

import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_      =     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
			from transformers.models.plbart.modeling_plbart import shift_tokens_right

a_      =     5_0003
a_      =     5_0002






@require_sentencepiece
@require_tokenizers
class _UpperCamelCase				(				__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =PLBartTokenizer
			lowerCamelCase__							     =None
			lowerCamelCase__							     =False
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Dict:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				SCREAMING_SNAKE_CASE :   int					  =							PLBartTokenizer(a   ,   language_codes="base"   ,   keep_accents=a						)
				tokenizer.save_pretrained(self.tmpdirname						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							PLBartTokenizer(a   ,   language_codes="base"   ,   keep_accents=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							tokenizer.tokenize("This is a test"						)
				self.assertListEqual(a   ,   ["▁This", "▁is", "▁a", "▁t", "est"]						)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(a						)   ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,   )

				SCREAMING_SNAKE_CASE :   int					  =							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
				self.assertListEqual(
				    a   ,   [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]   ,   )
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							tokenizer.convert_tokens_to_ids(a						)
				self.assertListEqual(
				    a   ,   [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   int					  =							tokenizer.convert_ids_to_tokens(a						)
				self.assertListEqual(
				    a   ,   [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   List[str]					  =							tokenizer.vocab_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							[tokenizer.convert_ids_to_tokens(a						) for x in range(end - 4   ,   a						)]

				self.assertListEqual(a   ,   ["__java__", "__python__", "__en_XX__", "<mask>"]						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
				SCREAMING_SNAKE_CASE :   Any					  =							tokenizer(a						).input_ids
				self.assertEqual(
				    tokenizer.decode(a   ,   skip_special_tokens=a   ,   clean_up_tokenization_spaces=a						)   ,   a   ,   )






			def 				__UpperCamelCase			(      self				:			List[str]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							PLBartTokenizer(a   ,   language_codes="multi"   ,   keep_accents=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							tokenizer.tokenize("This is a test"						)
				self.assertListEqual(a   ,   ["▁This", "▁is", "▁a", "▁t", "est"]						)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(a						)   ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,   )

				SCREAMING_SNAKE_CASE :   Tuple					  =							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
				self.assertListEqual(
				    a   ,   [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]   ,   )
				SCREAMING_SNAKE_CASE :   int					  =							tokenizer.convert_tokens_to_ids(a						)
				self.assertListEqual(
				    a   ,   [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tokenizer.convert_ids_to_tokens(a						)
				self.assertListEqual(
				    a   ,   [
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]   ,   )
				SCREAMING_SNAKE_CASE :   List[Any]					  =							tokenizer.vocab_size
				SCREAMING_SNAKE_CASE :   Dict					  =							[tokenizer.convert_ids_to_tokens(a						) for x in range(end - 7   ,   a						)]

				self.assertListEqual(
				    a   ,   ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							tokenizer(a						).input_ids
				self.assertEqual(
				    tokenizer.decode(a   ,   skip_special_tokens=a   ,   clean_up_tokenization_spaces=a						)   ,   a   ,   )






@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     ='uclanlp/plbart-python-en_XX'
			lowerCamelCase__							     =[
			    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
			    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
			]
			lowerCamelCase__							     =[
			    'Returns the maximum value of a b c.',
			    'Sums the values of a b c.',
			]
			lowerCamelCase__							     =[
			    134,
			    5452,
			    33460,
			    33441,
			    33463,
			    33465,
			    33463,
			    33449,
			    988,
			    20,
			    33456,
			    19,
			    33456,
			    771,
			    39,
			    4258,
			    889,
			    3318,
			    33441,
			    33463,
			    33465,
			    33463,
			    33449,
			    2471,
			    2,
			    PYTHON_CODE,
			]
			@classmethod
			def 				__UpperCamelCase			(      cls				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   PLBartTokenizer					  =							PLBartTokenizer.from_pretrained(
				    cls.checkpoint_name   ,   language_codes="base"   ,   src_lang="python"   ,   tgt_lang="en_XX"						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							1
				return cls
			def 				__UpperCamelCase			(      self				:			Any						)				->							Optional[int]:

				"""simple docstring"""

				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]   ,   5_0001						)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]   ,   5_0002						)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]   ,   5_0003						)
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
				self.assertListEqual(self.expected_src_tokens   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				self.assertIn(a   ,   self.tokenizer.all_special_ids						)
				SCREAMING_SNAKE_CASE :   Dict					  =							[EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
				SCREAMING_SNAKE_CASE :   str					  =							self.tokenizer.decode(a   ,   skip_special_tokens=a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.tokenizer.decode(generated_ids[1:]   ,   skip_special_tokens=a						)
				self.assertEqual(a   ,   a						)
				self.assertNotIn(self.tokenizer.eos_token   ,   a						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
				self.assertIsInstance(src_text[0]   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							10
				SCREAMING_SNAKE_CASE :   Dict					  =							self.tokenizer(a   ,   max_length=a   ,   truncation=a						).input_ids[0]
				self.assertEqual(ids[-2]   ,   2						)
				self.assertEqual(ids[-1]   ,   a						)
				self.assertEqual(len(a						)   ,   a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]						)   ,   [5_0004, 5_0001]						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							tempfile.mkdtemp()
				SCREAMING_SNAKE_CASE :   Any					  =							self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							PLBartTokenizer.from_pretrained(a						)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,   a						)
			@require_torch
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.tokenizer(self.src_text   ,   text_target=self.tgt_text   ,   padding=a   ,   return_tensors="pt"						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							shift_tokens_right(batch["labels"]   ,   self.tokenizer.pad_token_id						)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				self.assertEqual(batch.input_ids[1][-2:].tolist()   ,   [2, PYTHON_CODE]						)
				self.assertEqual(batch.decoder_input_ids[1][0]   ,   a						)
				self.assertEqual(batch.decoder_input_ids[1][-1]   ,   2						)
				self.assertEqual(batch.labels[1][-2:].tolist()   ,   [2, EN_CODE]						)
			@require_torch
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							self.tokenizer(
				    self.src_text   ,   text_target=self.tgt_text   ,   padding=a   ,   truncation=a   ,   max_length=len(self.expected_src_tokens						)   ,   return_tensors="pt"   ,   )
				SCREAMING_SNAKE_CASE :   Tuple					  =							shift_tokens_right(batch["labels"]   ,   self.tokenizer.pad_token_id						)

				self.assertIsInstance(a   ,   a						)

				self.assertEqual((2, 26)   ,   batch.input_ids.shape						)
				self.assertEqual((2, 26)   ,   batch.attention_mask.shape						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens   ,   a						)
				self.assertEqual(2   ,   batch.decoder_input_ids[0, -1]						)  # EOS
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens   ,   []						)
				self.assertEqual(self.tokenizer.suffix_tokens   ,   [self.tokenizer.eos_token_id, PYTHON_CODE]						)
			def 				__UpperCamelCase			(      self				:			Dict						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.tokenizer(self.src_text   ,   padding=a   ,   truncation=a   ,   max_length=3   ,   return_tensors="pt"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							self.tokenizer(
				    text_target=self.tgt_text   ,   padding=a   ,   truncation=a   ,   max_length=10   ,   return_tensors="pt"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							targets["input_ids"]
				SCREAMING_SNAKE_CASE :   Dict					  =							shift_tokens_right(a   ,   self.tokenizer.pad_token_id						)

				self.assertEqual(batch.input_ids.shape[1]   ,   3						)
				self.assertEqual(batch.decoder_input_ids.shape[1]   ,   10						)






