


import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
				import onnxruntime as ort




class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		int										=						'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


	def  A(    self				,     lowercase__=0):
							__UpperCAmelCase			:							str	    =	np.random.RandomState(lowercase__)
							__UpperCAmelCase			:							Dict	    =	{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Tuple	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							int	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							Tuple	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_2_8, 1_2_8, 3)
							__UpperCAmelCase			:							Optional[Any]	    =	np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							__UpperCAmelCase			:							Optional[int]	    =	PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=lowercase__)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							int	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							Optional[int]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_2_8, 1_2_8, 3)
							__UpperCAmelCase			:							List[str]	    =	np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							__UpperCAmelCase			:							List[Any]	    =	LMSDiscreteScheduler.from_config(pipe.scheduler.config)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Union[str, Any]	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							Any	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_2_8, 1_2_8, 3)
							__UpperCAmelCase			:							Union[str, Any]	    =	np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							__UpperCAmelCase			:							Optional[Any]	    =	EulerDiscreteScheduler.from_config(pipe.scheduler.config)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Optional[Any]	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							str	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_2_8, 1_2_8, 3)
							__UpperCAmelCase			:							int	    =	np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							__UpperCAmelCase			:							List[str]	    =	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Dict	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Optional[int]	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							Optional[Any]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_2_8, 1_2_8, 3)
							__UpperCAmelCase			:							Dict	    =	np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							__UpperCAmelCase			:							List[Any]	    =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Optional[int]	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Optional[Any]	    =	pipe(**lowercase__).images
							__UpperCAmelCase			:							List[str]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 1_2_8, 1_2_8, 3)
							__UpperCAmelCase			:							Dict	    =	np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Any	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							List[Any]	    =	3 * [inputs['''prompt''']]

							# forward
							__UpperCAmelCase			:							Dict	    =	pipe(**lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	output.images[0, -3:, -3:, -1]

							__UpperCAmelCase			:							Union[str, Any]	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Dict	    =	3 * [inputs.pop('''prompt''')]

							__UpperCAmelCase			:							Tuple	    =	pipe.tokenizer(
							    lowercase__				,     padding='''max_length'''				,     max_length=pipe.tokenizer.model_max_length				,     truncation=lowercase__				,     return_tensors='''np'''				,     )
							__UpperCAmelCase			:							Optional[Any]	    =	text_inputs['''input_ids''']

							__UpperCAmelCase			:							Union[str, Any]	    =	pipe.text_encoder(input_ids=text_inputs.astype(np.intaa))[0]

							__UpperCAmelCase			:							Union[str, Any]	    =	prompt_embeds

							# forward
							__UpperCAmelCase			:							Union[str, Any]	    =	pipe(**lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	output.images[0, -3:, -3:, -1]

							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider='''CPUExecutionProvider''')
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							int	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Any	    =	3 * ['''this is a negative prompt''']
							__UpperCAmelCase			:							Optional[int]	    =	negative_prompt
							__UpperCAmelCase			:							List[Any]	    =	3 * [inputs['''prompt''']]

							# forward
							__UpperCAmelCase			:							Any	    =	pipe(**lowercase__)
							__UpperCAmelCase			:							str	    =	output.images[0, -3:, -3:, -1]

							__UpperCAmelCase			:							Optional[int]	    =	self.get_dummy_inputs()
							__UpperCAmelCase			:							Any	    =	3 * [inputs.pop('''prompt''')]

							__UpperCAmelCase			:							Optional[int]	    =	[]
							for p in [prompt, negative_prompt]:
													__UpperCAmelCase			:							str	    =	pipe.tokenizer(
													    lowercase__				,     padding='''max_length'''				,     max_length=pipe.tokenizer.model_max_length				,     truncation=lowercase__				,     return_tensors='''np'''				,     )
													__UpperCAmelCase			:							List[Any]	    =	text_inputs['''input_ids''']

													embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa))[0])

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	embeds

							# forward
							__UpperCAmelCase			:							Union[str, Any]	    =	pipe(**lowercase__)
							__UpperCAmelCase			:							str	    =	output.images[0, -3:, -3:, -1]

							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4






@nightly
@require_onnxruntime
@require_torch_gpu
class 							lowerCamelCase   (  unittest.TestCase ):


	@property
	def  A(    self):
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


