import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						lowercase    (     __UpperCAmelCase				,       unittest.TestCase     ):
							lowercase__						:       List[Any]								=							GPTaTokenizer
							lowercase__						:       int								=							GPTaTokenizerFast
							lowercase__						:       str								=							True
							lowercase__						:       Tuple								=							{"""add_prefix_space""": True}
							lowercase__						:       Tuple								=							False




							def 	__snake_case(						self   :					Optional[int]      )					->				List[Any]:



												'''simple docstring'''

												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												SCREAMING_SNAKE_CASE    			=       [
												    "l",
												    "o",
												    "w",
												    "e",
												    "r",
												    "s",
												    "t",
												    "i",
												    "d",
												    "n",
												    "\u0120",
												    "\u0120l",
												    "\u0120n",
												    "\u0120lo",
												    "\u0120low",
												    "er",
												    "\u0120lowest",
												    "\u0120newer",
												    "\u0120wider",
												    "<unk>",
												    "<|endoftext|>",
												]
												SCREAMING_SNAKE_CASE    			=       dict(zip(lowerCAmelCase_	,							range(len(lowerCAmelCase_      )      )      )      )
												SCREAMING_SNAKE_CASE    			=       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
												SCREAMING_SNAKE_CASE    			=       {"unk_token": "<unk>"}

												SCREAMING_SNAKE_CASE    			=       os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["vocab_file"]      )
												SCREAMING_SNAKE_CASE    			=       os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["merges_file"]      )
												with open(self.vocab_file	,							"w"	,							encoding="utf-8"      ) as fp:
																	fp.write(json.dumps(lowerCAmelCase_      ) + "\n"      )
												with open(self.merges_file	,							"w"	,							encoding="utf-8"      ) as fp:
																	fp.write("\n".join(lowerCAmelCase_      )      )




							def 	__snake_case(						self   :					Union[str, Any]	,							**_UpperCamelCase   :					Any      )					->				Dict:



												'''simple docstring'''

												kwargs.update(self.special_tokens_map      )
												return GPTaTokenizer.from_pretrained(self.tmpdirname	,							**lowerCAmelCase_      )




							def 	__snake_case(						self   :					str	,							**_UpperCamelCase   :					List[str]      )					->				Dict:



												'''simple docstring'''

												kwargs.update(self.special_tokens_map      )
												return GPTaTokenizerFast.from_pretrained(self.tmpdirname	,							**lowerCAmelCase_      )




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Tuple      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "lower newer"
												SCREAMING_SNAKE_CASE    			=       "lower newer"
												return input_text, output_text




							def 	__snake_case(						self   :					List[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       GPTaTokenizer(self.vocab_file	,							self.merges_file	,							**self.special_tokens_map      )
												SCREAMING_SNAKE_CASE    			=       "lower newer"
												SCREAMING_SNAKE_CASE    			=       ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
												SCREAMING_SNAKE_CASE    			=       tokenizer.tokenize(lowerCAmelCase_	,							add_prefix_space=lowerCAmelCase_      )
												self.assertListEqual(lowerCAmelCase_	,							lowerCAmelCase_      )

												SCREAMING_SNAKE_CASE    			=       tokens + [tokenizer.unk_token]
												SCREAMING_SNAKE_CASE    			=       [14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )	,							lowerCAmelCase_      )




							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												if not self.test_rust_tokenizer:
																	return

												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_      )

												SCREAMING_SNAKE_CASE    			=       "lower newer"

												# Testing tokenization
												SCREAMING_SNAKE_CASE    			=       tokenizer.tokenize(lowerCAmelCase_	,							add_prefix_space=lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       rust_tokenizer.tokenize(lowerCAmelCase_      )
												self.assertListEqual(lowerCAmelCase_	,							lowerCAmelCase_      )

												# Testing conversion to ids without special tokens
												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(lowerCAmelCase_	,							add_special_tokens=lowerCAmelCase_	,							add_prefix_space=lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       rust_tokenizer.encode(lowerCAmelCase_	,							add_special_tokens=lowerCAmelCase_      )
												self.assertListEqual(lowerCAmelCase_	,							lowerCAmelCase_      )

												# Testing conversion to ids with special tokens
												SCREAMING_SNAKE_CASE    			=       self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(lowerCAmelCase_	,							add_prefix_space=lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       rust_tokenizer.encode(lowerCAmelCase_      )
												self.assertListEqual(lowerCAmelCase_	,							lowerCAmelCase_      )

												# Testing the unknown token
												SCREAMING_SNAKE_CASE    			=       tokens + [rust_tokenizer.unk_token]
												SCREAMING_SNAKE_CASE    			=       [14, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )	,							lowerCAmelCase_      )




							def 	__snake_case(						self   :					List[str]	,							*_UpperCamelCase   :					Tuple	,							**_UpperCamelCase   :					Any      )					->				Optional[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Dict=15      )					->				List[str]:



