
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		(  A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Union[str, Any]									=		"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[Any]=0       )						->						str:
         snake_case								= floats_tensor((1, 3, 1_2_8, 1_2_8)    ,   rng=random.Random(_A       )       )
         snake_case								= np.random.RandomState(_A       )
         snake_case								= {
             "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 		UpperCAmelCase(self				:   Any       )						->						Tuple:
         snake_case								= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"       )
         pipe.set_progress_bar_config(disable=_A       )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   str       )						->						Dict:
         snake_case								= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"       )
         snake_case								= PNDMScheduler.from_config(pipe.scheduler.config    ,   skip_prk_steps=_A       )
         pipe.set_progress_bar_config(disable=_A       )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   Optional[Any]       )						->						Tuple:
         snake_case								= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"       )
         snake_case								= LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=_A       )

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

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   List[str]       )						->						Any:
         snake_case								= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"       )
         snake_case								= EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=_A       )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   List[Any]       )						->						int:
         snake_case								= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"       )
         snake_case								= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=_A       )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         snake_case								= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint    ,   provider="CPUExecutionProvider"       )
         snake_case								= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=_A       )

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

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case								= 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  ):





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





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         snake_case								= ort.SessionOptions()
         snake_case								= False
         return options





     def 		UpperCAmelCase(self				:   str       )						->						List[str]:
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"       )
         snake_case								= init_image.resize((7_6_8, 5_1_2)       )
         # using the PNDM scheduler by default
         snake_case								= 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       )

         snake_case								= "A fantasy landscape, trending on artstation"

         snake_case								= np.random.RandomState(0       )
         snake_case								= pipe(
             prompt=_A    ,   image=_A    ,   strength=0.75    ,   guidance_scale=7.5    ,   num_inference_steps=1_0    ,   generator=_A    ,   output_type="np"    ,   )
         snake_case								= output.images
         snake_case								= images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case								= 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 		UpperCAmelCase(self				:   Any       )						->						Any:
         snake_case								= load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/img2img/sketch-mountains-input.jpg"       )
         snake_case								= init_image.resize((7_6_8, 5_1_2)       )
         snake_case								= LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-v1-5"    ,   subfolder="scheduler"    ,   revision="onnx"       )
         snake_case								= 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       )

         snake_case								= "A fantasy landscape, trending on artstation"

         snake_case								= np.random.RandomState(0       )
         snake_case								= pipe(
             prompt=_A    ,   image=_A    ,   strength=0.75    ,   guidance_scale=7.5    ,   num_inference_steps=2_0    ,   generator=_A    ,   output_type="np"    ,   )
         snake_case								= output.images
         snake_case								= images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert images.shape == (1, 5_1_2, 7_6_8, 3)
         snake_case								= 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



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

_A   =			{
    "bert-base-uncased": 5_12,
    "bert-large-uncased": 5_12,
    "bert-base-cased": 5_12,
    "bert-large-cased": 5_12,
    "bert-base-multilingual-uncased": 5_12,
    "bert-base-multilingual-cased": 5_12,
    "bert-base-chinese": 5_12,
    "bert-base-german-cased": 5_12,
    "bert-large-uncased-whole-word-masking": 5_12,
    "bert-large-cased-whole-word-masking": 5_12,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_12,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_12,
    "bert-base-cased-finetuned-mrpc": 5_12,
    "bert-base-german-dbmdz-cased": 5_12,
    "bert-base-german-dbmdz-uncased": 5_12,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_12,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_12,
    "wietsedv/bert-base-dutch-cased": 5_12,
}

_A   =			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Union[str, Any]									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Union[str, Any]									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Any									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							int									=		BertTokenizer





     def __init__(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=None    ,   _A				:   Optional[int]=None    ,   _A				:   List[str]=True    ,   _A				:   List[Any]="[UNK]"    ,   _A				:   Union[str, Any]="[SEP]"    ,   _A				:   List[Any]="[PAD]"    ,   _A				:   List[Any]="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   int=True    ,   _A				:   Tuple=None    ,   **_A				:   Optional[int]    ,   )						->						int:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Union[str, Any]    ,   _A				:   int=None       )						->						Any:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [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       ) * [0] + len(token_ids_a + sep       ) * [1]





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

