


'''simple docstring'''
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__snake_case							   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

__snake_case							   =  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''


def     a   (    __a				,      __a				,      __a=8     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCamelCase__     :Union[str, Any]							      =				width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







class      lowercase			(							A__      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__()

							self.register_modules(
							    unet=UpperCamelCase_					,     scheduler=UpperCamelCase_					,     movq=UpperCamelCase_					,     )
							UpperCamelCase__     :str							      =				2 ** (len(self.movq.config.block_out_channels							) - 1)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if latents is None:
												UpperCamelCase__     :Dict							      =				randn_tensor(UpperCamelCase_					,     generator=UpperCamelCase_					,     device=UpperCamelCase_					,     dtype=UpperCamelCase_							)
							else:
												if latents.shape != shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''							)
												UpperCamelCase__     :Optional[Any]							      =				latents.to(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				latents * scheduler.init_noise_sigma
							return latents




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0							):

							'''simple docstring'''


							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('''Please install accelerate via `pip install accelerate`'''							)

							UpperCamelCase__     :Union[str, Any]							      =				torch.device(F'''cuda:{gpu_id}'''							)

							UpperCamelCase__     :Tuple							      =				[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(UpperCamelCase_					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0							):

							'''simple docstring'''


							if is_accelerate_available() and is_accelerate_version('''>='''					,     '''0.17.0.dev0'''							):
												from accelerate import cpu_offload_with_hook
							else:
												raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''							)

							UpperCamelCase__     :List[Any]							      =				torch.device(F'''cuda:{gpu_id}'''							)

							if self.device.type != "cpu":
												self.to('''cpu'''					,     silence_dtype_warnings=UpperCamelCase_							)
												torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							UpperCamelCase__     :Optional[Any]							      =				None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				cpu_offload_with_hook(UpperCamelCase_					,     UpperCamelCase_					,     prev_module_hook=UpperCamelCase_							)

							# We'll offload the last model manually.
							UpperCamelCase__     :List[str]							      =				hook




		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if not hasattr(self.unet					,     '''_hf_hook'''							):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(UpperCamelCase_					,     '''_hf_hook'''							)
												    and hasattr(module._hf_hook					,     '''execution_device'''							)
												    and module._hf_hook.execution_device is not None
												):
																	return torch.device(module._hf_hook.execution_device							)
							return self.device




		@torch.no_grad()
		@replace_example_docstring(UpperCamelCase_							)
		def __call__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = 512					,     UpperCamelCase_ = 512					,     UpperCamelCase_ = 100					,     UpperCamelCase_ = 4.0					,     UpperCamelCase_ = 1					,     UpperCamelCase_ = None					,     UpperCamelCase_ = None					,     UpperCamelCase_ = "pil"					,     UpperCamelCase_ = True					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				self._execution_device

							UpperCamelCase__     :str							      =				guidance_scale > 1.0

							if isinstance(UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :List[str]							      =				torch.cat(UpperCamelCase_					,     dim=0							)
							if isinstance(UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :str							      =				torch.cat(UpperCamelCase_					,     dim=0							)
							if isinstance(UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :Union[str, Any]							      =				torch.cat(UpperCamelCase_					,     dim=0							)

							UpperCamelCase__     :Union[str, Any]							      =				image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
												UpperCamelCase__     :Optional[int]							      =				image_embeds.repeat_interleave(UpperCamelCase_					,     dim=0							)
												UpperCamelCase__     :Any							      =				negative_image_embeds.repeat_interleave(UpperCamelCase_					,     dim=0							)
												UpperCamelCase__     :int							      =				hint.repeat_interleave(UpperCamelCase_					,     dim=0							)

							UpperCamelCase__     :Any							      =				torch.cat([negative_image_embeds, image_embeds]					,     dim=0							).to(dtype=self.unet.dtype					,     device=UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				torch.cat([hint, hint]					,     dim=0							).to(dtype=self.unet.dtype					,     device=UpperCamelCase_							)
							self.scheduler.set_timesteps(UpperCamelCase_					,     device=UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				self.scheduler.timesteps

							UpperCamelCase__     :Optional[int]							      =				self.movq.config.latent_channels

							UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				downscale_height_and_width(UpperCamelCase_					,     UpperCamelCase_					,     self.movq_scale_factor							)

