



"""simple docstring"""







# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class   UpperCamelCase	(     snake_case					,   snake_case					,   snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  StableDiffusionControlNetInpaintPipeline
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	SCREAMING_SNAKE_CASE_							:       str						       =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  frozenset({"control_image"}       )  # skip `image` and `mask` for now, only test for control_image
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  TEXT_TO_IMAGE_IMAGE_PARAMS






	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: Optional[int]     =   UNetaDConditionModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,sample_size=32				,in_channels=9				,out_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,cross_attention_dim=32				,)
			torch.manual_seed(0   )
			_lowercase				: str     =   ControlNetModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,in_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,cross_attention_dim=32				,conditioning_embedding_out_channels=(16, 32)				,)
			torch.manual_seed(0   )
			_lowercase				: Dict     =   DDIMScheduler(
			    beta_start=0.00085				,beta_end=0.012				,beta_schedule="""scaled_linear"""				,clip_sample=UpperCAmelCase_				,set_alpha_to_one=UpperCAmelCase_				,)
			torch.manual_seed(0   )
			_lowercase				: Dict     =   AutoencoderKL(
			    block_out_channels=[32, 64]				,in_channels=3				,out_channels=3				,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,latent_channels=4				,)
			torch.manual_seed(0   )
			_lowercase				: int     =   CLIPTextConfig(
			    bos_token_id=0				,eos_token_id=2				,hidden_size=32				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
			_lowercase				: Optional[int]     =   CLIPTextModel(UpperCAmelCase_   )
			_lowercase				: Optional[int]     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

			_lowercase				: Union[str, Any]     =   {
			    """unet""": unet,
			    """controlnet""": controlnet,
			    """scheduler""": scheduler,
			    """vae""": vae,
			    """text_encoder""": text_encoder,
			    """tokenizer""": tokenizer,
			    """safety_checker""": None,
			    """feature_extractor""": None,
			}
			return components






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=0   ):
			if str(UpperCAmelCase_   ).startswith("""mps"""   ):
					_lowercase				: Union[str, Any]     =   torch.manual_seed(UpperCAmelCase_   )
			else:
					_lowercase				: Tuple     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )

			_lowercase				: Dict     =   2
			_lowercase				: List[str]     =   randn_tensor(
			    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,generator=UpperCAmelCase_				,device=torch.device(UpperCAmelCase_   )				,)
			_lowercase				: Tuple     =   floats_tensor((1, 3, 32, 32)				,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
			_lowercase				: str     =   init_image.cpu().permute(0				,2				,3				,1   )[0]

			_lowercase				: List[str]     =   Image.fromarray(np.uinta(UpperCAmelCase_   )   ).convert("""RGB"""   ).resize((64, 64)   )
			_lowercase				: int     =   Image.fromarray(np.uinta(init_image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )

			_lowercase				: List[str]     =   {
			    """prompt""": """A painting of a squirrel eating a burger""",
			    """generator""": generator,
			    """num_inference_steps""": 2,
			    """guidance_scale""": 6.0,
			    """output_type""": """numpy""",
			    """image""": image,
			    """mask_image""": mask_image,
			    """control_image""": control_image,
			}

			return inputs






	def    lowerCamelCase__					(     self   ):
			return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3   )






	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()				,reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,)
	def    lowerCamelCase__					(     self   ):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )






	def    lowerCamelCase__					(     self   ):
			self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Any						       =  StableDiffusionControlNetInpaintPipeline
	SCREAMING_SNAKE_CASE_							:       Dict						       =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  frozenset([]       )






