

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
 __A      		=     '''pt'''
elif is_tf_available():
 __A      		=     '''tf'''
else:
 __A      		=     '''jax'''



class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Any										=    PerceiverTokenizer
      UpperCamelCase_      :		str										=    False



      def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
        super().setUp()
        _snake_case						=							PerceiverTokenizer()
        tokenizer.save_pretrained(self.tmpdirname		)



      @cached_property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[Any]:
        return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , **A__				:    Union[str, Any]		)   ->					PerceiverTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Union[str, Any]      , A__				:    Any=False      , A__				:    int=20      , A__				:    Tuple=5		)   ->					Tuple[str, list]:
        # XXX The default common tokenizer tests assume that every ID is decodable on its own.
        # This assumption is invalid for Perceiver because single bytes might not be
        # valid utf-8 (byte 128 for instance).
        # Here we're overriding the smallest possible method to provide
        # a clean sequence without making the same assumption.

        _snake_case						=							[]
        for i in range(len(A__		)		):
          try:
            _snake_case						=							tokenizer.decode([i]      , clean_up_tokenization_spaces=A__		)
          except UnicodeDecodeError:
            pass
          toks.append((i, tok)		)

        _snake_case						=							list(filter(lambda A__		: re.match(R'''^[ a-zA-Z]+$'''      , t[1]		)      , A__		)		)
        _snake_case						=							list(filter(lambda A__		: [t[0]] == tokenizer.encode(t[1]      , add_special_tokens=A__		)      , A__		)		)
        if max_length is not None and len(A__		) > max_length:
          _snake_case						=							toks[:max_length]
        if min_length is not None and len(A__		) < min_length and len(A__		) > 0:
          while len(A__		) < min_length:
            _snake_case						=							toks + toks
        # toks_str = [t[1] for t in toks]
        _snake_case						=							[t[0] for t in toks]

        # Ensure consistency
        _snake_case						=							tokenizer.decode(A__      , clean_up_tokenization_spaces=A__		)
        if " " not in output_txt and len(A__		) > 1:
          _snake_case						=							(
              tokenizer.decode([toks_ids[0]]      , clean_up_tokenization_spaces=A__		)
              + ''' '''
              + tokenizer.decode(toks_ids[1:]      , clean_up_tokenization_spaces=A__		)
          )
        if with_prefix_space:
          _snake_case						=							''' ''' + output_txt
        _snake_case						=							tokenizer.encode(A__      , add_special_tokens=A__		)
        return output_txt, output_ids



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					str:
        _snake_case						=							self.perceiver_tokenizer
        _snake_case						=							'''Unicode €.'''
        _snake_case						=							tokenizer(A__		)
        _snake_case						=							[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
        self.assertEqual(encoded['''input_ids''']      , A__		)

        # decoding
        _snake_case						=							tokenizer.decode(A__		)
        self.assertEqual(A__      , '''[CLS]Unicode €.[SEP]'''		)

        _snake_case						=							tokenizer('''e è é ê ë'''		)
        _snake_case						=							[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
        self.assertEqual(encoded['''input_ids''']      , A__		)
        # decoding
        _snake_case						=							tokenizer.decode(A__		)
        self.assertEqual(A__      , '''[CLS]e è é ê ë[SEP]'''		)

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''		)		)      , '''[CLS]e è é ê ë[SEP]'''		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					int:
        _snake_case						=							self.perceiver_tokenizer
        _snake_case						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        # fmt: off
        _snake_case						=							[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
        # fmt: on
        _snake_case						=							tokenizer(A__      , padding=A__      , return_tensors=A__		)
        self.assertIsInstance(A__      , A__		)

        if FRAMEWORK != "jax":
          _snake_case						=							list(batch.input_ids.numpy()[0]		)
        else:
          _snake_case						=							list(batch.input_ids.tolist()[0]		)

        self.assertListEqual(A__      , A__		)

        self.assertEqual((2, 38)      , batch.input_ids.shape		)
        self.assertEqual((2, 38)      , batch.attention_mask.shape		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _snake_case						=							self.perceiver_tokenizer
        _snake_case						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        _snake_case						=							tokenizer(A__      , padding=A__      , return_tensors=A__		)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn('''input_ids'''      , A__		)
        self.assertIn('''attention_mask'''      , A__		)
        self.assertNotIn('''decoder_input_ids'''      , A__		)
        self.assertNotIn('''decoder_attention_mask'''      , A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        _snake_case						=							self.perceiver_tokenizer
        _snake_case						=							[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        _snake_case						=							tokenizer(
            text_target=A__      , max_length=32      , padding='''max_length'''      , truncation=A__      , return_tensors=A__		)
        self.assertEqual(32      , targets['''input_ids'''].shape[1]		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        # safety check on max_len default value so we are sure the test works
        _snake_case						=							self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            self.assertNotEqual(tokenizer.model_max_length      , 42		)

