






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

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

from ...test_tokenization_common import TokenizerTesterMixin


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



class       __UpperCAmelCase		(_UpperCamelCase       ,unittest.TestCase    ):
			__snake_case				:      Optional[int] 						=							ByTaTokenizer
			__snake_case				:      Tuple 						=							False







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									super().setUp()
									_SCREAMING_SNAKE_CASE	      =  ByTaTokenizer()
									tokenizer.save_pretrained(self.tmpdirname     )







			@cached_property
			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									return ByTaTokenizer.from_pretrained("""google/byt5-small"""     )







			def 							UpperCamelCase      (	self:  Any      ,	**UpperCAmelCase_:  List[Any]     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Optional[int]      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  Optional[Any]=False      ,	UpperCAmelCase_:  Dict=20      ,	UpperCAmelCase_:  List[str]=5     ):







									'''simple docstring'''




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

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

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







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.ta_base_tokenizer
									_SCREAMING_SNAKE_CASE	      =  tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]     )
									_SCREAMING_SNAKE_CASE	      =  tokenizer(["""hi""", """I went to the gym""", """"""]     )
									self.assertListEqual(batch_with_eos_added["""input_ids"""]      ,	batch_without_eos_added["""input_ids"""]     )







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.ta_base_tokenizer
									_SCREAMING_SNAKE_CASE	      =  """Unicode €."""
									_SCREAMING_SNAKE_CASE	      =  tokenizer(lowerCAmelCase_     )
									_SCREAMING_SNAKE_CASE	      =  [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
									self.assertEqual(encoded["""input_ids"""]      ,	lowerCAmelCase_     )

									# decoding
									_SCREAMING_SNAKE_CASE	      =  tokenizer.decode(lowerCAmelCase_     )
									self.assertEqual(lowerCAmelCase_      ,	"""Unicode €.</s>"""     )

									_SCREAMING_SNAKE_CASE	      =  tokenizer("""e è é ê ë"""     )
									_SCREAMING_SNAKE_CASE	      =  [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
									self.assertEqual(encoded["""input_ids"""]      ,	lowerCAmelCase_     )
									# decoding
									_SCREAMING_SNAKE_CASE	      =  tokenizer.decode(lowerCAmelCase_     )
									self.assertEqual(lowerCAmelCase_      ,	"""e è é ê ë</s>"""     )

									# encode/decode, but with `encode` instead of `__call__`
									self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""     )     )      ,	"""e è é ê ë</s>"""     )







			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.ta_base_tokenizer
									_SCREAMING_SNAKE_CASE	      =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									# fmt: off
									_SCREAMING_SNAKE_CASE	      =  [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
									# fmt: on
									_SCREAMING_SNAKE_CASE	      =  tokenizer(lowerCAmelCase_      ,	padding=lowerCAmelCase_      ,	return_tensors=lowerCAmelCase_     )
									self.assertIsInstance(lowerCAmelCase_      ,	lowerCAmelCase_     )

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

									self.assertListEqual(lowerCAmelCase_      ,	lowerCAmelCase_     )

									self.assertEqual((2, 37)      ,	batch.input_ids.shape     )
									self.assertEqual((2, 37)      ,	batch.attention_mask.shape     )







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




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







			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.ta_base_tokenizer
									_SCREAMING_SNAKE_CASE	      =  ["""A long paragraph for summarization. </s>"""]
									_SCREAMING_SNAKE_CASE	      =  ["""Summary of the text. </s>"""]
									# fmt: off
									_SCREAMING_SNAKE_CASE	      =  [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
									_SCREAMING_SNAKE_CASE	      =  [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
									# fmt: on

									_SCREAMING_SNAKE_CASE	      =  tokenizer(lowerCAmelCase_      ,	text_target=lowerCAmelCase_     )

									self.assertEqual(lowerCAmelCase_      ,	batch["""input_ids"""][0]     )
									self.assertEqual(lowerCAmelCase_      ,	batch["""labels"""][0]     )







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									_SCREAMING_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
									_SCREAMING_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
																					_SCREAMING_SNAKE_CASE	      =  tempfile.mkdtemp()

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

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

																					shutil.rmtree(lowerCAmelCase_     )

									_SCREAMING_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
																					_SCREAMING_SNAKE_CASE	      =  tempfile.mkdtemp()

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

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

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

																					shutil.rmtree(lowerCAmelCase_     )







			def 							UpperCamelCase      (	self:  Optional[int]     ):







									'''simple docstring'''




