
"""simple docstring"""


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''prophetnet.tokenizer'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

_lowercase				 =					{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

_lowercase				 =					{
    '''microsoft/xprophetnet-large-wiki100-cased''': 5_12,
}






def    _snake_case					(		snake_case__ :  Optional[Any]  ):
	A     = collections.OrderedDict()
	with open(snake_case__						,   'r'						,   encoding='utf-8'  ) as reader:
		A     = reader.readlines()
	for index, token in enumerate(snake_case__  ):
		A     = token.rstrip('\n'  )
		A     = index
	return vocab







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     Tuple   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Optional[Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Tuple   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   int	,A_  :   List[str]	,A_  :   Dict="[SEP]"	,A_  :   List[str]="[SEP]"	,A_  :   Optional[int]="[SEP]"	,A_  :   str="[UNK]"	,A_  :   str="[PAD]"	,A_  :   Dict="[CLS]"	,A_  :   Optional[Any]="[MASK]"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   List[Any]	,)  -> None:
							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_	,eos_token=A_	,sep_token=A_	,unk_token=A_	,pad_token=A_	,cls_token=A_	,mask_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							try:
								import sentencepiece as spm
							except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece'   )
								raise

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							A     = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

							for i in range(10   ):
								A     = F'[unused{i}]'
								A     = 5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							A     = 12
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
								self.unique_no_split_tokens.append(A_   )


						def __getstate__(			self  :   Tuple   )  -> List[str]:
							A     = self.__dict__.copy()
							A     = None
							return state


						def __setstate__(			self  :   Dict	,A_  :   int   )  -> str:
							A     = d
							try:
								import sentencepiece as spm
							except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece'   )
								raise

							# for backward compatibility
							if not hasattr(self	,'sp_model_kwargs'   ):
								A     = {}

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]

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


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							return len(self.sp_model   ) + self.fairseq_offset


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Any:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   str   )  -> str:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Union[str, Any]   )  -> int:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   List[str]   )  -> str:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Union[str, Any]   )  -> Tuple:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = os.path.join(
							    A_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

							if token_ids_a is None:
								return token_ids_a + [self.sep_token_id]
							A     = [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep

"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Dict   =     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   List[str]=0   )  -> str:
							A     = floats_tensor((1, 3, 128, 128)	,rng=random.Random(A_   )   )
							A     = np.random.RandomState(A_   )
							A     = {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'strength': 0.75,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]   )
							assert np.abs(image_slice - expected_slice   ).max() < 1e-1


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = PNDMScheduler.from_config(pipe.scheduler.config	,skip_prk_steps=A_   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							# warmup pass to apply optimizations
							A     = pipe(**self.get_dummy_inputs()   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> str:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]   )

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







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



						'''simple docstring'''


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A     = ort.SessionOptions()
							A     = False
							return options


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> str:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							# using the PNDM scheduler by default
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'	,revision='onnx'	,safety_checker=A_	,feature_extractor=A_	,provider=self.gpu_provider	,sess_options=self.gpu_options	,)
							pipe.set_progress_bar_config(disable=A_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=10	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]   )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[Any]:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							A     = LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,subfolder='scheduler'	,revision='onnx'   )
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,revision='onnx'	,scheduler=A_	,safety_checker=A_	,feature_extractor=A_	,provider=self.gpu_provider	,sess_options=self.gpu_options	,)
							pipe.set_progress_bar_config(disable=A_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=20	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]   )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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