	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: Union[str, Any]     =   UNetaDConditionModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,sample_size=32				,in_channels=4				,out_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,cross_attention_dim=32				,)
			torch.manual_seed(0   )
			_lowercase				: Optional[int]     =   ControlNetModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,in_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,cross_attention_dim=32				,conditioning_embedding_out_channels=(16, 32)				,)
			torch.manual_seed(0   )
			_lowercase				: int     =   DDIMScheduler(
			    beta_start=0.00085				,beta_end=0.012				,beta_schedule="""scaled_linear"""				,clip_sample=UpperCAmelCase_				,set_alpha_to_one=UpperCAmelCase_				,)
			torch.manual_seed(0   )
			_lowercase				: Optional[int]     =   AutoencoderKL(
			    block_out_channels=[32, 64]				,in_channels=3				,out_channels=3				,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,latent_channels=4				,)
			torch.manual_seed(0   )
			_lowercase				: List[str]     =   CLIPTextConfig(
			    bos_token_id=0				,eos_token_id=2				,hidden_size=32				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
			_lowercase				: Tuple     =   CLIPTextModel(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

			_lowercase				: List[str]     =   {
			    """unet""": unet,
			    """controlnet""": controlnet,
			    """scheduler""": scheduler,
			    """vae""": vae,
			    """text_encoder""": text_encoder,
			    """tokenizer""": tokenizer,
			    """safety_checker""": None,
			    """feature_extractor""": None,
			}
			return components







class   UpperCamelCase	(     snake_case					,   snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Any						       =  StableDiffusionControlNetInpaintPipeline
	SCREAMING_SNAKE_CASE_							:       Optional[Any]						       =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	SCREAMING_SNAKE_CASE_							:       str						       =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			_lowercase				: Tuple     =   UNetaDConditionModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,sample_size=32				,in_channels=9				,out_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,cross_attention_dim=32				,)
			torch.manual_seed(0   )

			def init_weights(UpperCAmelCase_   ):
					if isinstance(UpperCAmelCase_				,torch.nn.Convad   ):
							torch.nn.init.normal(m.weight   )
							m.bias.data.fill_(1.0   )

			_lowercase				: str     =   ControlNetModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,in_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,cross_attention_dim=32				,conditioning_embedding_out_channels=(16, 32)				,)
			controlneta.controlnet_down_blocks.apply(UpperCAmelCase_   )

			torch.manual_seed(0   )
			_lowercase				: Dict     =   ControlNetModel(
			    block_out_channels=(32, 64)				,layers_per_block=2				,in_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,cross_attention_dim=32				,conditioning_embedding_out_channels=(16, 32)				,)
			controlneta.controlnet_down_blocks.apply(UpperCAmelCase_   )

			torch.manual_seed(0   )
			_lowercase				: Optional[Any]     =   DDIMScheduler(
			    beta_start=0.00085				,beta_end=0.012				,beta_schedule="""scaled_linear"""				,clip_sample=UpperCAmelCase_				,set_alpha_to_one=UpperCAmelCase_				,)
			torch.manual_seed(0   )
			_lowercase				: Dict     =   AutoencoderKL(
			    block_out_channels=[32, 64]				,in_channels=3				,out_channels=3				,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,latent_channels=4				,)
			torch.manual_seed(0   )
			_lowercase				: Any     =   CLIPTextConfig(
			    bos_token_id=0				,eos_token_id=2				,hidden_size=32				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
			_lowercase				: Union[str, Any]     =   CLIPTextModel(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

			_lowercase				: Optional[int]     =   MultiControlNetModel([controlneta, controlneta]   )

			_lowercase				: int     =   {
			    """unet""": unet,
			    """controlnet""": controlnet,
			    """scheduler""": scheduler,
			    """vae""": vae,
			    """text_encoder""": text_encoder,
			    """tokenizer""": tokenizer,
			    """safety_checker""": None,
			    """feature_extractor""": None,
			}
			return components






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=0   ):
			if str(UpperCAmelCase_   ).startswith("""mps"""   ):
					_lowercase				: Tuple     =   torch.manual_seed(UpperCAmelCase_   )
			else:
					_lowercase				: Union[str, Any]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )

