
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      A__							(     __magic_name__						,   unittest.TestCase	):
				lowercase			    =  LongformerTokenizer
				lowercase			    =  True
				lowercase			    =  LongformerTokenizerFast
				lowercase			    =  True






				def 							_lowerCamelCase		(						self					:      str   ):






							'''simple docstring'''







							super().setUp()

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

							lowerCAmelCase__      :      List[str]         =		os.path.join(self.tmpdirname    ,     VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Optional[int]         =		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 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     **a					:      Optional[int]   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     **a					:      int   ):






							'''simple docstring'''







							kwargs.update(self.special_tokens_map   )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		'lower newer'
							lowerCAmelCase__      :      Union[str, Any]         =		'lower newer'
							return input_text, output_text






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.tokenizer_class(self.vocab_file    ,     self.merges_file    ,     **self.special_tokens_map   )
							lowerCAmelCase__      :      Dict         =		'lower newer'
							lowerCAmelCase__      :      List[Any]         =		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							lowerCAmelCase__      :      Optional[int]         =		tokenizer.tokenize(a   )  # , add_prefix_space=True)
							self.assertListEqual(a    ,     a   )

							lowerCAmelCase__      :      int         =		tokens + [tokenizer.unk_token]
							lowerCAmelCase__      :      Tuple         =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(a   )    ,     a   )






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		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 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'   )

							lowerCAmelCase__      :      Optional[Any]         =		tokenizer.encode('sequence builders'    ,     add_special_tokens=a   )
							lowerCAmelCase__      :      Union[str, Any]         =		tokenizer.encode('multi-sequence build'    ,     add_special_tokens=a   )

							lowerCAmelCase__      :      List[Any]         =		tokenizer.encode(
							    'sequence builders'    ,     add_special_tokens=a    ,     add_prefix_space=a   )
							lowerCAmelCase__      :      str         =		tokenizer.encode(
							    'sequence builders'    ,     'multi-sequence build'    ,     add_special_tokens=a    ,     add_prefix_space=a   )

							lowerCAmelCase__      :      Dict         =		tokenizer.build_inputs_with_special_tokens(a   )
							lowerCAmelCase__      :      Union[str, Any]         =		tokenizer.build_inputs_with_special_tokens(a    ,     a   )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.get_tokenizer()

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

							# Testing encoder arguments
							lowerCAmelCase__      :      Optional[Any]         =		tokenizer.encode(a    ,     add_special_tokens=a    ,     add_prefix_space=a   )
							lowerCAmelCase__      :      List[str]         =		tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertNotEqual(a    ,     a   )

							lowerCAmelCase__      :      Optional[Any]         =		tokenizer.encode(a    ,     add_special_tokens=a    ,     add_prefix_space=a   )
							lowerCAmelCase__      :      Any         =		tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertEqual(a    ,     a   )

							tokenizer.add_special_tokens({'bos_token': '<s>'}   )
							lowerCAmelCase__      :      Union[str, Any]         =		tokenizer.encode(a    ,     add_special_tokens=a   )
							lowerCAmelCase__      :      List[str]         =		tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
							self.assertNotEqual(a    ,     a   )

							# Testing spaces after special tokens
							lowerCAmelCase__      :      Tuple         =		'<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(a    ,     lstrip=a    ,     rstrip=a   )}   )  # mask token has a left space
							lowerCAmelCase__      :      Union[str, Any]         =		tokenizer.convert_tokens_to_ids(a   )

							lowerCAmelCase__      :      List[Any]         =		'Encode <mask> sequence'
							lowerCAmelCase__      :      Optional[Any]         =		'Encode <mask>sequence'

							lowerCAmelCase__      :      Optional[Any]         =		tokenizer.encode(a   )
							lowerCAmelCase__      :      Dict         =		encoded.index(a   )
							lowerCAmelCase__      :      Optional[Any]         =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertEqual(a    ,     a   )

							lowerCAmelCase__      :      Optional[int]         =		tokenizer.encode(a   )
							lowerCAmelCase__      :      List[str]         =		encoded.index(a   )
							lowerCAmelCase__      :      Union[str, Any]         =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertNotEqual(a    ,     a   )






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






							'''simple docstring'''







							pass






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      Dict         =		self.rust_tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      Optional[int]         =		self.tokenizer_class.from_pretrained(a    ,     **a   )
													lowerCAmelCase__      :      Tuple         =		'A, <mask> AllenNLP sentence.'
													lowerCAmelCase__      :      Optional[int]         =		tokenizer_r.encode_plus(a    ,     add_special_tokens=a    ,     return_token_type_ids=a   )
													lowerCAmelCase__      :      Tuple         =		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']   )    ,     )

													lowerCAmelCase__      :      List[str]         =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
													lowerCAmelCase__      :      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, 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 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							for trim_offsets, add_prefix_space in itertools.product([True, False]    ,     repeat=2   ):
										lowerCAmelCase__      :      List[str]         =		self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )

										lowerCAmelCase__      :      List[str]         =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
										lowerCAmelCase__      :      Optional[Any]         =		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 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''   ):
													lowerCAmelCase__      :      Any         =		'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
													lowerCAmelCase__      :      Tuple         =		f'''{text_of_1_token} {text_of_1_token}'''

													lowerCAmelCase__      :      List[str]         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      Any         =		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   ))    ,     )

													lowerCAmelCase__      :      Any         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      str         =		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   ))    ,     )

													lowerCAmelCase__      :      str         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      List[str]         =		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   ))    ,     )

													lowerCAmelCase__      :      Union[str, Any]         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      Tuple         =		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   ))    ,     )

													lowerCAmelCase__      :      Any         =		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)),
													# )

													lowerCAmelCase__      :      int         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      Union[str, Any]         =		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   ))    ,     )

													lowerCAmelCase__      :      Tuple         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      List[Any]         =		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   ))    ,     )

													lowerCAmelCase__      :      List[Any]         =		self.rust_tokenizer_class.from_pretrained(
													    a    ,     use_fast=a    ,     add_prefix_space=a    ,     trim_offsets=a   )
													lowerCAmelCase__      :      Optional[Any]         =		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   ))    ,     )

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )