




'''simple docstring'''







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 			_a  (	__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					PerceiverTokenizer
       __a				:     str   				=					False
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase			       =		PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname				)
       @cached_property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	int				):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[str]=False   ,				lowercase						:	Union[str, Any]=20   ,				lowercase						:	Union[str, Any]=5				):



           '''simple docstring'''

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

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

           # Ensure consistency
           UpperCAmelCase			       =		tokenizer.decode(lowercase   ,				clean_up_tokenization_spaces=lowercase				)
           if " " not in output_txt and len(lowercase				) > 1:
               UpperCAmelCase			       =		(
                   tokenizer.decode([toks_ids[0]]   ,				clean_up_tokenization_spaces=lowercase				)
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]   ,				clean_up_tokenization_spaces=lowercase				)
               )
           if with_prefix_space:
               UpperCAmelCase			       =		''' ''' + output_txt
           UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
           return output_txt, output_ids
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		'''Unicode €.'''
           UpperCAmelCase			       =		tokenizer(lowercase				)
           UpperCAmelCase			       =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)

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

           UpperCAmelCase			       =		tokenizer('''e è é ê ë'''				)
           UpperCAmelCase			       =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)
           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[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     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           UpperCAmelCase			       =		[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
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           self.assertIsInstance(lowercase   ,				lowercase				)

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

           self.assertListEqual(lowercase   ,				lowercase				)

           self.assertEqual((2, 38)   ,				batch.input_ids.shape				)
           self.assertEqual((2, 38)   ,				batch.attention_mask.shape				)
       def     A      (	self						:	str				):



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           UpperCAmelCase			       =		tokenizer(
               text_target=lowercase   ,				max_length=32   ,				padding='''max_length'''   ,				truncation=lowercase   ,				return_tensors=lowercase				)
           self.assertEqual(32   ,				targets['''input_ids'''].shape[1]				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		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
           UpperCAmelCase			       =		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
                   UpperCAmelCase			       =		tempfile.mkdtemp()

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

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)

                   shutil.rmtree(lowercase				)

           UpperCAmelCase			       =		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
                   UpperCAmelCase			       =		tempfile.mkdtemp()

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

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

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase   ,				model_max_length=43				)
                   self.assertEqual(tokenizer.model_max_length   ,				43				)

                   shutil.rmtree(lowercase				)
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		[]
           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(lowercase				)

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

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

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

                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]
                   UpperCAmelCase			       =		added_tokens_extra_ids + [
                       '''an_additional_special_token'''
                   ]

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

                   # 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
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				)
                   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
                   UpperCAmelCase			       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,				lstrip=lowercase				)]
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				additional_special_tokens=lowercase   ,				)

                   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     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]				)   ,				'''�'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           pass
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers(fast=lowercase   ,				do_lower_case=lowercase				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   UpperCAmelCase			       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                   UpperCAmelCase			       =		tokenizer.convert_tokens_to_string(lowercase				)

                   self.assertIsInstance(lowercase   ,				lowercase				)










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
						)