												'''simple docstring'''

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
																						SCREAMING_SNAKE_CASE    			=       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_	,							**lowerCAmelCase_      )

																						# Simple input
																						SCREAMING_SNAKE_CASE    			=       "This is a simple input"
																						SCREAMING_SNAKE_CASE    			=       ["This is a simple input 1", "This is a simple input 2"]
																						SCREAMING_SNAKE_CASE    			=       ("This is a simple input", "This is a pair")
																						SCREAMING_SNAKE_CASE    			=       [
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(lowerCAmelCase_	,							tokenizer_r.encode	,							lowerCAmelCase_	,							max_length=lowerCAmelCase_	,							padding="max_length"      )

																						# Simple input
																						self.assertRaises(lowerCAmelCase_	,							tokenizer_r.encode_plus	,							lowerCAmelCase_	,							max_length=lowerCAmelCase_	,							padding="max_length"      )

																						# Simple input
																						self.assertRaises(
																						    lowerCAmelCase_	,							tokenizer_r.batch_encode_plus	,							lowerCAmelCase_	,							max_length=lowerCAmelCase_	,							padding="max_length"	,							)

																						# Pair input
																						self.assertRaises(lowerCAmelCase_	,							tokenizer_r.encode	,							lowerCAmelCase_	,							max_length=lowerCAmelCase_	,							padding="max_length"      )

																						# Pair input
																						self.assertRaises(lowerCAmelCase_	,							tokenizer_r.encode_plus	,							lowerCAmelCase_	,							max_length=lowerCAmelCase_	,							padding="max_length"      )

																						# Pair input
																						self.assertRaises(
																						    lowerCAmelCase_	,							tokenizer_r.batch_encode_plus	,							lowerCAmelCase_	,							max_length=lowerCAmelCase_	,							padding="max_length"	,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       GPTaTokenizer.from_pretrained(self.tmpdirname	,							pad_token="<pad>"      )

												# Simple input
												SCREAMING_SNAKE_CASE    			=       "This is a simple input"
												SCREAMING_SNAKE_CASE    			=       ["This is a simple input looooooooong", "This is a simple input"]
												SCREAMING_SNAKE_CASE    			=       ("This is a simple input", "This is a pair")
												SCREAMING_SNAKE_CASE    			=       [
												    ("This is a simple input loooooong", "This is a simple input"),
												    ("This is a simple pair loooooong", "This is a simple pair"),
												]

												SCREAMING_SNAKE_CASE    			=       tokenizer.pad_token_id

												SCREAMING_SNAKE_CASE    			=       tokenizer(lowerCAmelCase_	,							padding="max_length"	,							max_length=30	,							return_tensors="np"      )
												SCREAMING_SNAKE_CASE    			=       tokenizer(lowerCAmelCase_	,							padding=lowerCAmelCase_	,							truncate=lowerCAmelCase_	,							return_tensors="np"      )
												SCREAMING_SNAKE_CASE    			=       tokenizer(*lowerCAmelCase_	,							padding="max_length"	,							max_length=60	,							return_tensors="np"      )
												SCREAMING_SNAKE_CASE    			=       tokenizer(lowerCAmelCase_	,							padding=lowerCAmelCase_	,							truncate=lowerCAmelCase_	,							return_tensors="np"      )

												# s
												# test single string max_length padding
												self.assertEqual(out_s["input_ids"].shape[-1]	,							30      )
												self.assertTrue(pad_token_id in out_s["input_ids"]      )
												self.assertTrue(0 in out_s["attention_mask"]      )

												# s2
												# test automatic padding
												self.assertEqual(out_sa["input_ids"].shape[-1]	,							33      )
												# long slice doesn't have padding
												self.assertFalse(pad_token_id in out_sa["input_ids"][0]      )
												self.assertFalse(0 in out_sa["attention_mask"][0]      )
												# short slice does have padding
												self.assertTrue(pad_token_id in out_sa["input_ids"][1]      )
												self.assertTrue(0 in out_sa["attention_mask"][1]      )

												# p
												# test single pair max_length padding
												self.assertEqual(out_p["input_ids"].shape[-1]	,							60      )
												self.assertTrue(pad_token_id in out_p["input_ids"]      )
												self.assertTrue(0 in out_p["attention_mask"]      )

												# p2
												# test automatic padding pair
												self.assertEqual(out_pa["input_ids"].shape[-1]	,							52      )
												# long slice pair doesn't have padding
												self.assertFalse(pad_token_id in out_pa["input_ids"][0]      )
												self.assertFalse(0 in out_pa["attention_mask"][0]      )
												# short slice pair does have padding
												self.assertTrue(pad_token_id in out_pa["input_ids"][1]      )
												self.assertTrue(0 in out_pa["attention_mask"][1]      )




							def 	__snake_case(						self   :					Tuple      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "$$$"
												SCREAMING_SNAKE_CASE    			=       GPTaTokenizer.from_pretrained(self.tmpdirname	,							bos_token=lowerCAmelCase_	,							add_bos_token=lowerCAmelCase_      )