			_lowercase				: Tuple     =   2

			_lowercase				: Union[str, Any]     =   [
			    randn_tensor(
			        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,generator=UpperCAmelCase_				,device=torch.device(UpperCAmelCase_   )				,),
			    randn_tensor(
			        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,generator=UpperCAmelCase_				,device=torch.device(UpperCAmelCase_   )				,),
			]
			_lowercase				: int     =   floats_tensor((1, 3, 32, 32)				,rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
			_lowercase				: Optional[int]     =   init_image.cpu().permute(0				,2				,3				,1   )[0]

			_lowercase				: List[str]     =   Image.fromarray(np.uinta(UpperCAmelCase_   )   ).convert("""RGB"""   ).resize((64, 64)   )
			_lowercase				: Optional[Any]     =   Image.fromarray(np.uinta(init_image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )

			_lowercase				: Tuple     =   {
			    """prompt""": """A painting of a squirrel eating a burger""",
			    """generator""": generator,
			    """num_inference_steps""": 2,
			    """guidance_scale""": 6.0,
			    """output_type""": """numpy""",
			    """image""": image,
			    """mask_image""": mask_image,
			    """control_image""": control_image,
			}

			return inputs






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   self.get_dummy_components()
			_lowercase				: List[str]     =   self.pipeline_class(**UpperCAmelCase_   )
			pipe.to(UpperCAmelCase_   )

			_lowercase				: List[Any]     =   10.0
			_lowercase				: Optional[Any]     =   4

			_lowercase				: Dict     =   self.get_dummy_inputs(UpperCAmelCase_   )
			_lowercase				: Dict     =   steps
			_lowercase				: Union[str, Any]     =   scale
			_lowercase				: Tuple     =   pipe(**UpperCAmelCase_   )[0]

			_lowercase				: str     =   self.get_dummy_inputs(UpperCAmelCase_   )
			_lowercase				: int     =   steps
			_lowercase				: Any     =   scale
			_lowercase				: Union[str, Any]     =   pipe(**UpperCAmelCase_				,control_guidance_start=0.1				,control_guidance_end=0.2   )[0]

			_lowercase				: str     =   self.get_dummy_inputs(UpperCAmelCase_   )
			_lowercase				: int     =   steps
			_lowercase				: str     =   scale
			_lowercase				: Optional[Any]     =   pipe(**UpperCAmelCase_				,control_guidance_start=[0.1, 0.3]				,control_guidance_end=[0.2, 0.7]   )[0]

			_lowercase				: Optional[int]     =   self.get_dummy_inputs(UpperCAmelCase_   )
			_lowercase				: Dict     =   steps
			_lowercase				: List[str]     =   scale
			_lowercase				: str     =   pipe(**UpperCAmelCase_				,control_guidance_start=0.4				,control_guidance_end=[0.5, 0.8]   )[0]

			# make sure that all outputs are different
			assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3
			assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3
			assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3






	def    lowerCamelCase__					(     self   ):
			return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3   )






	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()				,reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,)
	def    lowerCamelCase__					(     self   ):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )






	def    lowerCamelCase__					(     self   ):
			self._test_inference_batch_single_identical(expected_max_diff=2E-3   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   self.get_dummy_components()
			_lowercase				: Union[str, Any]     =   self.pipeline_class(**UpperCAmelCase_   )
			pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
			with tempfile.TemporaryDirectory() as tmpdir:
					try:
							# save_pretrained is not implemented for Multi-ControlNet
							pipe.save_pretrained(UpperCAmelCase_   )
					except NotImplementedError:
							pass







@slow
@require_torch_gpu
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""   )

			_lowercase				: Any     =   StableDiffusionControlNetInpaintPipeline.from_pretrained(
			    """runwayml/stable-diffusion-inpainting"""				,safety_checker=UpperCAmelCase_				,controlnet=UpperCAmelCase_   )
			pipe.enable_model_cpu_offload()
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: str     =   torch.Generator(device="""cpu"""   ).manual_seed(0   )
			_lowercase				: List[str]     =   load_image(
			    """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""   ).resize((5_12, 5_12)   )

			_lowercase				: Optional[Any]     =   load_image(
			    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
			    """/stable_diffusion_inpaint/input_bench_mask.png"""   ).resize((5_12, 5_12)   )