        # Now let's start the test
        _snake_case						=							self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            # Isolate this from the other tests because we save additional tokens/etc
            _snake_case						=							tempfile.mkdtemp()

            _snake_case						=							''' He is very happy, UNwant\u00E9d,running'''
            _snake_case						=							tokenizer.encode(A__      , add_special_tokens=A__		)
            tokenizer.save_pretrained(A__		)

            _snake_case						=							tokenizer.__class__.from_pretrained(A__		)
            _snake_case						=							after_tokenizer.encode(A__      , add_special_tokens=A__		)
            self.assertListEqual(A__      , A__		)

            shutil.rmtree(A__		)

        _snake_case						=							self.get_tokenizers(model_max_length=42		)
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            # Isolate this from the other tests because we save additional tokens/etc
            _snake_case						=							tempfile.mkdtemp()

            _snake_case						=							''' He is very happy, UNwant\u00E9d,running'''
            tokenizer.add_tokens(['''bim''', '''bambam''']		)
            _snake_case						=							tokenizer.additional_special_tokens
            additional_special_tokens.append('''new_additional_special_token'''		)
            tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}		)
            _snake_case						=							tokenizer.encode(A__      , add_special_tokens=A__		)
            tokenizer.save_pretrained(A__		)

            _snake_case						=							tokenizer.__class__.from_pretrained(A__		)
            _snake_case						=							after_tokenizer.encode(A__      , add_special_tokens=A__		)
            self.assertListEqual(A__      , A__		)
            self.assertIn('''new_additional_special_token'''      , after_tokenizer.additional_special_tokens		)
            self.assertEqual(after_tokenizer.model_max_length      , 42		)

            _snake_case						=							tokenizer.__class__.from_pretrained(A__      , model_max_length=43		)
            self.assertEqual(tokenizer.model_max_length      , 43		)

            shutil.rmtree(A__		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Tuple:
        _snake_case						=							[]
        if self.test_slow_tokenizer:
          tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

        if self.test_rust_tokenizer:
          tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
          with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer_utils.save_pretrained(A__		)

            with open(os.path.join(A__      , '''special_tokens_map.json'''		)      , encoding='''utf-8'''		) as json_file:
              _snake_case						=							json.load(A__		)

            with open(os.path.join(A__      , '''tokenizer_config.json'''		)      , encoding='''utf-8'''		) as json_file:
              _snake_case						=							json.load(A__		)

            _snake_case						=							[f"""<extra_id_{i}>""" for i in range(125		)]

            _snake_case						=							added_tokens_extra_ids + [
                '''an_additional_special_token'''
            ]
            _snake_case						=							added_tokens_extra_ids + [
                '''an_additional_special_token'''
            ]

            with open(os.path.join(A__      , '''special_tokens_map.json'''		)      , '''w'''      , encoding='''utf-8'''		) as outfile:
              json.dump(A__      , A__		)
            with open(os.path.join(A__      , '''tokenizer_config.json'''		)      , '''w'''      , encoding='''utf-8'''		) as outfile:
              json.dump(A__      , A__		)

            # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
            # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
            # "special_tokens_map.json" files
            _snake_case						=							tokenizer_class.from_pretrained(
                A__      , )
            self.assertIn(
                '''an_additional_special_token'''      , tokenizer_without_change_in_init.additional_special_tokens		)
            self.assertEqual(
                ['''an_additional_special_token''']      , tokenizer_without_change_in_init.convert_ids_to_tokens(
                    tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']		)		)      , )

            # Now we test that we can change the value of additional_special_tokens in the from_pretrained
            _snake_case						=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      , lstrip=A__		)]
            _snake_case						=							tokenizer_class.from_pretrained(
                A__      , additional_special_tokens=A__      , )

            self.assertIn('''a_new_additional_special_token'''      , tokenizer.additional_special_tokens		)
            self.assertEqual(
                ['''a_new_additional_special_token''']      , tokenizer.convert_ids_to_tokens(
                    tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']		)		)      , )



      def 						UpperCamelCase_		(      self				:    int		)   ->					Tuple:
        _snake_case						=							self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([178]		)      , '''�'''		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Union[str, Any]:
        pass



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        pass



      def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
        pass



      def 						UpperCamelCase_		(      self				:    Any		)   ->					str:
        pass







      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[int]:
        # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
        # strings and special added tokens as tokens
        _snake_case						=							self.get_tokenizers(fast=A__      , do_lower_case=A__		)
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            _snake_case						=							['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
            _snake_case						=							tokenizer.convert_tokens_to_string(A__		)

            self.assertIsInstance(A__      , A__		)




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
 )

