






"""simple docstring"""
import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_UpperCamelCase       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				RobertaTokenizer
	_A       :				List[str]    =				RobertaTokenizerFast
	_A       :				int    =				True
	_A       :				int    =				{"""cls_token""": """<s>"""}





	def    lowerCamelCase(self							):
		super().setUp()

		# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
		A_		:						Dict	    =   [
		    '''l''',
		    '''o''',
		    '''w''',
		    '''e''',
		    '''r''',
		    '''s''',
		    '''t''',
		    '''i''',
		    '''d''',
		    '''n''',
		    '''\u0120''',
		    '''\u0120l''',
		    '''\u0120n''',
		    '''\u0120lo''',
		    '''\u0120low''',
		    '''er''',
		    '''\u0120lowest''',
		    '''\u0120newer''',
		    '''\u0120wider''',
		    '''<unk>''',
		]
		A_		:						Any	    =   dict(zip(_UpperCAmelCase     ,     range(len(_UpperCAmelCase							)							)							)							)
		A_		:						Optional[Any]	    =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
		A_		:						Optional[int]	    =   {'''unk_token''': '''<unk>'''}

		A_		:						List[Any]	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		A_		:						Tuple	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]							)
		with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""							) as fp:
			fp.write(json.dumps(_UpperCAmelCase							) + """\n"""							)
		with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""							) as fp:
			fp.write("""\n""".join(_UpperCAmelCase							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		kwargs.update(self.special_tokens_map							)
		return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **_UpperCAmelCase							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		kwargs.update(self.special_tokens_map							)
		return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,     **_UpperCAmelCase							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   '''lower newer'''
		A_		:						List[str]	    =   '''lower newer'''
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						str	    =   self.tokenizer_class(self.vocab_file     ,     self.merges_file     ,     **self.special_tokens_map							)
		A_		:						Dict	    =   '''lower newer'''
		A_		:						List[Any]	    =   ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
		A_		:						str	    =   tokenizer.tokenize(_UpperCAmelCase							)  # , add_prefix_space=True)
		self.assertListEqual(_UpperCAmelCase     ,     _UpperCAmelCase							)

		A_		:						Optional[Any]	    =   tokens + [tokenizer.unk_token]
		A_		:						Optional[Any]	    =   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
		self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase							)     ,     _UpperCAmelCase							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_tokenizer()

		self.assertListEqual(tokenizer.encode("""Hello world!"""     ,     add_special_tokens=_UpperCAmelCase							)     ,     [0, 31414, 232, 328, 2]							)
		self.assertListEqual(
		    tokenizer.encode("""Hello world! cécé herlolip 418"""     ,     add_special_tokens=_UpperCAmelCase							)     ,     [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]     ,     )





	@slow
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.tokenizer_class.from_pretrained("""roberta-base"""							)

		A_		:						List[str]	    =   tokenizer.encode("""sequence builders"""     ,     add_special_tokens=_UpperCAmelCase							)
		A_		:						Tuple	    =   tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=_UpperCAmelCase							)

		A_		:						Optional[int]	    =   tokenizer.encode(
		    """sequence builders"""     ,     add_special_tokens=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase							)
		A_		:						List[str]	    =   tokenizer.encode(
		    """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase							)

		A_		:						Optional[int]	    =   tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase							)
		A_		:						List[str]	    =   tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     ,     _UpperCAmelCase							)

		assert encoded_sentence == encoded_text_from_decode
		assert encoded_pair == encoded_pair_from_decode





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.get_tokenizer()

		A_		:						List[Any]	    =   '''Encode this sequence.'''
		A_		:						str	    =   tokenizer.byte_encoder[''' '''.encode("""utf-8"""							)[0]]

		# Testing encoder arguments
		A_		:						str	    =   tokenizer.encode(_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase							)
		A_		:						Union[str, Any]	    =   tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
		self.assertNotEqual(_UpperCAmelCase     ,     _UpperCAmelCase							)

		A_		:						Tuple	    =   tokenizer.encode(_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase							)
		A_		:						Optional[Any]	    =   tokenizer.convert_ids_to_tokens(encoded[0]							)[0]
		self.assertEqual(_UpperCAmelCase     ,     _UpperCAmelCase							)

		tokenizer.add_special_tokens({"""bos_token""": """<s>"""}							)
		A_		:						str	    =   tokenizer.encode(_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
		A_		:						Tuple	    =   tokenizer.convert_ids_to_tokens(encoded[1]							)[0]
		self.assertNotEqual(_UpperCAmelCase     ,     _UpperCAmelCase							)

		# Testing spaces after special tokens
		A_		:						int	    =   '''<mask>'''
		tokenizer.add_special_tokens(
		    {"""mask_token""": AddedToken(_UpperCAmelCase     ,     lstrip=_UpperCAmelCase     ,     rstrip=_UpperCAmelCase							)}							)  # mask token has a left space
		A_		:						List[Any]	    =   tokenizer.convert_tokens_to_ids(_UpperCAmelCase							)

		A_		:						List[str]	    =   '''Encode <mask> sequence'''
		A_		:						Tuple	    =   '''Encode <mask>sequence'''