			@require_torch
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.tokenizer._build_translation_inputs(
				    "A test"   ,   return_tensors="pt"   ,   src_lang="en_XX"   ,   tgt_lang="java"						)

				self.assertEqual(
				    nested_simplify(a						)   ,   {
				        # A, test, EOS, en_XX
				        "input_ids": [[150, 242, 2, 5_0003]],
				        "attention_mask": [[1, 1, 1, 1]],
				        # java
				        "forced_bos_token_id": 5_0001,
				    }   ,   )


import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
a_      =     logging.getLogger(__name__)


a_      =     'Hello world! cécé herlolip'


a_      =     namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							BertAbsConfig(
	    temp_dir="."				,      finetune_bert=_a				,      large=_a				,      share_emb=_a				,      use_bert_emb=_a				,      encoder="bert"				,      max_pos=512				,      enc_layers=6				,      enc_hidden_size=512				,      enc_heads=8				,      enc_ff_size=512				,      enc_dropout=0.2				,      dec_layers=6				,      dec_hidden_size=768				,      dec_heads=8				,      dec_ff_size=2048				,      dec_dropout=0.2				,      )
	SCREAMING_SNAKE_CASE :   Dict					  =							torch.load(_a				,      lambda _a				,      _a: storage)
	SCREAMING_SNAKE_CASE :   str					  =							AbsSummarizer(_a				,      torch.device("cpu")				,      _a)
	original.eval()

	SCREAMING_SNAKE_CASE :   List[str]					  =							BertAbsSummarizer(_a				,      torch.device("cpu"))
	new_model.eval()

	# -------------------
	# Convert the weights
	# -------------------

	logging.info("convert the model")
	new_model.bert.load_state_dict(original.bert.state_dict())
	new_model.decoder.load_state_dict(original.decoder.state_dict())
	new_model.generator.load_state_dict(original.generator.state_dict())

	# ----------------------------------
	# Make sure the outpus are identical
	# ----------------------------------

	logging.info("Make sure that the models' outputs are identical")
	SCREAMING_SNAKE_CASE :   List[str]					  =							BertTokenizer.from_pretrained("bert-base-uncased")

	# prepare the model inputs
	SCREAMING_SNAKE_CASE :   List[str]					  =							tokenizer.encode("This is sample éàalj'-.")
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).unsqueeze(0)
	SCREAMING_SNAKE_CASE :   List[Any]					  =							tokenizer.encode("This is sample 3 éàalj'-.")
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_a)))
	SCREAMING_SNAKE_CASE :   int					  =							torch.tensor(_a).unsqueeze(0)

	# failsafe to make sure the weights reset does not affect the
	# loaded weights.
	assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0

	# forward pass
	SCREAMING_SNAKE_CASE :   List[Any]					  =							encoder_input_ids
	SCREAMING_SNAKE_CASE :   List[Any]					  =							decoder_input_ids
	SCREAMING_SNAKE_CASE :   Dict					  =							None
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							None
	SCREAMING_SNAKE_CASE :   List[Any]					  =							None
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							None
	SCREAMING_SNAKE_CASE :   Dict					  =							None

	# The original model does not apply the geneator layer immediatly but rather in
	# the beam search (where it combines softmax + linear layer). Since we already
	# apply the softmax in our generation process we only apply the linear layer here.
	# We make sure that the outputs of the full stack are identical
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							original(_a				,      _a				,      _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Dict					  =							original.generator(_a)

	SCREAMING_SNAKE_CASE :   Any					  =							new_model(
	    _a				,      _a				,      _a				,      _a				,      _a)[0]
	SCREAMING_SNAKE_CASE :   Tuple					  =							new_model.generator(_a)

	SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.max(torch.abs(output_converted_model - output_original_model)).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_a))
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
	print("Maximum absolute difference beween weights: {:.2f}".format(_a))

	SCREAMING_SNAKE_CASE :   int					  =							torch.allclose(_a				,      _a				,      atol=1E-3)
	if are_identical:
		logging.info("all weights are equal up to 1e-3")
	else:
		raise ValueError("the weights are different. The new model is likely different from the original one.")

	# The model has been saved with torch.save(model) and this is bound to the exact
	# directory structure. We save the state_dict instead.
	logging.info("saving the model's state dictionary")
	torch.save(
	    new_model.state_dict()				,      "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin")


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()
			parser.add_argument(
			    '--bertabs_checkpoint_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path the official PyTorch dump.',
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=str,
			    required=True,
			    help='Path to the output PyTorch model.',
			)
			a_      =     parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)