



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Any     =    BartTokenizer
					__A  :     str     =    BartTokenizerFast
					__A  :     Optional[int]     =    True
					__A  :     Optional[int]     =    filter_roberta_detectors







					def 			_lowercase      (							self   :  Dict):
											super().setUp()
											A__						:				Union[str, Any]         =			[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											A__						:				List[str]         =			dict(zip(_A      ,		range(len(_A))))
											A__						:				List[str]         =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											A__						:				str         =			{"unk_token": "<unk>"}

											A__						:				Dict         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["vocab_file"])
											A__						:				Optional[Any]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["merges_file"])
											with open(self.vocab_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write(json.dumps(_A) + "\n")
											with open(self.merges_file      ,		"w"      ,		encoding="utf-8") as fp:
																	fp.write("\n".join(_A))







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  str):
											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Union[str, Any]      ,		**_A   :  List[Any]):
											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,		**_A)







					def 			_lowercase      (							self   :  Tuple      ,		_A   :  Union[str, Any]):
											return "lower newer", "lower newer"







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizer.from_pretrained("facebook/bart-large")







					@cached_property
					def 			_lowercase      (							self   :  Optional[int]):
											return BartTokenizerFast.from_pretrained("facebook/bart-large")







					@require_torch
					def 			_lowercase      (							self   :  Dict):
											A__						:				Optional[int]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											A__						:				List[Any]         =			[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		max_length=len(_A)      ,		padding=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)

																	self.assertEqual((2, 9)      ,		batch.input_ids.shape)
																	self.assertEqual((2, 9)      ,		batch.attention_mask.shape)
																	A__						:				Dict         =			batch.input_ids.tolist()[0]
																	self.assertListEqual(_A      ,		_A)
																	# Test that special tokens are reset







					@require_torch
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Optional[Any]         =			["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Tuple         =			tokenizer(_A      ,		padding=_A      ,		return_tensors="pt")
																	# check if input_ids are returned and no labels
																	self.assertIn("input_ids"      ,		_A)
																	self.assertIn("attention_mask"      ,		_A)
																	self.assertNotIn("labels"      ,		_A)
																	self.assertNotIn("decoder_attention_mask"      ,		_A)







					@require_torch
					def 			_lowercase      (							self   :  int):
											A__						:				Optional[Any]         =			[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				int         =			tokenizer(text_target=_A      ,		max_length=32      ,		padding="max_length"      ,		return_tensors="pt")
																	self.assertEqual(32      ,		targets["input_ids"].shape[1])







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				List[str]         =			tokenizer(
																	    ["I am a small frog" * 1024, "I am a small frog"]      ,		padding=_A      ,		truncation=_A      ,		return_tensors="pt")
																	self.assertIsInstance(_A      ,		_A)
																	self.assertEqual(batch.input_ids.shape      ,		(2, 1024))







					@require_torch
					def 			_lowercase      (							self   :  List[str]):
											A__						:				int         =			["A long paragraph for summarization."]
											A__						:				List[str]         =			[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	A__						:				Optional[Any]         =			tokenizer(_A      ,		return_tensors="pt")
																	A__						:				Union[str, Any]         =			tokenizer(text_target=_A      ,		return_tensors="pt")
																	A__						:				Tuple         =			inputs["input_ids"]
																	A__						:				str         =			targets["input_ids"]
																	self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
																	self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
																	self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







					def 			_lowercase      (							self   :  Optional[int]):
											pass







					def 			_lowercase      (							self   :  Any):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
																							A__						:				Union[str, Any]         =			self.rust_tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				int         =			self.tokenizer_class.from_pretrained(_A      ,		**_A)
																							A__						:				Union[str, Any]         =			"A, <mask> AllenNLP sentence."
																							A__						:				Dict         =			tokenizer_r.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)
																							A__						:				Optional[Any]         =			tokenizer_p.encode_plus(_A      ,		add_special_tokens=_A      ,		return_token_type_ids=_A)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r["token_type_ids"])      ,		sum(tokens_p["token_type_ids"]))

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])      ,		sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])      ,		)

																							A__						:				Optional[int]         =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																							A__						:				Dict         =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																							self.assertSequenceEqual(tokens_r["input_ids"]      ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																							self.assertSequenceEqual(
																							    _A      ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __lowercase				,  __lowercase				,  model_class="bert"				,  is_encoder_named_decoder=__lowercase				,  is_encoder=__lowercase				,  )

						# Save pytorch-model
						print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
						model.save_pretrained(__lowercase							)


if __name__ == "__main__":
		snake_case     :    List[Any]						=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--is_encoder_named_decoder',
		    action='store_true',
		    help='If decoder has to be renamed to encoder in PyTorch model.',
		)
		parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
		parser.add_argument('--vocab_size', default=5_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_hub_path,
		    args.pytorch_dump_path,
		    args.is_encoder_named_decoder,
		    args.vocab_size,
		    is_encoder=args.is_encoder,
		)