

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():
		lowerCamelCase      				=     '''pt'''
elif is_tf_available():
		lowerCamelCase      				=     '''tf'''
else:
		lowerCamelCase      				=     '''jax'''





class     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =PerceiverTokenizer
							__UpperCAmelCase       =False




							def        A__  ( self							):
												super().setUp()
												__lowerCAmelCase					=							PerceiverTokenizer()
												tokenizer.save_pretrained(self.tmpdirname							)




							@cached_property
							def        A__  ( self							):
												return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'							)




							def        A__  ( self					,						**_A							):
												return self.tokenizer_class.from_pretrained(self.tmpdirname					,						**_A							)




							def        A__  ( self					,						_A					,						_A=False					,						_A=2_0					,						_A=5							):
												# 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.

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

												__lowerCAmelCase					=							list(filter(lambda _A							: re.match(R'^[ a-zA-Z]+$'					,						t[1]							)					,						_A							)							)
												__lowerCAmelCase					=							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:
																	__lowerCAmelCase					=							toks[:max_length]
												if min_length is not None and len(_A							) < min_length and len(_A							) > 0:
																	while len(_A							) < 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(_A					,						clean_up_tokenization_spaces=_A							)
												if " " not in output_txt and len(_A							) > 1:
																	__lowerCAmelCase					=							(
																	    tokenizer.decode([toks_ids[0]]					,						clean_up_tokenization_spaces=_A							)
																	    + ' '
																	    + tokenizer.decode(toks_ids[1:]					,						clean_up_tokenization_spaces=_A							)
																	)
												if with_prefix_space:
																	__lowerCAmelCase					=							' ' + output_txt
												__lowerCAmelCase					=							tokenizer.encode(_A					,						add_special_tokens=_A							)
												return output_txt, output_ids




							def        A__  ( self							):
												__lowerCAmelCase					=							self.perceiver_tokenizer
												__lowerCAmelCase					=							'Unicode €.'
												__lowerCAmelCase					=							tokenizer(_A							)
												__lowerCAmelCase					=							[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
												self.assertEqual(encoded['input_ids']					,						_A							)

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

												__lowerCAmelCase					=							tokenizer('e è é ê ë'							)
												__lowerCAmelCase					=							[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
												self.assertEqual(encoded['input_ids']					,						_A							)
												# decoding
												__lowerCAmelCase					=							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        A__  ( self							):
												__lowerCAmelCase					=							self.perceiver_tokenizer
												__lowerCAmelCase					=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
												# fmt: off
												__lowerCAmelCase					=							[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
												# fmt: on
												__lowerCAmelCase					=							tokenizer(_A					,						padding=_A					,						return_tensors=_A							)
												self.assertIsInstance(_A					,						_A							)

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

												self.assertListEqual(_A					,						_A							)

												self.assertEqual((2, 3_8)					,						batch.input_ids.shape							)
												self.assertEqual((2, 3_8)					,						batch.attention_mask.shape							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.perceiver_tokenizer
												__lowerCAmelCase					=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
												__lowerCAmelCase					=							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        A__  ( self							):
												__lowerCAmelCase					=							self.perceiver_tokenizer
												__lowerCAmelCase					=							[
												    'Summary of the text.',
												    'Another summary.',
												]
												__lowerCAmelCase					=							tokenizer(
												    text_target=_A					,						max_length=3_2					,						padding='max_length'					,						truncation=_A					,						return_tensors=_A							)
												self.assertEqual(3_2					,						targets['input_ids'].shape[1]							)




							def        A__  ( self							):
												# safety check on max_len default value so we are sure the test works
												__lowerCAmelCase					=							self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""							):
																						self.assertNotEqual(tokenizer.model_max_length					,						4_2							)

        # 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(_A					,						add_special_tokens=_A							)
																						tokenizer.save_pretrained(_A							)

																						__lowerCAmelCase					=							tokenizer.__class__.from_pretrained(_A							)
																						__lowerCAmelCase					=							after_tokenizer.encode(_A					,						add_special_tokens=_A							)
																						self.assertListEqual(_A					,						_A							)

																						shutil.rmtree(_A							)

												__lowerCAmelCase					=							self.get_tokenizers(model_max_length=4_2							)
												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(_A					,						add_special_tokens=_A							)
																						tokenizer.save_pretrained(_A							)

																						__lowerCAmelCase					=							tokenizer.__class__.from_pretrained(_A							)
																						__lowerCAmelCase					=							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					,						4_2							)

