.assertEqual(len(__lowercase		)			,      11_03		)






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							self.assertEqual(self.get_tokenizer().vocab_size			,      11_03		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
							snake_case_						       =	self.tokenizer_class.from_pretrained(self.tmpdirname		)
							snake_case_						       =	(
							    "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
							    " </s> <pad> <pad> <pad>"
							)
							snake_case_						       =	rust_tokenizer([raw_input_str]			,      return_tensors=__lowercase			,      add_special_tokens=__lowercase		).input_ids[0]
							snake_case_						       =	py_tokenizer([raw_input_str]			,      return_tensors=__lowercase			,      add_special_tokens=__lowercase		).input_ids[0]
							self.assertListEqual(__lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							snake_case_						       =	"<mask_1> To ensure a <mask_2> flow of bank resolutions."
							snake_case_						       =	[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
							snake_case_						       =	tokenizer([raw_input_str]			,      return_tensors=__lowercase		).input_ids[0]
							self.assertListEqual(__lowercase			,      __lowercase		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 9_61_03
							assert tokenizer.pad_token_id == 0
							assert tokenizer.eos_token_id == 1
							assert tokenizer.offset == 1_03
							assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
							assert tokenizer.unk_token == "<unk>"
							assert tokenizer.model_max_length == 10_24
							snake_case_						       =	"To ensure a smooth flow of bank resolutions."
							snake_case_						       =	[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
							snake_case_						       =	tokenizer([raw_input_str]			,      return_tensors=__lowercase		).input_ids[0]
							self.assertListEqual(__lowercase			,      __lowercase		)
							assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






						@require_torch
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	["This is going to be way too long." * 1_50, "short example"]
							snake_case_						       =	["not super long but more than 5 tokens", "tiny"]
							snake_case_						       =	self._large_tokenizer(__lowercase			,      padding=__lowercase			,      truncation=__lowercase			,      return_tensors="pt"		)
							snake_case_						       =	self._large_tokenizer(
							    text_target=__lowercase			,      max_length=5			,      padding=__lowercase			,      truncation=__lowercase			,      return_tensors="pt"		)

							assert batch.input_ids.shape == (2, 10_24)
							assert batch.attention_mask.shape == (2, 10_24)
							assert targets["input_ids"].shape == (2, 5)
							assert len(__lowercase		) == 2  # input_ids, attention_mask.






						@slow
						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	{"input_ids": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=__lowercase			,      model_name="google/bigbird-pegasus-large-arxiv"			,      revision="ba85d0851d708441f91440d509690f1ab6353415"			,      )







@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase  (							UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  PegasusTokenizer
						lowerCAmelCase_							=  PegasusTokenizerFast
						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  True






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						       =	PegasusTokenizer(__lowercase			,      offset=0			,      mask_token_sent=__lowercase			,      mask_token="[MASK]"		)
							tokenizer.save_pretrained(self.tmpdirname		)






						@cached_property
						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"		)






						def 			snake_case__		(		self				:	Tuple			,      **__lowercase				:	List[Any]		):






							"""simple docstring"""






							return PegasusTokenizer.from_pretrained(self.tmpdirname			,      **__lowercase		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Dict		):






							"""simple docstring"""






							return ("This is a test", "This is a test")






						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
							snake_case_						       =	self.tokenizer_class.from_pretrained(self.tmpdirname		)
							snake_case_						       =	(
							    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
							    " <pad> <pad> <pad>"
							)
							snake_case_						       =	rust_tokenizer([raw_input_str]			,      return_tensors=__lowercase			,      add_special_tokens=__lowercase		).input_ids[0]
							snake_case_						       =	py_tokenizer([raw_input_str]			,      return_tensors=__lowercase			,      add_special_tokens=__lowercase		).input_ids[0]
							self.assertListEqual(__lowercase			,      __lowercase		)






						@require_torch
						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							snake_case_						       =	["This is going to be way too long." * 10_00, "short example"]
							snake_case_						       =	["not super long but more than 5 tokens", "tiny"]
							snake_case_						       =	self._large_tokenizer(__lowercase			,      padding=__lowercase			,      truncation=__lowercase			,      return_tensors="pt"		)
							snake_case_						       =	self._large_tokenizer(
							    text_target=__lowercase			,      max_length=5			,      padding=__lowercase			,      truncation=__lowercase			,      return_tensors="pt"		)

							assert batch.input_ids.shape == (2, 40_96)
							assert batch.attention_mask.shape == (2, 40_96)
							assert targets["input_ids"].shape == (2, 5)
							assert len(__lowercase		) == 2  # input_ids, attention_mask.






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	(
							    "This is an example string that is used to test the original TF implementation against the HF"
							    " implementation"
							)

							snake_case_						       =	self._large_tokenizer(__lowercase		).input_ids

							self.assertListEqual(
							    __lowercase			,      [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]			,      )






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	MobileBertConfig.from_json_file(_A				)
	print(f"Building PyTorch model from configuration: {config}"				)
	snake_case_						       =	MobileBertForPreTraining(_A				)
	# Load weights from tf checkpoint
	snake_case_						       =	load_tf_weights_in_mobilebert(_A	,      _A	,      _A				)
	# Save pytorch-model
	print(f"Save PyTorch model to {pytorch_dump_path}"				)
	torch.save(model.state_dict()	,      _A				)


if __name__ == "__main__":
		lowercase__							:	Dict											=		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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT 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."
		)
		lowercase__							:	Tuple											=		parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
