




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		(  __UpperCAmelCase      ,				unittest.TestCase     ):
		__snake_case									=   PerceiverTokenizer
		__snake_case									=   False



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								super().setUp()
								lowerCAmelCase_      			=  PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname   )



		@cached_property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''   )



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								return self.tokenizer_class.from_pretrained(self.tmpdirname,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=False,	UpperCamelCase__=20,	UpperCamelCase__=5   ):







								"""simple docstring"""



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

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

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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

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







								"""simple docstring"""



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

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

								self.assertListEqual(UpperCamelCase__,	UpperCamelCase__   )

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.perceiver_tokenizer
								lowerCAmelCase_      			=  [
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								lowerCAmelCase_      			=  tokenizer(
								    text_target=UpperCamelCase__,	max_length=32,	padding='''max_length''',	truncation=UpperCamelCase__,	return_tensors=UpperCamelCase__   )
								self.assertEqual(32,	targets['''input_ids'''].shape[1]   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  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
																				lowerCAmelCase_      			=  tempfile.mkdtemp()

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

																				lowerCAmelCase_      			=  tokenizer.__class__.from_pretrained(UpperCamelCase__   )
																				lowerCAmelCase_      			=  after_tokenizer.encode(UpperCamelCase__,	add_special_tokens=UpperCamelCase__   )
																				self.assertListEqual(UpperCamelCase__,	UpperCamelCase__   )

																				shutil.rmtree(UpperCamelCase__   )

								lowerCAmelCase_      			=  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
																				lowerCAmelCase_      			=  tempfile.mkdtemp()

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

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

																				lowerCAmelCase_      			=  tokenizer.__class__.from_pretrained(UpperCamelCase__,	model_max_length=43   )
																				self.assertEqual(tokenizer.model_max_length,	43   )

																				shutil.rmtree(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  []
								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(UpperCamelCase__   )

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

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

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

																				lowerCAmelCase_      			=  added_tokens_extra_ids + [
																				    '''an_additional_special_token'''
																				]
																				lowerCAmelCase_      			=  added_tokens_extra_ids + [
																				    '''an_additional_special_token'''
																				]

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

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

																				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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]   ),	'''�'''   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

																				self.assertIsInstance(UpperCamelCase__,	UpperCamelCase__   )










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
						)




