





"""simple docstring"""





import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__				:Union[str, Any]				   =       get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__				:Union[str, Any]				   =       get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__				:Dict				   =       'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class 							snake_case							(			__UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Union[str, Any]         =       CamembertTokenizer
      _A     :       Optional[int]         =       CamembertTokenizerFast
      _A     :       str         =       True
      _A     :       str         =       True

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        super().setUp()

        # We have a SentencePiece fixture for testing
        __UpperCAmelCase    :						List[str]       =						CamembertTokenizer(__lowercase )
        tokenizer.save_pretrained(self.tmpdirname )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						'''<pad>'''
        __UpperCAmelCase    :						str       =						1

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

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0]						,				'''<s>NOTUSED''' )
        self.assertEqual(vocab_keys[1]						,				'''<pad>''' )
        self.assertEqual(vocab_keys[-1]						,				'''<mask>''' )
        self.assertEqual(len(__lowercase )						,				1_004 )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        self.assertEqual(self.get_tokenizer().vocab_size						,				1_005 )

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						CamembertTokenizer(__lowercase )
        tokenizer.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						Dict       =						CamembertTokenizerFast.from_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Optional[Any]       =						'''I was born in 92000, and this is falsé.'''

        __UpperCAmelCase    :						str       =						tokenizer.encode(__lowercase )
        __UpperCAmelCase    :						Dict       =						rust_tokenizer.encode(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						List[Any]       =						tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        __UpperCAmelCase    :						List[str]       =						rust_tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        __UpperCAmelCase    :						Union[str, Any]       =						tokenizer.convert_ids_to_tokens(__lowercase )
        __UpperCAmelCase    :						List[str]       =						rust_tokenizer.tokenize(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        if not self.test_rust_tokenizer:
          return

        __UpperCAmelCase    :						str       =						self.get_tokenizer()
        __UpperCAmelCase    :						List[str]       =						self.get_rust_tokenizer()

        __UpperCAmelCase    :						Optional[int]       =						'''I was born in 92000, and this is falsé.'''

        __UpperCAmelCase    :						Optional[Any]       =						tokenizer.tokenize(__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						rust_tokenizer.tokenize(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        __UpperCAmelCase    :						int       =						rust_tokenizer.encode(__lowercase						,				add_special_tokens=__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )

        __UpperCAmelCase    :						int       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Tuple       =						tokenizer.encode(__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						rust_tokenizer.encode(__lowercase )
        self.assertListEqual(__lowercase						,				__lowercase )







      @slow
      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						{'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], '''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, 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, 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]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        __UpperCAmelCase    :						Tuple       =						[
            '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
            '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
            '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
            '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
            '''telles que la traduction et la synthèse de texte.''',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=__lowercase						,				model_name='''camembert-base'''						,				revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''						,				sequences=__lowercase						,				)






"""simple docstring"""





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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						checkpoints.load_tax_checkpoint(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						flatten_dict(UpperCAmelCase_  )
  return flax_params


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{}

  __UpperCAmelCase    :						Any       =						{
      '''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''',
  }

  __UpperCAmelCase    :						Optional[int]       =						{
      '''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
      __UpperCAmelCase    :						Optional[Any]       =						'''.'''.join(key[1:]  )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __UpperCAmelCase    :						List[str]       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __UpperCAmelCase    :						Dict       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __UpperCAmelCase    :						Dict       =						re.sub(R'''layers_(\d+)'''  ,			R'''layer.\1'''  ,			UpperCAmelCase_  )
        __UpperCAmelCase    :						Union[str, Any]       =						new_key.replace('''encoder'''  ,			'''encoder.encoder'''  )

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

      __UpperCAmelCase    :						Dict       =						flax_dict[key]

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

  return converted_torch_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						get_flax_param(UpperCAmelCase_  )

  if not use_large:
    __UpperCAmelCase    :						List[str]       =						PixaStructVisionConfig()
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig()
  else:
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructVisionConfig(
        hidden_size=15_36  ,			d_ff=39_68  ,			num_attention_heads=24  ,			num_hidden_layers=18  )
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig(hidden_size=15_36  ,			d_ff=39_68  ,			num_heads=24  ,			num_layers=18  )
  __UpperCAmelCase    :						Dict       =						PixaStructConfig(
      vision_config=encoder_config.to_dict()  ,			text_config=decoder_config.to_dict()  ,			is_vqa=UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						PixaStructForConditionalGeneration(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						rename_and_convert_flax_params(UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''  )
  __UpperCAmelCase    :						Union[str, Any]       =						PixaStructImageProcessor()
  __UpperCAmelCase    :						Optional[int]       =						PixaStructProcessor(image_processor=UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  )

  if use_large:
    __UpperCAmelCase    :						str       =						40_96

  __UpperCAmelCase    :						str       =						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__":
  lowercase__				:List[str]				   =       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.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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