	@property
	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	ort.SessionOptions()
							__UpperCAmelCase			:							List[Any]	    =	False
							return options


	def  A(    self):
							# using the PNDM scheduler by default
							__UpperCAmelCase			:							Tuple	    =	OnnxStableDiffusionPipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4'''				,     revision='''onnx'''				,     safety_checker=lowercase__				,     feature_extractor=lowercase__				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							int	    =	'''A painting of a squirrel eating a burger'''
							np.random.seed(0)
							__UpperCAmelCase			:							Dict	    =	sd_pipe([prompt]				,     guidance_scale=6.0				,     num_inference_steps=1_0				,     output_type='''np''')
							__UpperCAmelCase			:							Dict	    =	output.images

							__UpperCAmelCase			:							str	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCAmelCase			:							List[str]	    =	np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	DDIMScheduler.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''				,     subfolder='''scheduler'''				,     revision='''onnx''')
							__UpperCAmelCase			:							Dict	    =	OnnxStableDiffusionPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     scheduler=lowercase__				,     safety_checker=lowercase__				,     feature_extractor=lowercase__				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Any	    =	'''open neural network exchange'''
							__UpperCAmelCase			:							Any	    =	np.random.RandomState(0)
							__UpperCAmelCase			:							List[Any]	    =	sd_pipe([prompt]				,     guidance_scale=7.5				,     num_inference_steps=1_0				,     generator=lowercase__				,     output_type='''np''')
							__UpperCAmelCase			:							List[str]	    =	output.images
							__UpperCAmelCase			:							Optional[int]	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCAmelCase			:							int	    =	np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	LMSDiscreteScheduler.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''				,     subfolder='''scheduler'''				,     revision='''onnx''')
							__UpperCAmelCase			:							Union[str, Any]	    =	OnnxStableDiffusionPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     scheduler=lowercase__				,     safety_checker=lowercase__				,     feature_extractor=lowercase__				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
							sd_pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Optional[Any]	    =	'''open neural network exchange'''
							__UpperCAmelCase			:							Optional[int]	    =	np.random.RandomState(0)
							__UpperCAmelCase			:							Dict	    =	sd_pipe([prompt]				,     guidance_scale=7.5				,     num_inference_steps=1_0				,     generator=lowercase__				,     output_type='''np''')
							__UpperCAmelCase			:							Union[str, Any]	    =	output.images
							__UpperCAmelCase			:							str	    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							__UpperCAmelCase			:							Optional[int]	    =	np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1])

							assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	0

							def test_callback_fn(lowercase__				,     lowercase__				,     lowercase__) -> None:
													__UpperCAmelCase			:							Optional[Any]	    =	True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 0:
																			assert latents.shape == (1, 4, 6_4, 6_4)
																			__UpperCAmelCase			:							Union[str, Any]	    =	latents[0, -3:, -3:, -1]
																			__UpperCAmelCase			:							Optional[Any]	    =	np.array(
																			    [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7])

																			assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
													elif step == 5:
																			assert latents.shape == (1, 4, 6_4, 6_4)
																			__UpperCAmelCase			:							Union[str, Any]	    =	latents[0, -3:, -3:, -1]
																			__UpperCAmelCase			:							Dict	    =	np.array(
																			    [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5])

																			assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

							__UpperCAmelCase			:							str	    =	False

							__UpperCAmelCase			:							List[str]	    =	OnnxStableDiffusionPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     safety_checker=lowercase__				,     feature_extractor=lowercase__				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
							pipe.set_progress_bar_config(disable=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	'''Andromeda galaxy in a bottle'''

							__UpperCAmelCase			:							List[Any]	    =	np.random.RandomState(0)
							pipe(
							    prompt=lowercase__				,     num_inference_steps=5				,     guidance_scale=7.5				,     generator=lowercase__				,     callback=lowercase__				,     callback_steps=1				,     )
							assert test_callback_fn.has_been_called
							assert number_of_steps == 6


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	OnnxStableDiffusionPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''				,     revision='''onnx'''				,     safety_checker=lowercase__				,     feature_extractor=lowercase__				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
							assert isinstance(lowercase__				,     lowercase__)
							assert pipe.safety_checker is None

							__UpperCAmelCase			:							Union[str, Any]	    =	pipe('''example prompt'''				,     num_inference_steps=2).images[0]
							assert image is not None

