

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





class 	snake_case_   (		__A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				ByTaTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				False







			def 				snake_case__(     self :				Optional[Any]							)      ->Any:
									super().setUp()
									snake_case_   =	ByTaTokenizer()
									tokenizer.save_pretrained(self.tmpdirname							)







			@cached_property
			def 				snake_case__(     self :				List[str]							)      ->Any:
									return ByTaTokenizer.from_pretrained('''google/byt5-small'''							)







			def 				snake_case__(     self :				str							,   **_UpperCamelCase :				Optional[Any]							)      ->ByTaTokenizer:
									return self.tokenizer_class.from_pretrained(self.tmpdirname							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Any							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				str=2_0							,   _UpperCamelCase :				Any=5							)      ->Tuple[str, list]:
									# XXX The default common tokenizer tests assume that every ID is decodable on its own.
									# This assumption is invalid for ByT5 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.

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

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

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







			def 				snake_case__(     self :				Optional[int]							)      ->Optional[Any]:
									snake_case_   =	self.ta_base_tokenizer
									snake_case_   =	tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']							)
									snake_case_   =	tokenizer(['''hi''', '''I went to the gym''', '''''']							)
									self.assertListEqual(batch_with_eos_added['''input_ids''']							,   batch_without_eos_added['''input_ids''']							)







			def 				snake_case__(     self :				int							)      ->Union[str, Any]:
									snake_case_   =	self.ta_base_tokenizer
									snake_case_   =	'''Unicode €.'''
									snake_case_   =	tokenizer(_UpperCamelCase							)
									snake_case_   =	[8_8, 1_1_3, 1_0_8, 1_0_2, 1_1_4, 1_0_3, 1_0_4, 3_5, 2_2_9, 1_3_3, 1_7_5, 4_9, 1]
									self.assertEqual(encoded['''input_ids''']							,   _UpperCamelCase							)

									# decoding
									snake_case_   =	tokenizer.decode(_UpperCamelCase							)
									self.assertEqual(_UpperCamelCase							,   '''Unicode €.</s>'''							)

									snake_case_   =	tokenizer('''e è é ê ë'''							)
									snake_case_   =	[1_0_4, 3_5, 1_9_8, 1_7_1, 3_5, 1_9_8, 1_7_2, 3_5, 1_9_8, 1_7_3, 3_5, 1_9_8, 1_7_4, 1]
									self.assertEqual(encoded['''input_ids''']							,   _UpperCamelCase							)
									# decoding
									snake_case_   =	tokenizer.decode(_UpperCamelCase							)
									self.assertEqual(_UpperCamelCase							,   '''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 				snake_case__(     self :				List[Any]							)      ->Tuple:
									snake_case_   =	self.ta_base_tokenizer
									snake_case_   =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									# fmt: off
									snake_case_   =	[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 1, 0]
									# fmt: on
									snake_case_   =	tokenizer(_UpperCamelCase							,   padding=_UpperCamelCase							,   return_tensors=_UpperCamelCase							)
									self.assertIsInstance(_UpperCamelCase							,   _UpperCamelCase							)

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

									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)

									self.assertEqual((2, 3_7)							,   batch.input_ids.shape							)
									self.assertEqual((2, 3_7)							,   batch.attention_mask.shape							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->Any:
									snake_case_   =	self.ta_base_tokenizer
									snake_case_   =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									snake_case_   =	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 				snake_case__(     self :				Dict							)      ->List[str]:
									snake_case_   =	self.ta_base_tokenizer
									snake_case_   =	[
									    '''Summary of the text.''',
									    '''Another summary.''',
									]
									snake_case_   =	tokenizer(
									    text_target=_UpperCamelCase							,   max_length=3_2							,   padding='''max_length'''							,   truncation=_UpperCamelCase							,   return_tensors=_UpperCamelCase							)
									self.assertEqual(3_2							,   targets['''input_ids'''].shape[1]							)







			def 				snake_case__(     self :				Tuple							)      ->Tuple:
									snake_case_   =	self.ta_base_tokenizer
									snake_case_   =	['''A long paragraph for summarization. </s>''']
									snake_case_   =	['''Summary of the text. </s>''']
									# fmt: off
									snake_case_   =	[6_8, 3_5, 1_1_1, 1_1_4, 1_1_3, 1_0_6, 3_5, 1_1_5, 1_0_0, 1_1_7, 1_0_0, 1_0_6, 1_1_7, 1_0_0, 1_1_5, 1_0_7, 3_5, 1_0_5, 1_1_4, 1_1_7, 3_5, 1_1_8, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_0_8, 1_2_5, 1_0_0, 1_1_9, 1_0_8, 1_1_4, 1_1_3, 4_9, 3_5, 1]
									snake_case_   =	[8_6, 1_2_0, 1_1_2, 1_1_2, 1_0_0, 1_1_7, 1_2_4, 3_5, 1_1_4, 1_0_5, 3_5, 1_1_9, 1_0_7, 1_0_4, 3_5, 1_1_9, 1_0_4, 1_2_3, 1_1_9, 4_9, 3_5, 1]
									# fmt: on

									snake_case_   =	tokenizer(_UpperCamelCase							,   text_target=_UpperCamelCase							)

									self.assertEqual(_UpperCamelCase							,   batch['''input_ids'''][0]							)
									self.assertEqual(_UpperCamelCase							,   batch['''labels'''][0]							)