			_lowercase				: List[Any]     =   """pitch black hole"""

			_lowercase				: List[Any]     =   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""   ).resize((5_12, 5_12)   )

			_lowercase				: Any     =   pipe(
			    UpperCAmelCase_				,image=UpperCAmelCase_				,mask_image=UpperCAmelCase_				,control_image=UpperCAmelCase_				,generator=UpperCAmelCase_				,output_type="""np"""				,num_inference_steps=3				,)

			_lowercase				: Optional[int]     =   output.images[0]

			assert image.shape == (5_12, 5_12, 3)

			_lowercase				: Any     =   load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""   )

			assert np.abs(expected_image - image   ).max() < 9E-2






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""   )

			_lowercase				: Tuple     =   StableDiffusionControlNetInpaintPipeline.from_pretrained(
			    """runwayml/stable-diffusion-v1-5"""				,safety_checker=UpperCAmelCase_				,controlnet=UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   DDIMScheduler.from_config(pipe.scheduler.config   )
			pipe.enable_model_cpu_offload()
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: Any     =   torch.Generator(device="""cpu"""   ).manual_seed(33   )

			_lowercase				: List[str]     =   load_image(
			    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""   )
			_lowercase				: List[Any]     =   init_image.resize((5_12, 5_12)   )

			_lowercase				: Tuple     =   load_image(
			    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""   )
			_lowercase				: Any     =   mask_image.resize((5_12, 5_12)   )

			_lowercase				: Tuple     =   """a handsome man with ray-ban sunglasses"""

			def make_inpaint_condition(UpperCAmelCase_				,UpperCAmelCase_   ):
					_lowercase				: Tuple     =   np.array(image.convert("""RGB"""   )   ).astype(np.floataa   ) / 255.0
					_lowercase				: Any     =   np.array(image_mask.convert("""L"""   )   ).astype(np.floataa   ) / 255.0

					assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
					_lowercase				: Optional[Any]     =   -1.0  # set as masked pixel
					_lowercase				: Dict     =   np.expand_dims(UpperCAmelCase_				,0   ).transpose(0				,3				,1				,2   )
					_lowercase				: int     =   torch.from_numpy(UpperCAmelCase_   )
					return image

			_lowercase				: str     =   make_inpaint_condition(UpperCAmelCase_				,UpperCAmelCase_   )

			_lowercase				: Union[str, Any]     =   pipe(
			    UpperCAmelCase_				,image=UpperCAmelCase_				,mask_image=UpperCAmelCase_				,control_image=UpperCAmelCase_				,guidance_scale=9.0				,eta=1.0				,generator=UpperCAmelCase_				,num_inference_steps=20				,output_type="""np"""				,)
			_lowercase				: Any     =   output.images[0]

			assert image.shape == (5_12, 5_12, 3)

			_lowercase				: List[str]     =   load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""   )

			assert np.abs(expected_image - image   ).max() < 9E-2








'''simple docstring'''

import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A					:     Optional[Any]      =logging.get_logger(__name__)

_A					:     Optional[Any]      ={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_A					:     str      ={
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

_A					:     List[Any]      ={
    '''microsoft/biogpt''': 1_0_2_4,
}







def     __UpperCamelCase					(				_lowercase       )			->      Dict:
	_lowercase       :							List[str]  	=		set()
	_lowercase       :							str  	=		word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)       )
		_lowercase       :							str  	=		char
	return pairs






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    VOCAB_FILES_NAMES
					A_         =    PRETRAINED_VOCAB_FILES_MAP
					A_         =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A_         =    ["""input_ids""", """attention_mask"""]




					def __init__(	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						List[str]  ,	UpperCamelCase_					:						List[Any]="<unk>"  ,	UpperCamelCase_					:						str="<s>"  ,	UpperCamelCase_					:						str="</s>"  ,	UpperCamelCase_					:						int="</s>"  ,	UpperCamelCase_					:						Tuple="<pad>"  ,	**UpperCamelCase_					:						Any  ,	)	->							Tuple:



						'''simple docstring'''





						super().__init__(
						    bos_token=UpperCamelCase_  ,	eos_token=UpperCamelCase_  ,	sep_token=UpperCamelCase_  ,	unk_token=UpperCamelCase_  ,	pad_token=UpperCamelCase_  ,	**UpperCamelCase_  ,	)

						try:
							import sacremoses
						except ImportError:
							raise ImportError(
							    'You need to install sacremoses to use BioGptTokenizer. '
							    'See https://pypi.org/project/sacremoses/ for installation.'       )

						_lowercase       :							Dict  	=		'en'
						_lowercase       :							Any  	=		sacremoses
						# cache of sm.MosesTokenizer instance
						_lowercase       :							Union[str, Any]  	=		{}
						_lowercase       :							List[str]  	=		{}
						with open(UpperCamelCase_  ,	encoding='utf-8'       ) as vocab_handle:
							_lowercase       :							Optional[int]  	=		json.load(UpperCamelCase_       )
						_lowercase       :							str  	=		{v: k for k, v in self.encoder.items()}
						with open(UpperCamelCase_  ,	encoding='utf-8'       ) as merges_handle:
							_lowercase       :							Tuple  	=		merges_handle.read().split('\n'       )[:-1]
						_lowercase       :							List[str]  	=		[tuple(merge.split()[:2]       ) for merge in merges]
						_lowercase       :							str  	=		dict(zip(UpperCamelCase_  ,	range(len(UpperCamelCase_       )       )       )       )
						_lowercase       :							Union[str, Any]  	=		{}




					@property
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						return len(self.encoder       )




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						return dict(self.encoder  ,	**self.added_tokens_encoder       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						int       )	->							Optional[Any]:



						'''simple docstring'''





						if lang not in self.cache_moses_tokenizer:
							_lowercase       :							Union[str, Any]  	=		self.sm.MosesTokenizer(lang=UpperCamelCase_       )
							_lowercase       :							int  	=		moses_tokenizer
						return self.cache_moses_tokenizer[lang].tokenize(
						    UpperCamelCase_  ,	aggressive_dash_splits=UpperCamelCase_  ,	return_str=UpperCamelCase_  ,	escape=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						str       )	->							List[str]:



						'''simple docstring'''





						if lang not in self.cache_moses_detokenizer:
							_lowercase       :							str  	=		self.sm.MosesDetokenizer(lang=UpperCamelCase_       )
							_lowercase       :							List[Any]  	=		moses_detokenizer
						return self.cache_moses_detokenizer[lang].detokenize(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]       )	->							Optional[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		tuple(token[:-1]       ) + (token[-1] + '</w>',)
						if token in self.cache:
							return self.cache[token]
						_lowercase       :							Dict  	=		get_pairs(UpperCamelCase_       )

						if not pairs:
							return token + "</w>"

						while True:
							_lowercase       :							str  	=		min(UpperCamelCase_  ,	key=lambda UpperCamelCase_       : self.bpe_ranks.get(UpperCamelCase_  ,	float('inf'       )       )       )
							if bigram not in self.bpe_ranks:
								break
							_lowercase	,     _lowercase       :							int  	=		bigram
							_lowercase       :							Any  	=		[]
							_lowercase       :							str  	=		0
							while i < len(UpperCamelCase_       ):
								try:
									_lowercase       :							Union[str, Any]  	=		word.index(UpperCamelCase_  ,	UpperCamelCase_       )
								except ValueError:
									new_word.extend(word[i:]       )
									break
								else:
									new_word.extend(word[i:j]       )
									_lowercase       :							str  	=		j

								if word[i] == first and i < len(UpperCamelCase_       ) - 1 and word[i + 1] == second:
									new_word.append(first + second       )
									i += 2
								else:
									new_word.append(word[i]       )
									i += 1
							_lowercase       :							Optional[int]  	=		tuple(UpperCamelCase_       )
							_lowercase       :							Tuple  	=		new_word
							if len(UpperCamelCase_       ) == 1:
								break
							else:
								_lowercase       :							List[str]  	=		get_pairs(UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		' '.join(UpperCamelCase_       )
						if word == "\n  </w>":
							_lowercase       :							Union[str, Any]  	=		'\n</w>'
						_lowercase       :							List[Any]  	=		word
						return word