									_SCREAMING_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(lowerCAmelCase_     )

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

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

																					_SCREAMING_SNAKE_CASE	      =  [F'<extra_id_{i}>' for i in range(125     )]

																					_SCREAMING_SNAKE_CASE	      =  added_tokens_extra_ids + [
																					    """an_additional_special_token"""
																					]
																					_SCREAMING_SNAKE_CASE	      =  added_tokens_extra_ids + [
																					    """an_additional_special_token"""
																					]

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

																					# 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
																					_SCREAMING_SNAKE_CASE	      =  tokenizer_class.from_pretrained(
																					    lowerCAmelCase_      ,	)
																					self.assertIn(
																					    """an_additional_special_token"""      ,	tokenizer_without_change_in_init.additional_special_tokens     )
																					# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																					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
																					_SCREAMING_SNAKE_CASE	      =  added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""      ,	lstrip=lowerCAmelCase_     )]
																					_SCREAMING_SNAKE_CASE	      =  tokenizer_class.from_pretrained(
																					    lowerCAmelCase_      ,	additional_special_tokens=lowerCAmelCase_      ,	)

																					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:  List[str]     ):







									'''simple docstring'''




									_SCREAMING_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(lowerCAmelCase_     )

																					_SCREAMING_SNAKE_CASE	      =  tokenizer_class.from_pretrained(lowerCAmelCase_     )

																					self.assertTrue(tokenizer.decode([255]     ) == """"""     )







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									pass







			def 							UpperCamelCase      (	self:  Tuple     ):







									'''simple docstring'''




									pass







			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									pass







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									pass







			def 							UpperCamelCase      (	self:  List[str]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_tokenizers(fast=lowerCAmelCase_      ,	do_lower_case=lowerCAmelCase_     )
									for tokenizer in tokenizers:
															with self.subTest(F'{tokenizer.__class__.__name__}'     ):
																					_SCREAMING_SNAKE_CASE	      =  ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
																					_SCREAMING_SNAKE_CASE	      =  tokenizer.convert_tokens_to_string(lowerCAmelCase_     )

																					self.assertIsInstance(lowerCAmelCase_      ,	lowerCAmelCase_     )







			def 							UpperCamelCase      (	self:  str     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(F'{tokenizer.__class__.__name__}'     ):
																					_SCREAMING_SNAKE_CASE	      =  [
																					    """bos_token""",
																					    """eos_token""",
																					    """unk_token""",
																					    """sep_token""",
																					    """pad_token""",
																					    """cls_token""",
																					    """mask_token""",
																					]

																					_SCREAMING_SNAKE_CASE	      =  0
																					_SCREAMING_SNAKE_CASE	      =  tokenizer.convert_ids_to_tokens(
																					    lowerCAmelCase_      ,	skip_special_tokens=lowerCAmelCase_     )

																					for attr in attributes_list:
																											setattr(lowerCAmelCase_      ,	attr + """_id"""      ,	lowerCAmelCase_     )
																											self.assertEqual(getattr(lowerCAmelCase_      ,	lowerCAmelCase_     )      ,	lowerCAmelCase_     )
																											self.assertEqual(getattr(lowerCAmelCase_      ,	attr + """_id"""     )      ,	lowerCAmelCase_     )

																											setattr(lowerCAmelCase_      ,	attr + """_id"""      ,	lowerCAmelCase_     )
																											self.assertEqual(getattr(lowerCAmelCase_      ,	lowerCAmelCase_     )      ,	lowerCAmelCase_     )
																											self.assertEqual(getattr(lowerCAmelCase_      ,	attr + """_id"""     )      ,	lowerCAmelCase_     )

																					setattr(lowerCAmelCase_      ,	"""additional_special_tokens_ids"""      ,	[]     )
																					self.assertListEqual(getattr(lowerCAmelCase_      ,	"""additional_special_tokens"""     )      ,	[]     )
																					self.assertListEqual(getattr(lowerCAmelCase_      ,	"""additional_special_tokens_ids"""     )      ,	[]     )

																					setattr(lowerCAmelCase_      ,	"""additional_special_tokens_ids"""      ,	[token_id_to_test_setters]     )
																					self.assertListEqual(getattr(lowerCAmelCase_      ,	"""additional_special_tokens"""     )      ,	[token_to_test_setters]     )
																					self.assertListEqual(getattr(lowerCAmelCase_      ,	"""additional_special_tokens_ids"""     )      ,	[token_id_to_test_setters]     )








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