												SCREAMING_SNAKE_CASE    			=       "This is a simple input"
												SCREAMING_SNAKE_CASE    			=       ["This is a simple input 1", "This is a simple input 2"]

												SCREAMING_SNAKE_CASE    			=       tokenizer.bos_token_id

												SCREAMING_SNAKE_CASE    			=       tokenizer(lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       tokenizer(lowerCAmelCase_      )

												self.assertEqual(out_s.input_ids[0]	,							lowerCAmelCase_      )
												self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

												SCREAMING_SNAKE_CASE    			=       tokenizer.decode(out_s.input_ids      )
												SCREAMING_SNAKE_CASE    			=       tokenizer.batch_decode(out_sa.input_ids      )

												self.assertEqual(decode_s.split()[0]	,							lowerCAmelCase_      )
												self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )




							def 	__snake_case(						self   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												pass





							def 	__snake_case(						self   :					Union[str, Any]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       [self.get_tokenizer(do_lower_case=lowerCAmelCase_	,							add_bos_token=lowerCAmelCase_      )]
												for tokenizer in tokenizers:
																	with self.subTest(F"{tokenizer.__class__.__name__}"      ):
																						SCREAMING_SNAKE_CASE    			=       "Encode this."
																						SCREAMING_SNAKE_CASE    			=       "This one too please."
																						SCREAMING_SNAKE_CASE    			=       tokenizer.encode(lowerCAmelCase_	,							add_special_tokens=lowerCAmelCase_      )
																						encoded_sequence += tokenizer.encode(lowerCAmelCase_	,							add_special_tokens=lowerCAmelCase_      )
																						SCREAMING_SNAKE_CASE    			=       tokenizer.encode_plus(
																						    lowerCAmelCase_	,							lowerCAmelCase_	,							add_special_tokens=lowerCAmelCase_	,							return_special_tokens_mask=lowerCAmelCase_	,							)
																						SCREAMING_SNAKE_CASE    			=       encoded_sequence_dict["input_ids"]
																						SCREAMING_SNAKE_CASE    			=       encoded_sequence_dict["special_tokens_mask"]
																						self.assertEqual(len(lowerCAmelCase_      )	,							len(lowerCAmelCase_      )      )

																						SCREAMING_SNAKE_CASE    			=       [
																						    (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase_      )
																						]
																						SCREAMING_SNAKE_CASE    			=       [x for x in filtered_sequence if x is not None]
																						self.assertEqual(lowerCAmelCase_	,							lowerCAmelCase_      )



@require_tokenizers
class 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Optional[int]      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("facebook/opt-350m"	,							from_slow=lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       "A photo of a cat"

												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(
												    lowerCAmelCase_	,							)
												self.assertEqual(lowerCAmelCase_	,							[2, 250, 1_345, 9, 10, 4_758]      )
												tokenizer.save_pretrained("test_opt"      )

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("./test_opt"      )
												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(
												    lowerCAmelCase_	,							)
												self.assertEqual(lowerCAmelCase_	,							[2, 250, 1_345, 9, 10, 4_758]      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("facebook/opt-350m"	,							use_slow=lowerCAmelCase_      )
												SCREAMING_SNAKE_CASE    			=       "A photo of a cat"

												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(
												    lowerCAmelCase_	,							)
												# Same as above
												self.assertEqual(lowerCAmelCase_	,							[2, 250, 1_345, 9, 10, 4_758]      )





							@unittest.skip("This test is failing because of a bug in the fast tokenizer"      )
							def 	__snake_case(						self   :					List[str]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("facebook/opt-350m"	,							from_slow=lowerCAmelCase_      )

												SCREAMING_SNAKE_CASE    			=       "bos"
												SCREAMING_SNAKE_CASE    			=       tokenizer.get_vocab()["bos"]

												SCREAMING_SNAKE_CASE    			=       "A photo of a cat"
												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(
												    lowerCAmelCase_	,							)
												# We changed the bos token
												self.assertEqual(lowerCAmelCase_	,							[31_957, 250, 1_345, 9, 10, 4_758]      )
												tokenizer.save_pretrained("./tok"      )
												SCREAMING_SNAKE_CASE    			=       AutoTokenizer.from_pretrained("./tok"      )
												self.assertTrue(tokenizer.is_fast      )
												SCREAMING_SNAKE_CASE    			=       tokenizer.encode(
												    lowerCAmelCase_	,							)
												self.assertEqual(lowerCAmelCase_	,							[31_957, 250, 1_345, 9, 10, 4_758]      )





import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property


@unittest.skipUnless(os.path.exists(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												self.resolver.convert_models(["heb-eng"]      )





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"




