



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__					  =  'pt'
elif is_tf_available():
				lowerCAmelCase__					  =  'tf'
else:
				lowerCAmelCase__					  =  'jax'

class 				snake_case__(lowerCamelCase__    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		PerceiverTokenizer
					lowercase_								=		False







					def 			snake_case					(							self :     Tuple					):
									super().setUp()
									lowercase__					:   Tuple													=					PerceiverTokenizer()
									tokenizer.save_pretrained(self.tmpdirname					)







					@cached_property
					def 			snake_case					(							self :     Dict					):
									return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"					)







					def 			snake_case					(							self :     List[Any]				,       **SCREAMING_SNAKE_CASE :     Tuple					):
									return self.tokenizer_class.from_pretrained(self.tmpdirname				,       **__snake_case					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=False				,       SCREAMING_SNAKE_CASE :     str=20				,       SCREAMING_SNAKE_CASE :     List[str]=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.

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

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

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







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   str													=					self.perceiver_tokenizer
									lowercase__					:   Optional[Any]													=					'Unicode €.'
									lowercase__					:   Tuple													=					tokenizer(__snake_case					)
									lowercase__					:   Optional[int]													=					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
									self.assertEqual(encoded["input_ids"]				,       __snake_case					)

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

									lowercase__					:   List[str]													=					tokenizer("e è é ê ë"					)
									lowercase__					:   Dict													=					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
									self.assertEqual(encoded["input_ids"]				,       __snake_case					)
									# decoding
									lowercase__					:   Tuple													=					tokenizer.decode(__snake_case					)
									self.assertEqual(__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 			snake_case					(							self :     str					):
									lowercase__					:   List[str]													=					self.perceiver_tokenizer
									lowercase__					:   Optional[int]													=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
									# fmt: off
									lowercase__					:   Dict													=					[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
									lowercase__					:   Optional[int]													=					tokenizer(__snake_case				,       padding=__snake_case				,       return_tensors=__snake_case					)
									self.assertIsInstance(__snake_case				,       __snake_case					)

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

									self.assertListEqual(__snake_case				,       __snake_case					)

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







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   Optional[int]													=					self.perceiver_tokenizer
									lowercase__					:   Dict													=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
									lowercase__					:   str													=					tokenizer(__snake_case				,       padding=__snake_case				,       return_tensors=__snake_case					)
									# check if input_ids are returned and no decoder_input_ids
									self.assertIn("input_ids"				,       __snake_case					)
									self.assertIn("attention_mask"				,       __snake_case					)
									self.assertNotIn("decoder_input_ids"				,       __snake_case					)
									self.assertNotIn("decoder_attention_mask"				,       __snake_case					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[Any]													=					self.perceiver_tokenizer
									lowercase__					:   Union[str, Any]													=					[
									    'Summary of the text.',
									    'Another summary.',
									]
									lowercase__					:   List[str]													=					tokenizer(
									    text_target=__snake_case				,       max_length=32				,       padding="max_length"				,       truncation=__snake_case				,       return_tensors=__snake_case					)
									self.assertEqual(32				,       targets["input_ids"].shape[1]					)







					def 			snake_case					(							self :     List[Any]					):
									# safety check on max_len default value so we are sure the test works
									lowercase__					:   Union[str, Any]													=					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
									lowercase__					:   List[str]													=					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
																	lowercase__					:   Optional[int]													=					tempfile.mkdtemp()

																	lowercase__					:   Tuple													=					' He is very happy, UNwant\u00E9d,running'
																	lowercase__					:   Any													=					tokenizer.encode(__snake_case				,       add_special_tokens=__snake_case					)
																	tokenizer.save_pretrained(__snake_case					)

																	lowercase__					:   List[Any]													=					tokenizer.__class__.from_pretrained(__snake_case					)
																	lowercase__					:   int													=					after_tokenizer.encode(__snake_case				,       add_special_tokens=__snake_case					)
																	self.assertListEqual(__snake_case				,       __snake_case					)

																	shutil.rmtree(__snake_case					)

									lowercase__					:   Dict													=					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
																	lowercase__					:   List[str]													=					tempfile.mkdtemp()

