

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							:  Tuple            =						"""pt"""
elif is_tf_available():
						lowerCAmelCase							:  Tuple            =						"""tf"""
else:
						lowerCAmelCase							:  List[str]            =						"""jax"""



class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					PerceiverTokenizer
			_UpperCAmelCase       :							List[Any]      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									super().setUp()
									SCREAMING_SNAKE_CASE_:			Any  		=	PerceiverTokenizer()
									tokenizer.save_pretrained(self.tmpdirname)



			@cached_property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						**lowerCAmelCase__  :					Union[str, Any]):
									return self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Any=False       ,						lowerCAmelCase__  :					Tuple=20       ,						lowerCAmelCase__  :					List[Any]=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.

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
									for i in range(len(lowerCAmelCase__)):
															try:
																					SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.decode([i]       ,						clean_up_tokenization_spaces=lowerCAmelCase__)
															except UnicodeDecodeError:
																					pass
															toks.append((i, tok))

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	list(filter(lambda lowerCAmelCase__: re.match(R"^[ a-zA-Z]+$"       ,						t[1])       ,						lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	list(filter(lambda lowerCAmelCase__: [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_:			Optional[int]  		=	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_:			Optional[Any]  		=	toks + toks
        # toks_str = [t[1] for t in toks]
									SCREAMING_SNAKE_CASE_:			str  		=	[t[0] for t in toks]

									# Ensure consistency
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(lowerCAmelCase__       ,						clean_up_tokenization_spaces=lowerCAmelCase__)
									if " " not in output_txt and len(lowerCAmelCase__) > 1:
															SCREAMING_SNAKE_CASE_:			Dict  		=	(
															    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_:			Optional[Any]  		=	" " + output_txt
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.encode(lowerCAmelCase__       ,						add_special_tokens=lowerCAmelCase__)
									return output_txt, output_ids



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_:			str  		=	"Unicode €."
									SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
									self.assertEqual(encoded["input_ids"]       ,						lowerCAmelCase__)

									# decoding
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.decode(lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__       ,						"[CLS]Unicode €.[SEP]")

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer("e è é ê ë")
									SCREAMING_SNAKE_CASE_:			Dict  		=	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
									self.assertEqual(encoded["input_ids"]       ,						lowerCAmelCase__)
									# decoding
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__       ,						"[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  :					List[str]):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_:			Dict  		=	["A long paragraph for summarization.", "Another paragraph for summarization."]
									# fmt: off
									SCREAMING_SNAKE_CASE_:			Any  		=	[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
									# fmt: on
									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer(lowerCAmelCase__       ,						padding=lowerCAmelCase__       ,						return_tensors=lowerCAmelCase__)
									self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)

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

									self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

									self.assertEqual((2, 38)       ,						batch.input_ids.shape)
									self.assertEqual((2, 38)       ,						batch.attention_mask.shape)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	["A long paragraph for summarization.", "Another paragraph for summarization."]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.perceiver_tokenizer
									SCREAMING_SNAKE_CASE_:			Any  		=	[
									    "Summary of the text.",
									    "Another summary.",
									]
									SCREAMING_SNAKE_CASE_:			str  		=	tokenizer(
									    text_target=lowerCAmelCase__       ,						max_length=32       ,						padding="max_length"       ,						truncation=lowerCAmelCase__       ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(32       ,						targets["input_ids"].shape[1])



