"""simple docstring"""



import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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__     (   __SCREAMING_SNAKE_CASE,		unittest.TestCase   ):
 # TODO: is there an appropriate internal test set?
 _snake_case   :			List[str]           =		"ssube/stable-diffusion-x4-upscaler-onnx"


 def 		a__ (				self  ,						lowerCamelCase=0		):
        __a     				=  floats_tensor((1, 3, 128, 128)  ,						rng=random.Random(__UpperCAmelCase		)		)
        __a     				=  torch.manual_seed(__UpperCAmelCase		)
        __a     				=  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs


 def 		a__ (				self		):
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint  ,						provider="CPUExecutionProvider"		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

        # started as 128, should now be 512
        assert image.shape == (1, 512, 512, 3)
        __a     				=  np.array(
            [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]		)
        assert np.abs(image_slice - expected_slice		).max() < 1E-1


 def 		a__ (				self		):
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint  ,						provider="CPUExecutionProvider"		)
        __a     				=  PNDMScheduler.from_config(pipe.scheduler.config  ,						skip_prk_steps=__UpperCAmelCase		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

        assert image.shape == (1, 512, 512, 3)
        __a     				=  np.array(
            [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964]		)
        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1


 def 		a__ (				self		):
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint  ,						provider="CPUExecutionProvider"		)
        __a     				=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

        assert image.shape == (1, 512, 512, 3)
        __a     				=  np.array(
            [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515]		)

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


 def 		a__ (				self		):
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint  ,						provider="CPUExecutionProvider"		)
        __a     				=  EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

        assert image.shape == (1, 512, 512, 3)
        __a     				=  np.array(
            [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223]		)
        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1






 def 		a__ (				self		):
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint  ,						provider="CPUExecutionProvider"		)
        __a     				=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

        assert image.shape == (1, 512, 512, 3)
        __a     				=  np.array(
            [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043]		)

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






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


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


 @property
 def 		a__ (				self		):
        __a     				=  ort.SessionOptions()
        __a     				=  False
        return options


 def 		a__ (				self		):
        __a     				=  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/img2img/sketch-mountains-input.jpg"		)
        __a     				=  init_image.resize((128, 128)		)
        # using the PNDM scheduler by default
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
            "ssube/stable-diffusion-x4-upscaler-onnx"  ,						provider=self.gpu_provider  ,						sess_options=self.gpu_options  ,						)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        __a     				=  """A fantasy landscape, trending on artstation"""

        __a     				=  torch.manual_seed(0		)
        __a     				=  pipe(
            prompt=__UpperCAmelCase  ,						image=__UpperCAmelCase  ,						guidance_scale=7.5  ,						num_inference_steps=10  ,						generator=__UpperCAmelCase  ,						output_type="np"  ,						)
        __a     				=  output.images
        __a     				=  images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        __a     				=  np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972]		)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






 def 		a__ (				self		):
        __a     				=  load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/img2img/sketch-mountains-input.jpg"		)
        __a     				=  init_image.resize((128, 128)		)
        __a     				=  LMSDiscreteScheduler.from_pretrained(
            "ssube/stable-diffusion-x4-upscaler-onnx"  ,						subfolder="scheduler"		)
        __a     				=  OnnxStableDiffusionUpscalePipeline.from_pretrained(
            "ssube/stable-diffusion-x4-upscaler-onnx"  ,						scheduler=__UpperCAmelCase  ,						provider=self.gpu_provider  ,						sess_options=self.gpu_options  ,						)
        pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

        __a     				=  """A fantasy landscape, trending on artstation"""

        __a     				=  torch.manual_seed(0		)
        __a     				=  pipe(
            prompt=__UpperCAmelCase  ,						image=__UpperCAmelCase  ,						guidance_scale=7.5  ,						num_inference_steps=20  ,						generator=__UpperCAmelCase  ,						output_type="np"  ,						)
        __a     				=  output.images
        __a     				=  images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        __a     				=  np.array(
            [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566]		)
        # 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 collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

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


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.txt"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