							# create initial latent
							UpperCamelCase__     :List[Any]							      =				self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)					,     image_embeds.dtype					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     self.scheduler					,     )

							for i, t in enumerate(self.progress_bar(UpperCamelCase_							)							):
												# expand the latents if we are doing classifier free guidance
												UpperCamelCase__     :int							      =				torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

												UpperCamelCase__     :Union[str, Any]							      =				{'''image_embeds''': image_embeds, '''hint''': hint}
												UpperCamelCase__     :Tuple							      =				self.unet(
												    sample=UpperCamelCase_					,     timestep=UpperCamelCase_					,     encoder_hidden_states=UpperCamelCase_					,     added_cond_kwargs=UpperCamelCase_					,     return_dict=UpperCamelCase_					,     )[0]

												if do_classifier_free_guidance:
																	UpperCamelCase__      ,       UpperCamelCase__     :int							      =				noise_pred.split(latents.shape[1]					,     dim=1							)
																	UpperCamelCase__      ,       UpperCamelCase__     :int							      =				noise_pred.chunk(2							)
																	UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				variance_pred.chunk(2							)
																	UpperCamelCase__     :List[Any]							      =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCamelCase__     :List[Any]							      =				torch.cat([noise_pred, variance_pred_text]					,     dim=1							)

												if not (
												    hasattr(self.scheduler.config					,     '''variance_type'''							)
												    and self.scheduler.config.variance_type in ["learned", "learned_range"]
												):
																	UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				noise_pred.split(latents.shape[1]					,     dim=1							)

												# compute the previous noisy sample x_t -> x_t-1
												UpperCamelCase__     :Tuple							      =				self.scheduler.step(
												    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     generator=UpperCamelCase_					,     )[0]
							# post-processing
							UpperCamelCase__     :Tuple							      =				self.movq.decode(UpperCamelCase_					,     force_not_quantize=UpperCamelCase_							)['''sample''']

							if output_type not in ["pt", "np", "pil"]:
												raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''							)

							if output_type in ["np", "pil"]:
												UpperCamelCase__     :Any							      =				image * 0.5 + 0.5
												UpperCamelCase__     :Union[str, Any]							      =				image.clamp(0					,     1							)
												UpperCamelCase__     :Optional[Any]							      =				image.cpu().permute(0					,     2					,     3					,     1							).float().numpy()

							if output_type == "pil":
												UpperCamelCase__     :str							      =				self.numpy_to_pil(UpperCamelCase_							)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=UpperCamelCase_							)



'''simple docstring'''
import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

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

		"""simple docstring"""

		_a									=       RoCBertTokenizer
		_a									=       None
		_a									=       False
		_a									=       True
		_a									=       filter_non_english




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().setUp()

							UpperCamelCase__     :List[str]							      =				['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''你''', '''好''', '''是''', '''谁''', '''a''', '''b''', '''c''', '''d''']
							UpperCamelCase__     :Optional[Any]							      =				{}
							UpperCamelCase__     :str							      =				{}
							for i, value in enumerate(UpperCamelCase_							):
												UpperCamelCase__     :List[Any]							      =				i
												UpperCamelCase__     :List[str]							      =				i
							UpperCamelCase__     :Any							      =				os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES['''vocab_file''']							)
							UpperCamelCase__     :Optional[Any]							      =				os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES['''word_shape_file''']							)
							UpperCamelCase__     :List[str]							      =				os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES['''word_pronunciation_file''']							)
							with open(self.vocab_file					,     '''w'''					,     encoding='''utf-8'''							) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)
							with open(self.word_shape_file					,     '''w'''					,     encoding='''utf-8'''							) as word_shape_writer:
												json.dump(UpperCamelCase_					,     UpperCamelCase_					,     ensure_ascii=UpperCamelCase_							)
							with open(self.word_pronunciation_file					,     '''w'''					,     encoding='''utf-8'''							) as word_pronunciation_writer:
												json.dump(UpperCamelCase_					,     UpperCamelCase_					,     ensure_ascii=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.tokenizer_class(self.vocab_file					,     self.word_shape_file					,     self.word_pronunciation_file							)