																	lowercase__					:   int													=					' He is very happy, UNwant\u00E9d,running'
																	tokenizer.add_tokens(["bim", "bambam"]					)
																	lowercase__					:   List[str]													=					tokenizer.additional_special_tokens
																	additional_special_tokens.append("new_additional_special_token"					)
																	tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}					)
																	lowercase__					:   Union[str, Any]													=					tokenizer.encode(__snake_case				,       add_special_tokens=__snake_case					)
																	tokenizer.save_pretrained(__snake_case					)

																	lowercase__					:   int													=					tokenizer.__class__.from_pretrained(__snake_case					)
																	lowercase__					:   List[Any]													=					after_tokenizer.encode(__snake_case				,       add_special_tokens=__snake_case					)
																	self.assertListEqual(__snake_case				,       __snake_case					)
																	self.assertIn("new_additional_special_token"				,       after_tokenizer.additional_special_tokens					)
																	self.assertEqual(after_tokenizer.model_max_length				,       42					)

																	lowercase__					:   str													=					tokenizer.__class__.from_pretrained(__snake_case				,       model_max_length=43					)
																	self.assertEqual(tokenizer.model_max_length				,       43					)

																	shutil.rmtree(__snake_case					)







					def 			snake_case					(							self :     List[str]					):
									lowercase__					:   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(__snake_case					)

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

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

																	lowercase__					:   Dict													=					[f"""<extra_id_{i}>""" for i in range(125					)]

																	lowercase__					:   str													=					added_tokens_extra_ids + [
																	    'an_additional_special_token'
																	]
																	lowercase__					:   Dict													=					added_tokens_extra_ids + [
																	    'an_additional_special_token'
																	]

																	with open(os.path.join(__snake_case				,       "special_tokens_map.json"					)				,       "w"				,       encoding="utf-8"					) as outfile:
																					json.dump(__snake_case				,       __snake_case					)
																	with open(os.path.join(__snake_case				,       "tokenizer_config.json"					)				,       "w"				,       encoding="utf-8"					) as outfile:
																					json.dump(__snake_case				,       __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
																	lowercase__					:   Tuple													=					tokenizer_class.from_pretrained(
																	    __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
																	lowercase__					:   Dict													=					added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"				,       lstrip=__snake_case					)]
																	lowercase__					:   Tuple													=					tokenizer_class.from_pretrained(
																	    __snake_case				,       additional_special_tokens=__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 			snake_case					(							self :     Tuple					):
									lowercase__					:   Tuple													=					self.perceiver_tokenizer
									self.assertEqual(tokenizer.decode([178]					)				,       "�"					)







					def 			snake_case					(							self :     Union[str, Any]					):
									pass







					def 			snake_case					(							self :     int					):
									pass







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







					def 			snake_case					(							self :     str					):
									pass







					def 			snake_case					(							self :     Optional[Any]					):
									# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
									# strings and special added tokens as tokens
									lowercase__					:   str													=					self.get_tokenizers(fast=__snake_case				,       do_lower_case=__snake_case					)
									for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
																	lowercase__					:   Optional[Any]													=					['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
																	lowercase__					:   Tuple													=					tokenizer.convert_tokens_to_string(__snake_case					)

																	self.assertIsInstance(__snake_case				,       __snake_case					)









from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase__					  =  1.054571817e-34  # unit of ℏ : J * s

lowerCAmelCase__					  =  3e8  # unit of c : m * s^-1


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				if (force, area, distance).count(0						) != 1:
								raise ValueError("One and only one argument must be 0"						)
				if force < 0:
								raise ValueError("Magnitude of force can not be negative"						)
				if distance < 0:
								raise ValueError("Distance can not be negative"						)
				if area < 0:
								raise ValueError("Area can not be negative"						)
				if force == 0:
								lowercase__					:   Optional[Any]													=					(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
								    240 * (distance) ** 4
								)
								return {"force": force}
				elif area == 0:
								lowercase__					:   str													=					(240 * force * (distance) ** 4) / (
								    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
								)
								return {"area": area}
				elif distance == 0:
								lowercase__					:   Tuple													=					(
								    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
								) ** (1 / 4)
								return {"distance": distance}
				raise ValueError("One and only one argument must be 0"						)


# Run doctest
if __name__ == "__main__":
				import doctest

				doctest.testmod()




