




"""simple docstring"""






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






class lowerCAmelCase__      (	_a,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     PerceiverTokenizer
			lowerCamelCase__ =     False
			def    A_       (    self    ):
							super().setUp()
							_lowerCamelCase	:     Tuple       =       PerceiverTokenizer()
							tokenizer.save_pretrained(self.tmpdirname    )
			@cached_property
			def    A_       (    self    ):
							return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'    )
			def    A_       (    self    , **lowercase    ):
							return self.tokenizer_class.from_pretrained(self.tmpdirname    , **__UpperCamelCase    )
			def    A_       (    self    , lowercase    , lowercase=False    , lowercase=20    , lowercase=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.

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

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

							# Ensure consistency
							_lowerCamelCase	:     List[Any]       =       tokenizer.decode(__UpperCamelCase    , clean_up_tokenization_spaces=__UpperCamelCase    )
							if " " not in output_txt and len(__UpperCamelCase    ) > 1:
											_lowerCamelCase	:     List[str]       =       (
											    tokenizer.decode([toks_ids[0]]    , clean_up_tokenization_spaces=__UpperCamelCase    )
											    + ' '
											    + tokenizer.decode(toks_ids[1:]    , clean_up_tokenization_spaces=__UpperCamelCase    )
											)
							if with_prefix_space:
											_lowerCamelCase	:     str       =       ' ' + output_txt
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.encode(__UpperCamelCase    , add_special_tokens=__UpperCamelCase    )
							return output_txt, output_ids
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       self.perceiver_tokenizer
							_lowerCamelCase	:     Optional[Any]       =       'Unicode €.'
							_lowerCamelCase	:     Any       =       tokenizer(__UpperCamelCase    )
							_lowerCamelCase	:     Optional[int]       =       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
							self.assertEqual(encoded['input_ids']    , __UpperCamelCase    )

							# decoding
							_lowerCamelCase	:     Optional[int]       =       tokenizer.decode(__UpperCamelCase    )
							self.assertEqual(__UpperCamelCase    , '[CLS]Unicode €.[SEP]'    )

							_lowerCamelCase	:     str       =       tokenizer('e è é ê ë'    )
							_lowerCamelCase	:     Any       =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
							self.assertEqual(encoded['input_ids']    , __UpperCamelCase    )
							# decoding
							_lowerCamelCase	:     str       =       tokenizer.decode(__UpperCamelCase    )
							self.assertEqual(__UpperCamelCase    , '[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    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.perceiver_tokenizer
							_lowerCamelCase	:     Any       =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							# fmt: off
							_lowerCamelCase	:     List[str]       =       [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
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer(__UpperCamelCase    , padding=__UpperCamelCase    , return_tensors=__UpperCamelCase    )
							self.assertIsInstance(__UpperCamelCase    , __UpperCamelCase    )

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

							self.assertListEqual(__UpperCamelCase    , __UpperCamelCase    )

							self.assertEqual((2, 38)    , batch.input_ids.shape    )
							self.assertEqual((2, 38)    , batch.attention_mask.shape    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.perceiver_tokenizer
							_lowerCamelCase	:     Dict       =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							_lowerCamelCase	:     Optional[Any]       =       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    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.perceiver_tokenizer
							_lowerCamelCase	:     Dict       =       [
							    'Summary of the text.',
							    'Another summary.',
							]
							_lowerCamelCase	:     List[Any]       =       tokenizer(
							    text_target=__UpperCamelCase    , max_length=32    , padding='max_length'    , truncation=__UpperCamelCase    , return_tensors=__UpperCamelCase    )
							self.assertEqual(32    , targets['input_ids'].shape[1]    )
			def    A_       (    self    ):
							# safety check on max_len default value so we are sure the test works
							_lowerCamelCase	:     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
							_lowerCamelCase	:     Dict       =       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
															_lowerCamelCase	:     Optional[int]       =       tempfile.mkdtemp()

															_lowerCamelCase	:     List[Any]       =       ' He is very happy, UNwant\u00E9d,running'
															_lowerCamelCase	:     Optional[int]       =       tokenizer.encode(__UpperCamelCase    , add_special_tokens=__UpperCamelCase    )
															tokenizer.save_pretrained(__UpperCamelCase    )

															_lowerCamelCase	:     str       =       tokenizer.__class__.from_pretrained(__UpperCamelCase    )
															_lowerCamelCase	:     str       =       after_tokenizer.encode(__UpperCamelCase    , add_special_tokens=__UpperCamelCase    )
															self.assertListEqual(__UpperCamelCase    , __UpperCamelCase    )

															shutil.rmtree(__UpperCamelCase    )

							_lowerCamelCase	:     str       =       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
															_lowerCamelCase	:     List[Any]       =       tempfile.mkdtemp()

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

															_lowerCamelCase	:     Any       =       tokenizer.__class__.from_pretrained(__UpperCamelCase    )
															_lowerCamelCase	:     List[str]       =       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    , 42    )

															_lowerCamelCase	:     List[Any]       =       tokenizer.__class__.from_pretrained(__UpperCamelCase    , model_max_length=43    )
															self.assertEqual(tokenizer.model_max_length    , 43    )

															shutil.rmtree(__UpperCamelCase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       []
							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:
																			_lowerCamelCase	:     str       =       json.load(__UpperCamelCase    )

															with open(os.path.join(__UpperCamelCase    , 'tokenizer_config.json'    )    , encoding='utf-8'    ) as json_file:
																			_lowerCamelCase	:     List[Any]       =       json.load(__UpperCamelCase    )

															_lowerCamelCase	:     Union[str, Any]       =       [F'''<extra_id_{i}>''' for i in range(125    )]

															_lowerCamelCase	:     Dict       =       added_tokens_extra_ids + [
															    'an_additional_special_token'
															]
															_lowerCamelCase	:     Tuple       =       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
															_lowerCamelCase	:     Optional[int]       =       tokenizer_class.from_pretrained(
															    __UpperCamelCase    , )
															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
															_lowerCamelCase	:     List[str]       =       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'    , lstrip=__UpperCamelCase    )]
															_lowerCamelCase	:     List[Any]       =       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    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       self.perceiver_tokenizer
							self.assertEqual(tokenizer.decode([178]    )    , '�'    )
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							pass






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

															self.assertIsInstance(__UpperCamelCase    , __UpperCamelCase    )

from __future__ import annotations




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	float		,   SCREAMING_SNAKE_CASE_	:	float		,   SCREAMING_SNAKE_CASE_	:	float		,   )  ->     tuple:
						if (electron_conc, hole_conc, intrinsic_conc).count(0       ) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""       )
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""       )
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""       )
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""       )
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
		import doctest

		doctest.testmod()