

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_00_03
__A      		=     5_00_02



@require_sentencepiece
@require_tokenizers
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		str										=    PLBartTokenizer
      UpperCamelCase_      :		Tuple										=    None
      UpperCamelCase_      :		Tuple										=    False



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					str:
        super().setUp()

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



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[str]:
        _snake_case						=							PLBartTokenizer(A__      , language_codes='''base'''      , keep_accents=A__		)

        _snake_case						=							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]]      , )

        _snake_case						=							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''',
                '''.''',
            ]      , )
        _snake_case						=							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]
            ]      , )

        _snake_case						=							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>''',
                '''.''',
            ]      , )

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

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

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







      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[int]:
        _snake_case						=							PLBartTokenizer(A__      , language_codes='''multi'''      , keep_accents=A__		)

        _snake_case						=							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]]      , )

        _snake_case						=							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''',
                '''.''',
            ]      , )
        _snake_case						=							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]
            ]      , )

        _snake_case						=							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>''',
                '''.''',
            ]      , )
        _snake_case						=							tokenizer.vocab_size
        _snake_case						=							[tokenizer.convert_ids_to_tokens(A__		) for x in range(end - 7      , A__		)]

        self.assertListEqual(
            A__      , ['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__''']		)
        _snake_case						=							'''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
        _snake_case						=							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   lowercase_   (  unittest.TestCase					):
      UpperCamelCase_      :		Any										=    "uclanlp/plbart-python-en_XX"
      UpperCamelCase_      :		List[Any]										=    [
          "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
          "def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
      ]
      UpperCamelCase_      :		Optional[Any]										=    [
          "Returns the maximum value of a b c.",
          "Sums the values of a b c.",
      ]
      UpperCamelCase_      :		List[Any]										=    [
          1_3_4,
          5_4_5_2,
          3_3_4_6_0,
          3_3_4_4_1,
          3_3_4_6_3,
          3_3_4_6_5,
          3_3_4_6_3,
          3_3_4_4_9,
          9_8_8,
          2_0,
          3_3_4_5_6,
          1_9,
          3_3_4_5_6,
          7_7_1,
          3_9,
          4_2_5_8,
          8_8_9,
          3_3_1_8,
          3_3_4_4_1,
          3_3_4_6_3,
          3_3_4_6_5,
          3_3_4_6_3,
          3_3_4_4_9,
          2_4_7_1,
          2,
          PYTHON_CODE,
      ]



      @classmethod
      def 						UpperCamelCase_		(      cls				:    Any		)   ->					Optional[Any]:
        _snake_case						=							PLBartTokenizer.from_pretrained(
            cls.checkpoint_name      , language_codes='''base'''      , src_lang='''python'''      , tgt_lang='''en_XX'''		)
        _snake_case						=							1
        return cls



      def 						UpperCamelCase_		(      self				:    str		)   ->					str:
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__''']      , 50001		)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__''']      , 50002		)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__''']      , 50003		)



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Union[str, Any]:
        _snake_case						=							self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
        self.assertListEqual(self.expected_src_tokens      , A__		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Tuple:
        self.assertIn(A__      , self.tokenizer.all_special_ids		)
        _snake_case						=							[EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
        _snake_case						=							self.tokenizer.decode(A__      , skip_special_tokens=A__		)
        _snake_case						=							self.tokenizer.decode(generated_ids[1:]      , skip_special_tokens=A__		)
        self.assertEqual(A__      , A__		)
        self.assertNotIn(self.tokenizer.eos_token      , A__		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[Any]:
        _snake_case						=							['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
        self.assertIsInstance(src_text[0]      , A__		)
        _snake_case						=							10
        _snake_case						=							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				:    Any		)   ->					Optional[int]:
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__''']		)      , [50004, 50001]		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					List[str]:
        _snake_case						=							tempfile.mkdtemp()
        _snake_case						=							self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(A__		)
        _snake_case						=							PLBartTokenizer.from_pretrained(A__		)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids      , A__		)



      @require_torch
      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Optional[Any]:
        _snake_case						=							self.tokenizer(self.src_text      , text_target=self.tgt_text      , padding=A__      , return_tensors='''pt'''		)
        _snake_case						=							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				:    Optional[Any]		)   ->					Any:
        _snake_case						=							self.tokenizer(
            self.src_text      , text_target=self.tgt_text      , padding=A__      , truncation=A__      , max_length=len(self.expected_src_tokens		)      , return_tensors='''pt'''      , )
        _snake_case						=							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		)
        _snake_case						=							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				:    str		)   ->					str:
        _snake_case						=							self.tokenizer(self.src_text      , padding=A__      , truncation=A__      , max_length=3      , return_tensors='''pt'''		)
        _snake_case						=							self.tokenizer(
            text_target=self.tgt_text      , padding=A__      , truncation=A__      , max_length=10      , return_tensors='''pt'''		)
        _snake_case						=							targets['''input_ids''']
        _snake_case						=							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				:    str		)   ->					Any:
        _snake_case						=							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, 50003]],
                '''attention_mask''': [[1, 1, 1, 1]],
                # java
                '''forced_bos_token_id''': 50001,
            }      , )




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 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							checkpoints.load_tax_checkpoint(_UpperCamelCase					)
  _snake_case						=							flatten_dict(_UpperCamelCase					)
  return flax_params



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							{}

  _snake_case						=							{
      '''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''',
  }

  _snake_case						=							{
      '''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
      _snake_case						=							'''.'''.join(key[1:]					)

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

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

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

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

      _snake_case						=							flax_dict[key]

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

  return converted_torch_dict



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=False			,	_UpperCamelCase=False					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							get_flax_param(_UpperCamelCase					)

  if not use_large:
    _snake_case						=							PixaStructVisionConfig()
    _snake_case						=							PixaStructTextConfig()
  else:
    _snake_case						=							PixaStructVisionConfig(
        hidden_size=1_536			,	d_ff=3_968			,	num_attention_heads=24			,	num_hidden_layers=18					)
    _snake_case						=							PixaStructTextConfig(hidden_size=1_536			,	d_ff=3_968			,	num_heads=24			,	num_layers=18					)
  _snake_case						=							PixaStructConfig(
      vision_config=encoder_config.to_dict()			,	text_config=decoder_config.to_dict()			,	is_vqa=_UpperCamelCase					)

  _snake_case						=							PixaStructForConditionalGeneration(_UpperCamelCase					)

  _snake_case						=							rename_and_convert_flax_params(_UpperCamelCase					)
  model.load_state_dict(_UpperCamelCase					)

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

  if use_large:
    _snake_case						=							4_096

  _snake_case						=							True

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

  model.save_pretrained(_UpperCamelCase					)
  processor.save_pretrained(_UpperCamelCase					)

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


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
 )