SCREAMING_SNAKE_CASE__:Tuple			=	{
    """openbmb/cpm-ant-10b""": 1024,
}



def 					_lowerCamelCase(					a							):
       __a     				=  collections.OrderedDict()
       with open(a ,  "r" ,  encoding="utf-8"							) as reader:
              __a     				=  reader.readlines()
       for index, token in enumerate(a							):
              __a     				=  token.rstrip("\n"							)
              __a     				=  index
       return vocab






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase=200		):
        __a     				=  vocab
        __a     				=  unk_token
        __a     				=  max_input_chars_per_word






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  list(lowerCamelCase		)
        if len(lowerCamelCase		) > self.max_input_chars_per_word:
               return [self.unk_token]

        __a     				=  0
        __a     				=  []
        while start < len(lowerCamelCase		):
               __a     				=  len(lowerCamelCase		)
               __a     				=  None
               while start < end:
                      __a     				=  "".join(chars[start:end]		)
                      if substr in self.vocab:
                             __a     				=  substr
                             break
                      end -= 1
               if cur_substr is None:
                      sub_tokens.append(self.unk_token		)
                      start += 1
               else:
                      sub_tokens.append(lowerCamelCase		)
                      __a     				=  end

        return sub_tokens






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[int]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			int           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			int           =		False


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="<d>"  ,						lowerCamelCase="</d>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="</n>"  ,						lowerCamelCase="</_>"  ,						lowerCamelCase="left"  ,						**lowerCamelCase  ,						):
        requires_backends(self  ,						["jieba"]		)
        super().__init__(
            bod_token=lowerCamelCase  ,						eod_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						line_token=lowerCamelCase  ,						space_token=lowerCamelCase  ,						padding_side=lowerCamelCase  ,						**lowerCamelCase  ,						)
        __a     				=  bod_token
        __a     				=  eod_token
        __a     				=  load_vocab(lowerCamelCase		)
        __a     				=  self.encoder[space_token]
        __a     				=  self.encoder[line_token]

        del self.encoder[space_token]
        del self.encoder[line_token]

        __a     				=  collections.OrderedDict(sorted(self.encoder.items()  ,						key=lambda lowerCamelCase		: x[1]		)		)
        __a     				=  {v: k for k, v in self.encoder.items()}

        __a     				=  WordpieceTokenizer(vocab=self.encoder  ,						unk_token=self.unk_token		)


 @property
 def 		a__ (				self		):
        return self.encoder[self.bod_token]


 @property
 def 		a__ (				self		):
        return self.encoder[self.eod_token]


 @property
 def 		a__ (				self		):
        return self.encoder["\n"]


 @property
 def 		a__ (				self		):
        return len(self.encoder		)


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


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for x in jieba.cut(lowerCamelCase  ,						cut_all=lowerCamelCase		):
               output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase		)		)
        return output_tokens


 def 		a__ (				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  [i for i in token_ids if i >= 0]
        __a     				=  [
            x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
        ]
        return super()._decode(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return token in self.encoder


 def 		a__ (				self  ,						lowerCamelCase		):
        return "".join(lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.encoder.get(lowerCamelCase  ,						self.encoder.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase  ,						self.unk_token		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if os.path.isdir(lowerCamelCase		):
               __a     				=  os.path.join(
                   lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
        else:
               __a     				=  (filename_prefix + "-" if filename_prefix else "") + save_directory
        __a     				=  0
        if " " in self.encoder:
               __a     				=  self.encoder[" "]
               del self.encoder[" "]
        if "\n" in self.encoder:
               __a     				=  self.encoder["\n"]
               del self.encoder["\n"]
        __a     				=  collections.OrderedDict(sorted(self.encoder.items()  ,						key=lambda lowerCamelCase		: x[1]		)		)
        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as writer:
               for token, token_index in self.encoder.items():
                      if index != token_index:
                             logger.warning(
                                 F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                                 " Please check that the vocabulary is not corrupted!"		)
                             __a     				=  token_index
                      writer.write(token + "\n"		)
                      index += 1
        return (vocab_file,)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if token_ids_a is None:
               return [self.bos_token_id] + token_ids_a
        return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = False		):

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

        if token_ids_a is not None:
               return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		))
        return [1] + ([0] * len(lowerCamelCase		))
