




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     =			50_003
_A     =			50_002


@require_sentencepiece
@require_tokenizers
class  A		(  __UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   PLBartTokenizer
		__snake_case									=   None
		__snake_case									=   False



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCAmelCase_      			=  PLBartTokenizer(UpperCamelCase__,	language_codes='''base''',	keep_accents=UpperCamelCase__   )
								tokenizer.save_pretrained(self.tmpdirname   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  PLBartTokenizer(UpperCamelCase__,	language_codes='''base''',	keep_accents=UpperCamelCase__   )

								lowerCAmelCase_      			=  tokenizer.tokenize('''This is a test'''   )
								self.assertListEqual(UpperCamelCase__,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

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

								lowerCAmelCase_      			=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
								self.assertListEqual(
								    UpperCamelCase__,	[
								        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''',
								        '''.''',
								    ],	)
								lowerCAmelCase_      			=  tokenizer.convert_tokens_to_ids(UpperCamelCase__   )
								self.assertListEqual(
								    UpperCamelCase__,	[
								        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]
								    ],	)

								lowerCAmelCase_      			=  tokenizer.convert_ids_to_tokens(UpperCamelCase__   )
								self.assertListEqual(
								    UpperCamelCase__,	[
								        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>''',
								        '''.''',
								    ],	)

								lowerCAmelCase_      			=  tokenizer.vocab_size
								lowerCAmelCase_      			=  [tokenizer.convert_ids_to_tokens(UpperCamelCase__   ) for x in range(end - 4,	UpperCamelCase__   )]

								self.assertListEqual(UpperCamelCase__,	['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>''']   )

								lowerCAmelCase_      			=  '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
								lowerCAmelCase_      			=  tokenizer(UpperCamelCase__   ).input_ids
								self.assertEqual(
								    tokenizer.decode(UpperCamelCase__,	skip_special_tokens=UpperCamelCase__,	clean_up_tokenization_spaces=UpperCamelCase__   ),	UpperCamelCase__,	)







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  PLBartTokenizer(UpperCamelCase__,	language_codes='''multi''',	keep_accents=UpperCamelCase__   )

								lowerCAmelCase_      			=  tokenizer.tokenize('''This is a test'''   )
								self.assertListEqual(UpperCamelCase__,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

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

								lowerCAmelCase_      			=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
								self.assertListEqual(
								    UpperCamelCase__,	[
								        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''',
								        '''.''',
								    ],	)
								lowerCAmelCase_      			=  tokenizer.convert_tokens_to_ids(UpperCamelCase__   )
								self.assertListEqual(
								    UpperCamelCase__,	[
								        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]
								    ],	)

								lowerCAmelCase_      			=  tokenizer.convert_ids_to_tokens(UpperCamelCase__   )
								self.assertListEqual(
								    UpperCamelCase__,	[
								        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>''',
								        '''.''',
								    ],	)
								lowerCAmelCase_      			=  tokenizer.vocab_size
								lowerCAmelCase_      			=  [tokenizer.convert_ids_to_tokens(UpperCamelCase__   ) for x in range(end - 7,	UpperCamelCase__   )]

								self.assertListEqual(
								    UpperCamelCase__,	['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']   )
								lowerCAmelCase_      			=  '''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
								lowerCAmelCase_      			=  tokenizer(UpperCamelCase__   ).input_ids
								self.assertEqual(
								    tokenizer.decode(UpperCamelCase__,	skip_special_tokens=UpperCamelCase__,	clean_up_tokenization_spaces=UpperCamelCase__   ),	UpperCamelCase__,	)







