


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				:       int      =  'pt'
elif is_tf_available():
	A				:       Optional[Any]      =  'tf'
else:
	A				:       str      =  'jax'






class      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					Any    					=						PerceiverTokenizer
					snake_case_		:					str    					=						False


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

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


					@cached_property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Tuple:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     **SCREAMING_SNAKE_CASE						:		Union[str, Any]    )      ->						PerceiverTokenizer:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str=False  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=2_0  ,     SCREAMING_SNAKE_CASE						:		str=5    )      ->						Tuple[str, list]:




											'''simple docstring'''

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

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		"Unicode €."
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE    )
											__snake_case				=		[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"]  ,     SCREAMING_SNAKE_CASE    )

											# decoding
											__snake_case				=		tokenizer.decode(SCREAMING_SNAKE_CASE    )
											self.assertEqual(SCREAMING_SNAKE_CASE  ,     "[CLS]Unicode €.[SEP]"    )

											__snake_case				=		tokenizer("e è é ê ë"    )
											__snake_case				=		[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"]  ,     SCREAMING_SNAKE_CASE    )
											# decoding
											__snake_case				=		tokenizer.decode(SCREAMING_SNAKE_CASE    )
											self.assertEqual(SCREAMING_SNAKE_CASE  ,     "[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						:		Tuple    )      ->						str:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		["A long paragraph for summarization.", "Another paragraph for summarization."]
											# fmt: off
											__snake_case				=		[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
											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE  ,     padding=SCREAMING_SNAKE_CASE  ,     return_tensors=SCREAMING_SNAKE_CASE    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

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

											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Optional[int]:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											__snake_case				=		[
											    "Summary of the text.",
											    "Another summary.",
											]
											__snake_case				=		tokenizer(
											    text_target=SCREAMING_SNAKE_CASE  ,     max_length=3_2  ,     padding="max_length"  ,     truncation=SCREAMING_SNAKE_CASE  ,     return_tensors=SCREAMING_SNAKE_CASE    )
											self.assertEqual(3_2  ,     targets["input_ids"].shape[1]    )


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




											'''simple docstring'''

											__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(SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    )
																							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE    )

																							__snake_case				=		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE    )
																							__snake_case				=		after_tokenizer.encode(SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    )
																							self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																							shutil.rmtree(SCREAMING_SNAKE_CASE    )

											__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(SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    )
																							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE    )

																							__snake_case				=		tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE    )
																							__snake_case				=		after_tokenizer.encode(SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    )
																							self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
																							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(SCREAMING_SNAKE_CASE  ,     model_max_length=4_3    )
																							self.assertEqual(tokenizer.model_max_length  ,     4_3    )

																							shutil.rmtree(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Dict:




											'''simple docstring'''

											__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(SCREAMING_SNAKE_CASE    )

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

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

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

																							# 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(
																							    SCREAMING_SNAKE_CASE  ,     )
																							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
																							__snake_case				=		added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"  ,     lstrip=SCREAMING_SNAKE_CASE    )]
																							__snake_case				=		tokenizer_class.from_pretrained(
																							    SCREAMING_SNAKE_CASE  ,     additional_special_tokens=SCREAMING_SNAKE_CASE  ,     )

																							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						:		List[str]    )      ->						str:




											'''simple docstring'''

											__snake_case				=		self.perceiver_tokenizer
											self.assertEqual(tokenizer.decode([1_7_8]    )  ,     "�"    )


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




											'''simple docstring'''

											pass


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




											'''simple docstring'''

											pass


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Optional[Any]:




											'''simple docstring'''

											pass


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




											'''simple docstring'''

											pass






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




											'''simple docstring'''

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

																							self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )









from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A				:       Optional[Any]      =  {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       Optional[Any]      =  [
	    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'VanForImageClassification',
	    'VanModel',
	    'VanPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_van import (
		    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VanForImageClassification,
		    VanModel,
		    VanPreTrainedModel,
		)

else:
	import sys

	A				:       Union[str, Any]      =  _LazyModule(__name__, globals()['__file__'], _import_structure)