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									# safety check on max_len default value so we are sure the test works
									snake_case_   =	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
									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
																					snake_case_   =	tempfile.mkdtemp()

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

																					snake_case_   =	tokenizer.__class__.from_pretrained(_UpperCamelCase							)
																					snake_case_   =	after_tokenizer.encode(_UpperCamelCase							,   add_special_tokens=_UpperCamelCase							)
																					self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)

																					shutil.rmtree(_UpperCamelCase							)

									snake_case_   =	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
																					snake_case_   =	tempfile.mkdtemp()

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

																					snake_case_   =	tokenizer.__class__.from_pretrained(_UpperCamelCase							)
																					snake_case_   =	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							,   4_2							)

																					snake_case_   =	tokenizer.__class__.from_pretrained(_UpperCamelCase							,   model_max_length=4_3							)
																					self.assertEqual(tokenizer.model_max_length							,   4_3							)

																					shutil.rmtree(_UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->List[Any]:
									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(_UpperCamelCase							)

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

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

																					snake_case_   =	[f'''<extra_id_{i}>''' for i in range(1_2_5							)]

																					snake_case_   =	added_tokens_extra_ids + [
																					    '''an_additional_special_token'''
																					]
																					snake_case_   =	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
																					snake_case_   =	tokenizer_class.from_pretrained(
																					    _UpperCamelCase							,   )
																					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
																					snake_case_   =	added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''							,   lstrip=_UpperCamelCase							)]
																					snake_case_   =	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 				snake_case__(     self :				str							)      ->List[str]:
									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(_UpperCamelCase							)

																					snake_case_   =	tokenizer_class.from_pretrained(_UpperCamelCase							)

																					self.assertTrue(tokenizer.decode([2_5_5]							) == ''''''							)







			def 				snake_case__(     self :				Tuple							)      ->Optional[int]:
									pass







			def 				snake_case__(     self :				int							)      ->str:
									pass







			def 				snake_case__(     self :				Tuple							)      ->str:
									pass







			def 				snake_case__(     self :				int							)      ->Optional[Any]:
									pass







			def 				snake_case__(     self :				Tuple							)      ->Dict:
									# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
									# and special added tokens as tokens
									snake_case_   =	self.get_tokenizers(fast=_UpperCamelCase							,   do_lower_case=_UpperCamelCase							)
									for tokenizer in tokenizers:
															with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
																					snake_case_   =	['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
																					snake_case_   =	tokenizer.convert_tokens_to_string(_UpperCamelCase							)

																					self.assertIsInstance(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				List[Any]							)      ->Tuple:
									snake_case_   =	self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
																					snake_case_   =	[
																					    '''bos_token''',
																					    '''eos_token''',
																					    '''unk_token''',
																					    '''sep_token''',
																					    '''pad_token''',
																					    '''cls_token''',
																					    '''mask_token''',
																					]

																					snake_case_   =	0
																					snake_case_   =	tokenizer.convert_ids_to_tokens(
																					    _UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							)

																					for attr in attributes_list:
																											setattr(_UpperCamelCase							,   attr + '''_id'''							,   _UpperCamelCase							)
																											self.assertEqual(getattr(_UpperCamelCase							,   _UpperCamelCase							)							,   _UpperCamelCase							)
																											self.assertEqual(getattr(_UpperCamelCase							,   attr + '''_id'''							)							,   _UpperCamelCase							)

																											setattr(_UpperCamelCase							,   attr + '''_id'''							,   _UpperCamelCase							)
																											self.assertEqual(getattr(_UpperCamelCase							,   _UpperCamelCase							)							,   _UpperCamelCase							)
																											self.assertEqual(getattr(_UpperCamelCase							,   attr + '''_id'''							)							,   _UpperCamelCase							)

																					setattr(_UpperCamelCase							,   '''additional_special_tokens_ids'''							,   []							)
																					self.assertListEqual(getattr(_UpperCamelCase							,   '''additional_special_tokens'''							)							,   []							)
																					self.assertListEqual(getattr(_UpperCamelCase							,   '''additional_special_tokens_ids'''							)							,   []							)

																					setattr(_UpperCamelCase							,   '''additional_special_tokens_ids'''							,   [token_id_to_test_setters]							)
																					self.assertListEqual(getattr(_UpperCamelCase							,   '''additional_special_tokens'''							)							,   [token_to_test_setters]							)
																					self.assertListEqual(getattr(_UpperCamelCase							,   '''additional_special_tokens_ids'''							)							,   [token_id_to_test_setters]							)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)