@require_torch
@require_sentencepiece
@require_tokenizers
class  A		(  unittest.TestCase     ):
		__snake_case									=   'uclanlp/plbart-python-en_XX'
		__snake_case									=   [
		    'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
		    'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
		]
		__snake_case									=   [
		    'Returns the maximum value of a b c.',
		    'Sums the values of a b c.',
		]
		__snake_case									=   [
		    134,
		    5452,
		    3_3460,
		    3_3441,
		    3_3463,
		    3_3465,
		    3_3463,
		    3_3449,
		    988,
		    20,
		    3_3456,
		    19,
		    3_3456,
		    771,
		    39,
		    4258,
		    889,
		    3318,
		    3_3441,
		    3_3463,
		    3_3465,
		    3_3463,
		    3_3449,
		    2471,
		    2,
		    PYTHON_CODE,
		]



		@classmethod
		def        SCREAMING_SNAKE_CASE__			(     cls   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  PLBartTokenizer.from_pretrained(
								    cls.checkpoint_name,	language_codes='''base''',	src_lang='''python''',	tgt_lang='''en_XX'''   )
								lowerCAmelCase_      			=  1
								return cls



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens,	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.assertIn(UpperCamelCase__,	self.tokenizer.all_special_ids   )
								lowerCAmelCase_      			=  [EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
								lowerCAmelCase_      			=  self.tokenizer.decode(UpperCamelCase__,	skip_special_tokens=UpperCamelCase__   )
								lowerCAmelCase_      			=  self.tokenizer.decode(generated_ids[1:],	skip_special_tokens=UpperCamelCase__   )
								self.assertEqual(UpperCamelCase__,	UpperCamelCase__   )
								self.assertNotIn(self.tokenizer.eos_token,	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  ['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
								self.assertIsInstance(src_text[0],	UpperCamelCase__   )
								lowerCAmelCase_      			=  10
								lowerCAmelCase_      			=  self.tokenizer(UpperCamelCase__,	max_length=UpperCamelCase__,	truncation=UpperCamelCase__   ).input_ids[0]
								self.assertEqual(ids[-2],	2   )
								self.assertEqual(ids[-1],	UpperCamelCase__   )
								self.assertEqual(len(UpperCamelCase__   ),	UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']   ),	[5_0004, 5_0001]   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  tempfile.mkdtemp()
								lowerCAmelCase_      			=  self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(UpperCamelCase__   )
								lowerCAmelCase_      			=  PLBartTokenizer.from_pretrained(UpperCamelCase__   )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids,	UpperCamelCase__   )



		@require_torch
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer(self.src_text,	text_target=self.tgt_text,	padding=UpperCamelCase__,	return_tensors='''pt'''   )
								lowerCAmelCase_      			=  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],	UpperCamelCase__   )
								self.assertEqual(batch.decoder_input_ids[1][-1],	2   )
								self.assertEqual(batch.labels[1][-2:].tolist(),	[2, EN_CODE]   )



		@require_torch
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer(
								    self.src_text,	text_target=self.tgt_text,	padding=UpperCamelCase__,	truncation=UpperCamelCase__,	max_length=len(self.expected_src_tokens   ),	return_tensors='''pt''',	)
								lowerCAmelCase_      			=  shift_tokens_right(batch['''labels'''],	self.tokenizer.pad_token_id   )

								self.assertIsInstance(UpperCamelCase__,	UpperCamelCase__   )

								self.assertEqual((2, 26),	batch.input_ids.shape   )
								self.assertEqual((2, 26),	batch.attention_mask.shape   )
								lowerCAmelCase_      			=  batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens,	UpperCamelCase__   )
								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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer(self.src_text,	padding=UpperCamelCase__,	truncation=UpperCamelCase__,	max_length=3,	return_tensors='''pt'''   )
								lowerCAmelCase_      			=  self.tokenizer(
								    text_target=self.tgt_text,	padding=UpperCamelCase__,	truncation=UpperCamelCase__,	max_length=10,	return_tensors='''pt'''   )
								lowerCAmelCase_      			=  targets['''input_ids''']
								lowerCAmelCase_      			=  shift_tokens_right(UpperCamelCase__,	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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.tokenizer._build_translation_inputs(
								    '''A test''',	return_tensors='''pt''',	src_lang='''en_XX''',	tgt_lang='''java'''   )

								self.assertEqual(
								    nested_simplify(UpperCamelCase__   ),	{
								        # 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 os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  checkpoints.load_tax_checkpoint(_A  )
						lowerCAmelCase_      			=  flatten_dict(_A  )
						return flax_params