							UpperCamelCase__     :Optional[int]							      =				tokenizer.tokenize('''你好[SEP]你是谁'''							)
							self.assertListEqual(UpperCamelCase_					,     ['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁''']							)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_							)					,     [5, 6, 2, 5, 7, 8]							)
							self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(UpperCamelCase_							)					,     [5, 6, 2, 5, 7, 8]							)
							self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(UpperCamelCase_							)					,     [5, 6, 2, 5, 7, 8]							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				RoCBertBasicTokenizer()

							self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''							)					,     ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''							)					,     ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']							)
							self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''							)					,     ['''hello''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_					,     strip_accents=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''							)					,     ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']							)
							self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''							)					,     ['''h\u00E9llo''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_					,     strip_accents=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''							)					,     ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']							)
							self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''							)					,     ['''hello''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''							)					,     ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']							)
							self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''							)					,     ['''hello''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''							)					,     ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_					,     strip_accents=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''							)					,     ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_					,     strip_accents=UpperCamelCase_							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''							)					,     ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				RoCBertBasicTokenizer(do_lower_case=UpperCamelCase_					,     never_split=['''[UNK]''']							)

							self.assertListEqual(
							    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''							)					,     ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

							UpperCamelCase__     :List[str]							      =				{}
							for i, token in enumerate(UpperCamelCase_							):
												UpperCamelCase__     :Tuple							      =				i
							UpperCamelCase__     :Any							      =				RoCBertWordpieceTokenizer(vocab=UpperCamelCase_					,     unk_token='''[UNK]'''							)

							self.assertListEqual(tokenizer.tokenize(''''''							)					,     []							)

							self.assertListEqual(tokenizer.tokenize('''unwanted running'''							)					,     ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']							)

							self.assertListEqual(tokenizer.tokenize('''unwantedX running'''							)					,     ['''[UNK]''', '''runn''', '''##ing''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.assertTrue(_is_whitespace(''' '''							)							)
							self.assertTrue(_is_whitespace('''\t'''							)							)
							self.assertTrue(_is_whitespace('''\r'''							)							)
							self.assertTrue(_is_whitespace('''\n'''							)							)
							self.assertTrue(_is_whitespace('''\u00A0'''							)							)

							self.assertFalse(_is_whitespace('''A'''							)							)
							self.assertFalse(_is_whitespace('''-'''							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.assertTrue(_is_control('''\u0005'''							)							)

							self.assertFalse(_is_control('''A'''							)							)
							self.assertFalse(_is_control(''' '''							)							)
							self.assertFalse(_is_control('''\t'''							)							)
							self.assertFalse(_is_control('''\r'''							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.assertTrue(_is_punctuation('''-'''							)							)
							self.assertTrue(_is_punctuation('''$'''							)							)
							self.assertTrue(_is_punctuation('''`'''							)							)
							self.assertTrue(_is_punctuation('''.'''							)							)

							self.assertFalse(_is_punctuation('''A'''							)							)
							self.assertFalse(_is_punctuation(''' '''							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				self.get_tokenizer()

							# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
							self.assertListEqual([tokenizer.tokenize(UpperCamelCase_							) for t in ['''Test''', '''\xad''', '''test''']]					,     [['''[UNK]'''], [], ['''[UNK]''']]							)

							if self.test_rust_tokenizer:
												UpperCamelCase__     :List[str]							      =				self.get_rust_tokenizer()
												self.assertListEqual(
												    [rust_tokenizer.tokenize(UpperCamelCase_							) for t in ['''Test''', '''\xad''', '''test''']]					,     [['''[UNK]'''], [], ['''[UNK]''']]							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																	UpperCamelCase__     :Optional[Any]							      =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)

																	UpperCamelCase__     :Optional[int]							      =				F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
																	UpperCamelCase__     :List[str]							      =				tokenizer_r.encode_plus(
																	    UpperCamelCase_					,     return_attention_mask=UpperCamelCase_					,     return_token_type_ids=UpperCamelCase_					,     return_offsets_mapping=UpperCamelCase_					,     add_special_tokens=UpperCamelCase_					,     )