		A_		:						Dict	    =   tokenizer.encode(_UpperCAmelCase							)
		A_		:						Optional[Any]	    =   encoded.index(_UpperCAmelCase							)
		A_		:						List[str]	    =   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
		self.assertEqual(_UpperCAmelCase     ,     _UpperCAmelCase							)

		A_		:						str	    =   tokenizer.encode(_UpperCAmelCase							)
		A_		:						Optional[Any]	    =   encoded.index(_UpperCAmelCase							)
		A_		:						int	    =   tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]							)[0]
		self.assertNotEqual(_UpperCAmelCase     ,     _UpperCAmelCase							)





	def    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
				A_		:						List[Any]	    =   self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase     ,     **_UpperCAmelCase							)
				A_		:						Tuple	    =   self.tokenizer_class.from_pretrained(_UpperCAmelCase     ,     **_UpperCAmelCase							)
				A_		:						Optional[Any]	    =   '''A, <mask> AllenNLP sentence.'''
				A_		:						int	    =   tokenizer_r.encode_plus(_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase     ,     return_token_type_ids=_UpperCAmelCase							)
				A_		:						Optional[int]	    =   tokenizer_p.encode_plus(_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase     ,     return_token_type_ids=_UpperCAmelCase							)

				# token_type_ids should put 0 everywhere
				self.assertEqual(sum(tokens_r["""token_type_ids"""]							)     ,     sum(tokens_p["""token_type_ids"""]							)							)

				# attention_mask should put 1 everywhere, so sum over length should be 1
				self.assertEqual(
				    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)     ,     sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)     ,     )

				A_		:						List[Any]	    =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
				A_		:						int	    =   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)

				# Rust correctly handles the space before the mask while python doesnt
				self.assertSequenceEqual(tokens_p["""input_ids"""]     ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
				self.assertSequenceEqual(tokens_r["""input_ids"""]     ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

				self.assertSequenceEqual(
				    _UpperCAmelCase     ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
				self.assertSequenceEqual(
				    _UpperCAmelCase     ,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)





	def    lowerCamelCase(self							):
		for trim_offsets, add_prefix_space in itertools.product([True, False]     ,     repeat=2							):
			A_		:						Optional[int]	    =   self.rust_tokenizer_class.from_pretrained(
			    self.tmpdirname     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)

			A_		:						List[Any]	    =   json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()							)
			A_		:						str	    =   json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()							)

			self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]     ,     _UpperCAmelCase							)

			self.assertEqual(post_processor_state["""add_prefix_space"""]     ,     _UpperCAmelCase							)
			self.assertEqual(post_processor_state["""trim_offsets"""]     ,     _UpperCAmelCase							)





	def    lowerCamelCase(self							):
		# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
		# `trim_offsets`
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
				A_		:						str	    =   '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
				A_		:						Union[str, Any]	    =   f"""{text_of_1_token} {text_of_1_token}"""

				A_		:						Dict	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						Optional[Any]	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (0, len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (len(_UpperCAmelCase							) + 1, len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )

				A_		:						List[str]	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						Tuple	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (0, len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (len(_UpperCAmelCase							) + 1, len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )

				A_		:						int	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						Optional[Any]	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (0, len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (len(_UpperCAmelCase							), len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )

				A_		:						int	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						str	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (0, len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (len(_UpperCAmelCase							), len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )

				A_		:						Optional[int]	    =   f""" {text}"""

				# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
				#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
				# )
				# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
				# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
				# self.assertEqual(
				#     encoding.offset_mapping[1],
				#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
				# )

				A_		:						Dict	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						Optional[Any]	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (1, 1 + len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (1 + len(_UpperCAmelCase							) + 1, 1 + len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )

				A_		:						Tuple	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						int	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (0, 1 + len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (1 + len(_UpperCAmelCase							), 1 + len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )

				A_		:						Tuple	    =   self.rust_tokenizer_class.from_pretrained(
				    _UpperCAmelCase     ,     use_fast=_UpperCAmelCase     ,     add_prefix_space=_UpperCAmelCase     ,     trim_offsets=_UpperCAmelCase							)
				A_		:						str	    =   tokenizer_r(_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase							)
				self.assertEqual(encoding.offset_mapping[0]     ,     (0, 1 + len(_UpperCAmelCase							))							)
				self.assertEqual(
				    encoding.offset_mapping[1]     ,     (1 + len(_UpperCAmelCase							), 1 + len(_UpperCAmelCase							) + 1 + len(_UpperCAmelCase							))     ,     )









"""simple docstring"""
import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   get_activation("""swish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   get_activation("""silu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.SiLU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   get_activation("""mish"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.Mish							)

		self.assertEqual(act(torch.tensor(-200     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   get_activation("""gelu"""							)

		self.assertIsInstance(lowerCAmelCase_     ,     nn.GELU							)

		self.assertEqual(act(torch.tensor(-100     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertNotEqual(act(torch.tensor(-1     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(0     ,     dtype=torch.floataa							)							).item()     ,     0							)
		self.assertEqual(act(torch.tensor(20     ,     dtype=torch.floataa							)							).item()     ,     20							)