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  {}

						lowerCAmelCase_      			=  {
						    '''token_embedder''': '''embeddings''',
						    '''encoder_norm''': '''layernorm''',
						    '''kernel''': '''weight''',
						    '''.out''': '''.output''',
						    '''scale''': '''weight''',
						    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
						    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
						}

						lowerCAmelCase_      			=  {
						    '''query''': '''attention.query''',
						    '''key''': '''attention.key''',
						    '''value''': '''attention.value''',
						    '''output.dense''': '''output''',
						    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
						    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
						    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
						    '''mlp.''': '''mlp.DenseReluDense.''',
						    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
						    '''self_attention.o''': '''self_attention.attention.o''',
						    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
						    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
						    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
						    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowerCAmelCase_      			=  '''.'''.join(key[1:]  )

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowerCAmelCase_      			=  new_key.replace(_A ,  _A  )

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowerCAmelCase_      			=  new_key.replace(_A ,  _A  )

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowerCAmelCase_      			=  re.sub(r'''layers_(\d+)''' ,  r'''layer.\1''' ,  _A  )
																								lowerCAmelCase_      			=  new_key.replace('''encoder''' ,  '''encoder.encoder'''  )

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowerCAmelCase_      			=  re.sub(r'''layers_(\d+)''' ,  r'''layer.\1''' ,  _A  )

																		lowerCAmelCase_      			=  flax_dict[key]

						lowerCAmelCase_      			=  {}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowerCAmelCase_      			=  torch.from_numpy(converted_dict[key].T  )
												else:
																		lowerCAmelCase_      			=  torch.from_numpy(converted_dict[key]  )

						return converted_torch_dict





def __UpperCamelCase			(       _A ,  _A ,  _A=False ,  _A=False  ):
						lowerCAmelCase_      			=  get_flax_param(_A  )

						if not use_large:
												lowerCAmelCase_      			=  PixaStructVisionConfig()
												lowerCAmelCase_      			=  PixaStructTextConfig()
						else:
												lowerCAmelCase_      			=  PixaStructVisionConfig(
												    hidden_size=1536 ,  d_ff=3968 ,  num_attention_heads=24 ,  num_hidden_layers=18  )
												lowerCAmelCase_      			=  PixaStructTextConfig(hidden_size=1536 ,  d_ff=3968 ,  num_heads=24 ,  num_layers=18  )
						lowerCAmelCase_      			=  PixaStructConfig(
						    vision_config=encoder_config.to_dict() ,  text_config=decoder_config.to_dict() ,  is_vqa=_A  )

						lowerCAmelCase_      			=  PixaStructForConditionalGeneration(_A  )

						lowerCAmelCase_      			=  rename_and_convert_flax_params(_A  )
						model.load_state_dict(_A  )

						lowerCAmelCase_      			=  AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''  )
						lowerCAmelCase_      			=  PixaStructImageProcessor()
						lowerCAmelCase_      			=  PixaStructProcessor(image_processor=_A ,  tokenizer=_A  )

						if use_large:
												lowerCAmelCase_      			=  4096

						lowerCAmelCase_      			=  True

						# mkdir if needed
						os.makedirs(_A ,  exist_ok=_A  )

						model.save_pretrained(_A  )
						processor.save_pretrained(_A  )

						print('''Model saved in {}'''.format(_A  )  )


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
						parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
						_A     =			parser.parse_args()

						convert_pixastruct_original_pytorch_checkpoint_to_hf(
						    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
						)




