"""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 			_snake_case  (       _lowercase					,			unittest.TestCase		):
						snake_case__      	=      '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''






						def    lowerCamelCase__  (							self   :      Any				,       UpperCAmelCase   :      List[str]=0     ):
													__lowerCamelCase       :		Tuple		 =  floats_tensor((1, 3, 128, 128)				,       rng=random.Random(A_     )     )
													__lowerCamelCase       :		Optional[int]		 =  np.random.RandomState(A_     )
													__lowerCamelCase       :		Optional[int]		 =  {
													    "prompt": "A painting of a squirrel eating a burger",
													    "image": image,
													    "generator": generator,
													    "num_inference_steps": 3,
													    "strength": 0.7_5,
													    "guidance_scale": 7.5,
													    "output_type": "numpy",
													}
													return inputs






						def    lowerCamelCase__  (							self   :      Dict     ):
													__lowerCamelCase       :		int		 =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"     )
													pipe.set_progress_bar_config(disable=A_     )

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCamelCase       :		Union[str, Any]		 =  np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]     )
													assert np.abs(image_slice - expected_slice     ).max() < 1E-1






						def    lowerCamelCase__  (							self   :      List[Any]     ):
													__lowerCamelCase       :		str		 =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"     )
													__lowerCamelCase       :		str		 =  PNDMScheduler.from_config(pipe.scheduler.config				,       skip_prk_steps=A_     )
													pipe.set_progress_bar_config(disable=A_     )

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCamelCase       :		Any		 =  np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]     )

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






						def    lowerCamelCase__  (							self   :      Union[str, Any]     ):
													__lowerCamelCase       :		Any		 =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"     )
													__lowerCamelCase       :		Any		 =  LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=A_     )

													# warmup pass to apply optimizations
													__lowerCamelCase       :		Optional[Any]		 =  pipe(**self.get_dummy_inputs()     )

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCamelCase       :		List[str]		 =  np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]     )

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






						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													__lowerCamelCase       :		List[str]		 =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"     )
													__lowerCamelCase       :		List[str]		 =  EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=A_     )

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCamelCase       :		int		 =  np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]     )

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






						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													__lowerCamelCase       :		List[str]		 =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"     )
													__lowerCamelCase       :		Optional[int]		 =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=A_     )

													__lowerCamelCase       :		Dict		 =  self.get_dummy_inputs()
													__lowerCamelCase       :		str		 =  pipe(**A_     ).images
													__lowerCamelCase       :		Any		 =  image[0, -3:, -3:, -1]

													assert image.shape == (1, 128, 128, 3)
													__lowerCamelCase       :		List[Any]		 =  np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]     )

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







						def    lowerCamelCase__  (							self   :      int     ):
													__lowerCamelCase       :		Tuple		 =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,       provider="CPUExecutionProvider"     )
													__lowerCamelCase       :		int		 =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
													pipe.set_progress_bar_config(disable=A_     )

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

													assert image.shape == (1, 128, 128, 3)
													__lowerCamelCase       :		Optional[Any]		 =  np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]     )

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

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






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






						@property
						def    lowerCamelCase__  (							self   :      str     ):
													__lowerCamelCase       :		List[str]		 =  ort.SessionOptions()
													__lowerCamelCase       :		Any		 =  False
													return options






						def    lowerCamelCase__  (							self   :      Tuple     ):
													__lowerCamelCase       :		Optional[Any]		 =  load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/img2img/sketch-mountains-input.jpg"     )
													__lowerCamelCase       :		Dict		 =  init_image.resize((768, 512)     )
													# using the PNDM scheduler by default
													__lowerCamelCase       :		str		 =  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_     )

													__lowerCamelCase       :		List[Any]		 =  "A fantasy landscape, trending on artstation"

													__lowerCamelCase       :		Any		 =  np.random.RandomState(0     )
													__lowerCamelCase       :		Union[str, Any]		 =  pipe(
													    prompt=A_				,       image=A_				,       strength=0.7_5				,       guidance_scale=7.5				,       num_inference_steps=10				,       generator=A_				,       output_type="np"				,       )
													__lowerCamelCase       :		Optional[Any]		 =  output.images
													__lowerCamelCase       :		str		 =  images[0, 255:258, 383:386, -1]

													assert images.shape == (1, 512, 768, 3)
													__lowerCamelCase       :		Optional[Any]		 =  np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]     )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													__lowerCamelCase       :		Optional[Any]		 =  load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/img2img/sketch-mountains-input.jpg"     )
													__lowerCamelCase       :		List[str]		 =  init_image.resize((768, 512)     )
													__lowerCamelCase       :		int		 =  LMSDiscreteScheduler.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"				,       subfolder="scheduler"				,       revision="onnx"     )
													__lowerCamelCase       :		str		 =  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_     )

													__lowerCamelCase       :		Optional[int]		 =  "A fantasy landscape, trending on artstation"

													__lowerCamelCase       :		Optional[Any]		 =  np.random.RandomState(0     )
													__lowerCamelCase       :		str		 =  pipe(
													    prompt=A_				,       image=A_				,       strength=0.7_5				,       guidance_scale=7.5				,       num_inference_steps=20				,       generator=A_				,       output_type="np"				,       )
													__lowerCamelCase       :		Union[str, Any]		 =  output.images
													__lowerCamelCase       :		Optional[Any]		 =  images[0, 255:258, 383:386, -1]

													assert images.shape == (1, 512, 768, 3)
													__lowerCamelCase       :		Tuple		 =  np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]     )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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

"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowercase				 =					{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


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


						def __init__(			self  :   Union[str, Any]	,A_  :   str	,A_  :   str="<s>"	,A_  :   Any="</s>"	,A_  :   Tuple="</s>"	,A_  :   Any="<s>"	,A_  :   Optional[Any]="<unk>"	,A_  :   int="<pad>"	,A_  :   str="<mask>"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   Optional[int]	,)  -> None:
							# Mask token behave like a normal word, i.e. include the space before it
							A     = AddedToken(A_	,lstrip=A_	,rstrip=A_   ) if isinstance(A_	,A_   ) else mask_token

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

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

							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'

							# Mimic fairseq token-to-id alignment for the first 4 token
							A     = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							A     = 1

							A     = len(self.sp_model   ) + self.fairseq_offset
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(			self  :   Union[str, Any]   )  -> Any:
							A     = self.__dict__.copy()
							A     = None
							A     = self.sp_model.serialized_model_proto()
							return state


						def __setstate__(			self  :   str	,A_  :   str   )  -> Optional[Any]:
							A     = d

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

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


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

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,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 [1] + ([0] * len(A_   )) + [1]
							return [1] + ([0] * len(A_   )) + [1, 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]
							A     = [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]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							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  :   str	,A_  :   str   )  -> int:
							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  :   List[str]	,A_  :   Optional[Any]   )  -> List[Any]:
							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,)