																						__lowerCAmelCase					=							tokenizer.__class__.from_pretrained(_A					,						model_max_length=4_3							)
																						self.assertEqual(tokenizer.model_max_length					,						4_3							)

																						shutil.rmtree(_A							)




							def        A__  ( self							):
												__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(_A							)

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

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

																						__lowerCAmelCase					=							[F"""<extra_id_{i}>""" for i in range(1_2_5							)]

																						__lowerCAmelCase					=							added_tokens_extra_ids + [
																						    'an_additional_special_token'
																						]
																						__lowerCAmelCase					=							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
																						__lowerCAmelCase					=							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
																						__lowerCAmelCase					=							added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'					,						lstrip=_A							)]
																						__lowerCAmelCase					=							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        A__  ( self							):
												__lowerCAmelCase					=							self.perceiver_tokenizer
												self.assertEqual(tokenizer.decode([1_7_8]							)					,						'�'							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
												# strings and special added tokens as tokens
												__lowerCAmelCase					=							self.get_tokenizers(fast=_A					,						do_lower_case=_A							)
												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(_A							)

																						self.assertIsInstance(_A					,						_A							)









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase      				=     data_utils.TransfoXLTokenizer
lowerCamelCase      				=     data_utils.TransfoXLCorpus
lowerCamelCase      				=     data_utils
lowerCamelCase      				=     data_utils


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if transfo_xl_dataset_file:
										# Convert a pre-processed corpus (see original TensorFlow repo)
										with open(UpperCAmelCase__		,					'rb'      ) as fp:
															__lowerCAmelCase					=							pickle.load(UpperCAmelCase__		,					encoding='latin1'      )
										# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
										__lowerCAmelCase					=							pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
										print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
										__lowerCAmelCase					=							corpus.vocab.__dict__
										torch.save(UpperCAmelCase__		,					UpperCAmelCase__      )

										__lowerCAmelCase					=							corpus.__dict__
										corpus_dict_no_vocab.pop('vocab'		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							pytorch_dump_folder_path + '/' + CORPUS_NAME
										print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
										torch.save(UpperCAmelCase__		,					UpperCAmelCase__      )

					if tf_checkpoint_path:
										# Convert a pre-trained TensorFlow model
										__lowerCAmelCase					=							os.path.abspath(UpperCAmelCase__      )
										__lowerCAmelCase					=							os.path.abspath(UpperCAmelCase__      )

										print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
										# Initialise PyTorch model
										if transfo_xl_config_file == "":
															__lowerCAmelCase					=							TransfoXLConfig()
										else:
															__lowerCAmelCase					=							TransfoXLConfig.from_json_file(UpperCAmelCase__      )
										print(F"""Building PyTorch model from configuration: {config}"""      )
										__lowerCAmelCase					=							TransfoXLLMHeadModel(UpperCAmelCase__      )

										__lowerCAmelCase					=							load_tf_weights_in_transfo_xl(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )
										# Save pytorch-model
										__lowerCAmelCase					=							os.path.join(UpperCAmelCase__		,					UpperCAmelCase__      )
										__lowerCAmelCase					=							os.path.join(UpperCAmelCase__		,					UpperCAmelCase__      )
										print(F"""Save PyTorch model to {os.path.abspath(UpperCAmelCase__      )}"""      )
										torch.save(model.state_dict()		,					UpperCAmelCase__      )
										print(F"""Save configuration file to {os.path.abspath(UpperCAmelCase__      )}"""      )
										with open(UpperCAmelCase__		,					'w'		,					encoding='utf-8'      ) as f:
															f.write(config.to_json_string()      )


if __name__ == "__main__":
		lowerCamelCase      				=     argparse.ArgumentParser()
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
		)
		parser.add_argument(
		    '''--tf_checkpoint_path''',
		    default='''''',
		    type=str,
		    help='''An optional path to a TensorFlow checkpoint path to be converted.''',
		)
		parser.add_argument(
		    '''--transfo_xl_config_file''',
		    default='''''',
		    type=str,
		    help=(
		        '''An optional config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--transfo_xl_dataset_file''',
		    default='''''',
		    type=str,
		    help='''An optional dataset file to be converted in a vocabulary.''',
		)
		lowerCamelCase      				=     parser.parse_args()
		convert_transfo_xl_checkpoint_to_pytorch(
		    args.tf_checkpoint_path,
		    args.transfo_xl_config_file,
		    args.pytorch_dump_folder_path,
		    args.transfo_xl_dataset_file,
		)