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									# safety check on max_len default value so we are sure the test works
									SCREAMING_SNAKE_CASE_:			str  		=	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_:			Any  		=	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_:			Dict  		=	tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE_:			List[str]  		=	" He is very happy, UNwant\u00E9d,running"
																					SCREAMING_SNAKE_CASE_:			Any  		=	tokenizer.encode(lowerCAmelCase__       ,						add_special_tokens=lowerCAmelCase__)
																					tokenizer.save_pretrained(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			int  		=	tokenizer.__class__.from_pretrained(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			int  		=	after_tokenizer.encode(lowerCAmelCase__       ,						add_special_tokens=lowerCAmelCase__)
																					self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

																					shutil.rmtree(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	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_:			int  		=	tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE_:			Dict  		=	" He is very happy, UNwant\u00E9d,running"
																					tokenizer.add_tokens(["bim", "bambam"])
																					SCREAMING_SNAKE_CASE_:			str  		=	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_:			List[str]  		=	tokenizer.encode(lowerCAmelCase__       ,						add_special_tokens=lowerCAmelCase__)
																					tokenizer.save_pretrained(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			Any  		=	tokenizer.__class__.from_pretrained(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			str  		=	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_:			Optional[int]  		=	tokenizer.__class__.from_pretrained(lowerCAmelCase__       ,						model_max_length=43)
																					self.assertEqual(tokenizer.model_max_length       ,						43)

																					shutil.rmtree(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			str  		=	[]
									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_:			str  		=	json.load(lowerCAmelCase__)

																					with open(os.path.join(lowerCAmelCase__       ,						"tokenizer_config.json")       ,						encoding="utf-8") as json_file:
																											SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	json.load(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[F"<extra_id_{i}>" for i in range(125)]

																					SCREAMING_SNAKE_CASE_:			Any  		=	added_tokens_extra_ids + [
																					    "an_additional_special_token"
																					]
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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_:			List[str]  		=	tokenizer_class.from_pretrained(
																					    lowerCAmelCase__       ,						)
																					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
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"       ,						lstrip=lowerCAmelCase__)]
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.perceiver_tokenizer
									self.assertEqual(tokenizer.decode([178])       ,						"�")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									pass



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									pass




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
									# strings and special added tokens as tokens
									SCREAMING_SNAKE_CASE_:			Dict  		=	self.get_tokenizers(fast=lowerCAmelCase__       ,						do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_:			Dict  		=	["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer.convert_tokens_to_string(lowerCAmelCase__)

																					self.assertIsInstance(lowerCAmelCase__       ,						lowerCAmelCase__)









from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class   __lowercase    :




			"""simple docstring"""







			_UpperCAmelCase       :							int
			_UpperCAmelCase       :							int







class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Any       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			list[list[Edge]]  		=	[[] for _ in range(lowerCAmelCase__)]
									SCREAMING_SNAKE_CASE_:			Dict  		=	size



			def __getitem__(			self  :					Dict       ,						lowerCAmelCase__  :					int):
									return iter(self._graph[vertex])



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									return self._size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int):
									if weight not in (0, 1):
															raise ValueError("Edge weight must be either 0 or 1.")

									if to_vertex < 0 or to_vertex >= self.size:
															raise ValueError("Vertex indexes must be in [0; size).")

									self._graph[from_vertex].append(Edge(lowerCAmelCase__       ,						lowerCAmelCase__))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					int       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	deque([start_vertex])
									SCREAMING_SNAKE_CASE_:			list[int | None]  		=	[None] * self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	0

									while queue:
															SCREAMING_SNAKE_CASE_:			int  		=	queue.popleft()
															SCREAMING_SNAKE_CASE_:			str  		=	distances[current_vertex]
															if current_distance is None:
																					continue

															for edge in self[current_vertex]:
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	current_distance + edge.weight
																					SCREAMING_SNAKE_CASE_:			str  		=	distances[edge.destination_vertex]
																					if (
																					    isinstance(lowerCAmelCase__       ,						lowerCAmelCase__)
																					    and new_distance >= dest_vertex_distance
																					):
																											continue
																					SCREAMING_SNAKE_CASE_:			Any  		=	new_distance
																					if edge.weight == 0:
																											queue.appendleft(edge.destination_vertex)
																					else:
																											queue.append(edge.destination_vertex)

									if distances[finish_vertex] is None:
															raise ValueError("No path from start_vertex to finish_vertex.")

									return distances[finish_vertex]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






