






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= LongformerTokenizer
				lowercase__       							= True
				lowercase__       							= LongformerTokenizerFast
				lowercase__       							= True




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__     				=    [
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								lowercase__     				=    dict(zip(A__,						range(len(A__						)						)						)						)
								lowercase__     				=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								lowercase__     				=    {"""unk_token""": """<unk>"""}

								lowercase__     				=    os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''vocab_file''']						)
								lowercase__     				=    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(A__						) + '''\n'''						)
								with open(self.merges_file,						'''w''',						encoding='''utf-8'''						) as fp:
												fp.write('''\n'''.join(A__						)						)




				def        lowercase__     (  self		:					Optional[int],						**lowerCamelCase		:					int						):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map						)
								return self.tokenizer_class.from_pretrained(self.tmpdirname,						**A__						)




				def        lowercase__     (  self		:					Union[str, Any],						**lowerCamelCase		:					Any						):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map						)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,						**A__						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str						):

								'''simple docstring'''
								lowercase__     				=    """lower newer"""
								lowercase__     				=    """lower newer"""
								return input_text, output_text




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class(self.vocab_file,						self.merges_file,						**self.special_tokens_map						)
								lowercase__     				=    """lower newer"""
								lowercase__     				=    ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								lowercase__     				=    tokenizer.tokenize(A__						)  # , add_prefix_space=True)
								self.assertListEqual(A__,						A__						)

								lowercase__     				=    tokens + [tokenizer.unk_token]
								lowercase__     				=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A__						),						A__						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('''Hello world!''',						add_special_tokens=A__						),						[0, 31_414, 232, 328, 2]						)
								self.assertListEqual(
								    tokenizer.encode('''Hello world! cécé herlolip 418''',						add_special_tokens=A__						),						[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2],						)




				@slow
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''						)

								lowercase__     				=    tokenizer.encode('''sequence builders''',						add_special_tokens=A__						)
								lowercase__     				=    tokenizer.encode('''multi-sequence build''',						add_special_tokens=A__						)

								lowercase__     				=    tokenizer.encode(
								    '''sequence builders''',						add_special_tokens=A__,						add_prefix_space=A__						)
								lowercase__     				=    tokenizer.encode(
								    '''sequence builders''',						'''multi-sequence build''',						add_special_tokens=A__,						add_prefix_space=A__						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(A__						)
								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(A__,						A__						)

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()

								lowercase__     				=    """Encode this sequence."""
								lowercase__     				=    tokenizer.byte_encoder[""" """.encode('''utf-8'''						)[0]]

								# Testing encoder arguments
								lowercase__     				=    tokenizer.encode(A__,						add_special_tokens=A__,						add_prefix_space=A__						)
								lowercase__     				=    tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
								self.assertNotEqual(A__,						A__						)

								lowercase__     				=    tokenizer.encode(A__,						add_special_tokens=A__,						add_prefix_space=A__						)
								lowercase__     				=    tokenizer.convert_ids_to_tokens(encoded[0]						)[0]
								self.assertEqual(A__,						A__						)

								tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}						)
								lowercase__     				=    tokenizer.encode(A__,						add_special_tokens=A__						)
								lowercase__     				=    tokenizer.convert_ids_to_tokens(encoded[1]						)[0]
								self.assertNotEqual(A__,						A__						)

								# Testing spaces after special tokens
								lowercase__     				=    """<mask>"""
								tokenizer.add_special_tokens(
								    {'''mask_token''': AddedToken(A__,						lstrip=A__,						rstrip=A__						)}						)  # mask token has a left space
								lowercase__     				=    tokenizer.convert_tokens_to_ids(A__						)

								lowercase__     				=    """Encode <mask> sequence"""
								lowercase__     				=    """Encode <mask>sequence"""

								lowercase__     				=    tokenizer.encode(A__						)
								lowercase__     				=    encoded.index(A__						)
								lowercase__     				=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
								self.assertEqual(A__,						A__						)

								lowercase__     				=    tokenizer.encode(A__						)
								lowercase__     				=    encoded.index(A__						)
								lowercase__     				=    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]						)[0]
								self.assertNotEqual(A__,						A__						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(A__,						**A__						)
																lowercase__     				=    self.tokenizer_class.from_pretrained(A__,						**A__						)
																lowercase__     				=    """A, <mask> AllenNLP sentence."""
																lowercase__     				=    tokenizer_r.encode_plus(A__,						add_special_tokens=A__,						return_token_type_ids=A__						)
																lowercase__     				=    tokenizer_p.encode_plus(A__,						add_special_tokens=A__,						return_token_type_ids=A__						)

																# 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''']						),						)

																lowercase__     				=    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']						)
																lowercase__     				=    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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)
																self.assertSequenceEqual(tokens_r['''input_ids'''],						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]						)

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




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								for trim_offsets, add_prefix_space in itertools.product([True, False],						repeat=2						):
												lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
												    self.tmpdirname,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)

												lowercase__     				=    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()						)
												lowercase__     				=    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()						)

												self.assertEqual(pre_tokenizer_state['''add_prefix_space'''],						A__						)

												self.assertEqual(post_processor_state['''add_prefix_space'''],						A__						)
												self.assertEqual(post_processor_state['''trim_offsets'''],						A__						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								# 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})"""						):
																lowercase__     				=    """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																lowercase__     				=    F"""{text_of_1_token} {text_of_1_token}"""

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(0, len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(len(A__						) + 1, len(A__						) + 1 + len(A__						)),						)

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(0, len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(len(A__						) + 1, len(A__						) + 1 + len(A__						)),						)

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(0, len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(len(A__						), len(A__						) + 1 + len(A__						)),						)

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(0, len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(len(A__						), len(A__						) + 1 + len(A__						)),						)

																lowercase__     				=    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)),
																# )

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(1, 1 + len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(1 + len(A__						) + 1, 1 + len(A__						) + 1 + len(A__						)),						)

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(0, 1 + len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(1 + len(A__						), 1 + len(A__						) + 1 + len(A__						)),						)

																lowercase__     				=    self.rust_tokenizer_class.from_pretrained(
																    A__,						use_fast=A__,						add_prefix_space=A__,						trim_offsets=A__						)
																lowercase__     				=    tokenizer_r(A__,						return_offsets_mapping=A__,						add_special_tokens=A__						)
																self.assertEqual(encoding.offset_mapping[0],						(0, 1 + len(A__						))						)
																self.assertEqual(
																    encoding.offset_mapping[1],						(1 + len(A__						), 1 + len(A__						) + 1 + len(A__						)),						)












# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