																	UpperCamelCase__     :int							      =				tokenizer_r.do_lower_case if hasattr(UpperCamelCase_					,     '''do_lower_case'''							) else False
																	UpperCamelCase__     :str							      =				(
																	    [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), '''A'''),
																	        ((1, 2), ''','''),
																	        ((3, 5), '''na'''),
																	        ((5, 6), '''##ï'''),
																	        ((6, 8), '''##ve'''),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), '''Allen'''),
																	        ((21, 23), '''##NL'''),
																	        ((23, 24), '''##P'''),
																	        ((25, 33), '''sentence'''),
																	        ((33, 34), '''.'''),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	    if not do_lower_case
																	    else [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), '''a'''),
																	        ((1, 2), ''','''),
																	        ((3, 8), '''naive'''),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), '''allen'''),
																	        ((21, 23), '''##nl'''),
																	        ((23, 24), '''##p'''),
																	        ((25, 33), '''sentence'''),
																	        ((33, 34), '''.'''),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	)

																	self.assertEqual(
																	    [e[1] for e in expected_results]					,     tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']							)							)
																	self.assertEqual([e[0] for e in expected_results]					,     tokens['''offset_mapping''']							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				['''的''', '''人''', '''有''']
							UpperCamelCase__     :Tuple							      =				''''''.join(UpperCamelCase_							)
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																	UpperCamelCase__     :Union[str, Any]							      =				True
																	UpperCamelCase__     :str							      =				self.tokenizer_class.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)
																	UpperCamelCase__     :Any							      =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)

																	UpperCamelCase__     :Union[str, Any]							      =				tokenizer_p.encode(UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							)
																	UpperCamelCase__     :Optional[Any]							      =				tokenizer_r.encode(UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							)

																	UpperCamelCase__     :Any							      =				tokenizer_r.convert_ids_to_tokens(UpperCamelCase_							)
																	UpperCamelCase__     :str							      =				tokenizer_p.convert_ids_to_tokens(UpperCamelCase_							)

																	# it is expected that each Chinese character is not preceded by "##"
																	self.assertListEqual(UpperCamelCase_					,     UpperCamelCase_							)
																	self.assertListEqual(UpperCamelCase_					,     UpperCamelCase_							)

																	UpperCamelCase__     :Optional[int]							      =				False
																	UpperCamelCase__     :Union[str, Any]							      =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)
																	UpperCamelCase__     :Dict							      =				self.tokenizer_class.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)

																	UpperCamelCase__     :Optional[int]							      =				tokenizer_r.encode(UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							)
																	UpperCamelCase__     :List[Any]							      =				tokenizer_p.encode(UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							)

																	UpperCamelCase__     :Any							      =				tokenizer_r.convert_ids_to_tokens(UpperCamelCase_							)
																	UpperCamelCase__     :int							      =				tokenizer_p.convert_ids_to_tokens(UpperCamelCase_							)

																	# it is expected that only the first Chinese character is not preceded by "##".
																	UpperCamelCase__     :Optional[Any]							      =				[
																	    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(UpperCamelCase_							)
																	]
																	self.assertListEqual(UpperCamelCase_					,     UpperCamelCase_							)
																	self.assertListEqual(UpperCamelCase_					,     UpperCamelCase_							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.tokenizer_class(self.vocab_file					,     self.word_shape_file					,     self.word_pronunciation_file							)

							UpperCamelCase__     :Union[str, Any]							      =				tokenizer.encode('''你好'''					,     add_special_tokens=UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				tokenizer.encode('''你是谁'''					,     add_special_tokens=UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				tokenizer.build_inputs_with_special_tokens(UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				tokenizer.build_inputs_with_special_tokens(UpperCamelCase_					,     UpperCamelCase_							)

							assert encoded_sentence == [1] + text + [2]
							assert encoded_pair == [1] + text + [2] + text_a + [2]




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.get_tokenizers(do_lower_case=UpperCamelCase_							)
							for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
																	UpperCamelCase__     :Optional[int]							      =				'''你好，你是谁'''
																	UpperCamelCase__     :Optional[int]							      =				tokenizer.tokenize(UpperCamelCase_							)
																	UpperCamelCase__     :Union[str, Any]							      =				tokenizer.convert_tokens_to_ids(UpperCamelCase_							)
																	UpperCamelCase__     :Any							      =				tokenizer.convert_tokens_to_shape_ids(UpperCamelCase_							)
																	UpperCamelCase__     :int							      =				tokenizer.convert_tokens_to_pronunciation_ids(UpperCamelCase_							)
																	UpperCamelCase__     :Optional[int]							      =				tokenizer.prepare_for_model(
																	    UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							)

																	UpperCamelCase__     :Optional[int]							      =				tokenizer.encode_plus(UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							)

																	self.assertEqual(UpperCamelCase_					,     UpperCamelCase_							)