							# check that there's no error when saving a pipeline with one of the models being None
							with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowercase__)
													__UpperCAmelCase			:							List[Any]	    =	OnnxStableDiffusionPipeline.from_pretrained(lowercase__)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							__UpperCAmelCase			:							Optional[int]	    =	pipe('''example prompt'''				,     num_inference_steps=2).images[0]
							assert image is not None





import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCAmelCase       		=    logging.get_logger(__name__)


lowerCAmelCase       		=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCAmelCase       		=    {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase       		=    {
    """allenai/led-base-16384""": 16_384,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __SCREAMING_SNAKE_CASE							(       )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[int]	    =	(
						    list(range(ord('''!'''      )      ,       ord('''~'''      ) + 1      )      ) + list(range(ord('''¡'''      )      ,       ord('''¬'''      ) + 1      )      ) + list(range(ord('''®'''      )      ,       ord('''ÿ'''      ) + 1      )      )
						)
						__UpperCAmelCase			:							Tuple	    =	bs[:]
						__UpperCAmelCase			:							Dict	    =	0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(lowercase_      )
																		cs.append(2**8 + n      )
																		n += 1
						__UpperCAmelCase			:							Union[str, Any]	    =	[chr(lowercase_      ) for n in cs]
						return dict(zip(lowercase_      ,       lowercase_      )      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Any	    =	set()
						__UpperCAmelCase			:							Tuple	    =	word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												__UpperCAmelCase			:							Tuple	    =	char
						return pairs





class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		List[Any]										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		int										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase     :		int										=						['''input_ids''', '''attention_mask''']


	def __init__(    self				,     lowercase__				,     lowercase__				,     lowercase__="replace"				,     lowercase__="<s>"				,     lowercase__="</s>"				,     lowercase__="</s>"				,     lowercase__="<s>"				,     lowercase__="<unk>"				,     lowercase__="<pad>"				,     lowercase__="<mask>"				,     lowercase__=False				,     **lowercase__				,     ):
							__UpperCAmelCase			:							List[Any]	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else bos_token
							__UpperCAmelCase			:							int	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else eos_token
							__UpperCAmelCase			:							Optional[Any]	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else sep_token
							__UpperCAmelCase			:							Any	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else cls_token
							__UpperCAmelCase			:							Any	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else unk_token
							__UpperCAmelCase			:							List[str]	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							__UpperCAmelCase			:							Optional[Any]	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else mask_token

							super().__init__(
							    errors=lowercase__				,     bos_token=lowercase__				,     eos_token=lowercase__				,     unk_token=lowercase__				,     sep_token=lowercase__				,     cls_token=lowercase__				,     pad_token=lowercase__				,     mask_token=lowercase__				,     add_prefix_space=lowercase__				,     **lowercase__				,     )

							with open(lowercase__				,     encoding='''utf-8''') as vocab_handle:
													__UpperCAmelCase			:							Optional[int]	    =	json.load(lowercase__)
							__UpperCAmelCase			:							List[str]	    =	{v: k for k, v in self.encoder.items()}
							__UpperCAmelCase			:							Optional[Any]	    =	errors  # how to handle errors in decoding
							__UpperCAmelCase			:							str	    =	bytes_to_unicode()
							__UpperCAmelCase			:							List[str]	    =	{v: k for k, v in self.byte_encoder.items()}
							with open(lowercase__				,     encoding='''utf-8''') as merges_handle:
													__UpperCAmelCase			:							Optional[int]	    =	merges_handle.read().split('''\n''')[1:-1]
							__UpperCAmelCase			:							int	    =	[tuple(merge.split()) for merge in bpe_merges]
							__UpperCAmelCase			:							str	    =	dict(zip(lowercase__				,     range(len(lowercase__))))
							__UpperCAmelCase			:							Optional[int]	    =	{}
							__UpperCAmelCase			:							Optional[int]	    =	add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__UpperCAmelCase			:							List[Any]	    =	re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''')


	@property
	# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
	def  A(    self):
							return len(self.encoder)


	def  A(    self):
							return dict(self.encoder				,     **self.added_tokens_encoder)


	def  A(    self				,     lowercase__):
							if token in self.cache:
													return self.cache[token]
							__UpperCAmelCase			:							int	    =	tuple(lowercase__)
							__UpperCAmelCase			:							int	    =	get_pairs(lowercase__)

							if not pairs:
													return token

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

																			if word[i] == first and i < len(lowercase__) - 1 and word[i + 1] == second:
																									new_word.append(first + second)
																									i += 2
																			else:
																									new_word.append(word[i])
																									i += 1
													__UpperCAmelCase			:							Union[str, Any]	    =	tuple(lowercase__)
													__UpperCAmelCase			:							Dict	    =	new_word
													if len(lowercase__) == 1:
																			break
													else:
																			__UpperCAmelCase			:							Optional[int]	    =	get_pairs(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	''' '''.join(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	word
							return word


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							str	    =	[]
							for token in re.findall(self.pat				,     lowercase__):
													__UpperCAmelCase			:							Dict	    =	''''''.join(
													    self.byte_encoder[b] for b in token.encode('''utf-8'''))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__).split(''' '''))
							return bpe_tokens


	def  A(    self				,     lowercase__):
							return self.encoder.get(lowercase__				,     self.encoder.get(self.unk_token))


	def  A(    self				,     lowercase__):
							return self.decoder.get(lowercase__)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							str	    =	''''''.join(lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	bytearray([self.byte_decoder[c] for c in text]).decode('''utf-8'''				,     errors=self.errors)
							return text


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							if not os.path.isdir(lowercase__):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory")
													return
							__UpperCAmelCase			:							List[Any]	    =	os.path.join(
							    lowercase__				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
							__UpperCAmelCase			:							Optional[Any]	    =	os.path.join(
							    lowercase__				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

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

							__UpperCAmelCase			:							Tuple	    =	0
							with open(lowercase__				,     '''w'''				,     encoding='''utf-8''') as writer:
													writer.write('''#version: 0.2\n''')
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,     key=lambda lowercase__: 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!''')
																									__UpperCAmelCase			:							Optional[int]	    =	token_index
																			writer.write(''' '''.join(lowercase__) + '''\n''')
																			index += 1

							return vocab_file, merge_file


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							__UpperCAmelCase			:							Optional[Any]	    =	[self.cls_token_id]
							__UpperCAmelCase			:							Union[str, Any]	    =	[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = False):
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowercase__				,     token_ids_a=lowercase__				,     already_has_special_tokens=lowercase__)

							if token_ids_a is None:
													return [1] + ([0] * len(lowercase__)) + [1]
							return [1] + ([0] * len(lowercase__)) + [1, 1] + ([0] * len(lowercase__)) + [1]


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							List[Any]	    =	[self.sep_token_id]
							__UpperCAmelCase			:							List[Any]	    =	[self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


	def  A(    self				,     lowercase__				,     lowercase__=False				,     **lowercase__):
							__UpperCAmelCase			:							List[Any]	    =	kwargs.pop('''add_prefix_space'''				,     self.add_prefix_space)
							if (is_split_into_words or add_prefix_space) and (len(lowercase__) > 0 and not text[0].isspace()):
													__UpperCAmelCase			:							List[Any]	    =	''' ''' + text
							return (text, kwargs)


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = PaddingStrategy.DO_NOT_PAD				,     lowercase__ = None				,     lowercase__ = None				,     ):
							__UpperCAmelCase			:							Optional[Any]	    =	super()._pad(
							    encoded_inputs=lowercase__				,     max_length=lowercase__				,     padding_strategy=lowercase__				,     pad_to_multiple_of=lowercase__				,     return_attention_mask=lowercase__				,     )

							# Load from model defaults
							if return_attention_mask is None:
													__UpperCAmelCase			:							Optional[Any]	    =	'''attention_mask''' in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
													__UpperCAmelCase			:							Dict	    =	encoded_inputs[self.model_input_names[0]]
													# `global_attention_mask` need to have the same length as other (sequential) inputs.
													__UpperCAmelCase			:							int	    =	len(encoded_inputs['''global_attention_mask''']) != len(lowercase__)

													if needs_to_be_padded:
																			__UpperCAmelCase			:							Dict	    =	len(lowercase__) - len(encoded_inputs['''global_attention_mask'''])

																			if self.padding_side == "right":
																									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																									__UpperCAmelCase			:							Optional[Any]	    =	(
																									    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																									)
																			elif self.padding_side == "left":
																									__UpperCAmelCase			:							int	    =	[-1] * difference + encoded_inputs[
																									    '''global_attention_mask'''
																									]
																			else:
																									raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

							return encoded_inputs