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						List[str]=False       )	->							int:



						'''simple docstring'''





						if bypass_tokenizer:
							_lowercase       :							Optional[Any]  	=		text.split()
						else:
							_lowercase       :							List[str]  	=		self.moses_tokenize(UpperCamelCase_  ,	self.lang       )

						_lowercase       :							int  	=		[]
						for token in text:
							if token:
								split_tokens.extend(list(self.bpe(UpperCamelCase_       ).split(' '       )       )       )

						return split_tokens




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							int:



						'''simple docstring'''





						return self.encoder.get(UpperCamelCase_  ,	self.encoder.get(self.unk_token       )       )




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Tuple:



						'''simple docstring'''





						return self.decoder.get(UpperCamelCase_  ,	self.unk_token       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[t.replace(' '  ,	''       ).replace('</w>'  ,	' '       ) for t in tokens]
						_lowercase       :							List[str]  	=		''.join(UpperCamelCase_       ).split()
						# detokenize
						_lowercase       :							Dict  	=		self.moses_detokenize(UpperCamelCase_  ,	self.lang       )
						return text




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
						_lowercase       :							List[Any]  	=		[self.sep_token_id]
						return sep + token_ids_a + sep + token_ids_a




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None  ,	UpperCamelCase_					:						bool = False       )	->							List[int]:



						'''simple docstring'''





						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=UpperCamelCase_  ,	token_ids_a=UpperCamelCase_  ,	already_has_special_tokens=UpperCamelCase_       )
						# no bos used in fairseq
						if token_ids_a is not None:
							return [1] + ([0] * len(UpperCamelCase_       )) + [1] + ([0] * len(UpperCamelCase_       ))
						return [1] + ([0] * len(UpperCamelCase_       ))




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[self.sep_token_id]

						# no bos used in fairseq
						if token_ids_a is None:
							return len(token_ids_a + sep       ) * [0]
						return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]




					def     __UpperCAmelCase  (	self					:						Tuple  ,	UpperCamelCase_					:						str  ,	UpperCamelCase_					:						Optional[str] = None       )	->							Tuple[str]:



						'''simple docstring'''





						if not os.path.isdir(UpperCamelCase_       ):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
							return
						_lowercase       :							Any  	=		os.path.join(
						    UpperCamelCase_  ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
						_lowercase       :							Union[str, Any]  	=		os.path.join(
						    UpperCamelCase_  ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

						with open(UpperCamelCase_  ,	'w'  ,	encoding='utf-8'       ) as f:
							f.write(json.dumps(self.encoder  ,	indent=2  ,	sort_keys=UpperCamelCase_  ,	ensure_ascii=UpperCamelCase_       ) + '\n'       )

						_lowercase       :							List[str]  	=		0
						with open(UpperCamelCase_  ,	'w'  ,	encoding='utf-8'       ) as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,	key=lambda UpperCamelCase_       : kv[1]       ):
								if index != token_index:
									logger.warning(
									    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    ' Please check that the tokenizer is not corrupted!'       )
									_lowercase       :							Tuple  	=		token_index
								writer.write(' '.join(UpperCamelCase_       ) + '\n'       )
								index += 1

						return vocab_file, merge_file




					def __getstate__(	self					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.__dict__.copy()
						_lowercase       :							Dict  	=		None
						return state




					def __setstate__(	self					:						List[str]  ,	UpperCamelCase_					:						Dict       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		d

						try:
							import sacremoses
						except ImportError:
							raise ImportError(
							    'You need to install sacremoses to use XLMTokenizer. '
							    'See https://pypi.org/project/sacremoses/ for installation.'       )

						_lowercase       :							str  	=		sacremoses
