





'''simple docstring'''


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_		(		__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							int         =					"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"




 def 							_snake_case   (	self			,     _lowerCAmelCase=0		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      np.random.RandomState(_lowerCAmelCase		)
     _lowerCAmelCase				=      {
         "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 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

     assert image.shape == (1, 128, 128, 3)
     _lowerCAmelCase				=      np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455]		)

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




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     _lowerCAmelCase				=      PNDMScheduler.from_config(pipe.scheduler.config			,     skip_prk_steps=_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

     assert image.shape == (1, 128, 128, 3)
     _lowerCAmelCase				=      np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330]		)

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




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     _lowerCAmelCase				=      LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

     assert image.shape == (1, 128, 128, 3)
     _lowerCAmelCase				=      np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]		)

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




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     _lowerCAmelCase				=      EulerDiscreteScheduler.from_config(pipe.scheduler.config		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

     assert image.shape == (1, 128, 128, 3)
     _lowerCAmelCase				=      np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279]		)

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




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     _lowerCAmelCase				=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

     assert image.shape == (1, 128, 128, 3)
     _lowerCAmelCase				=      np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271]		)

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




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     _lowerCAmelCase				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

     assert image.shape == (1, 128, 128, 3)
     _lowerCAmelCase				=      np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200]		)

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




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_dummy_inputs()
     _lowerCAmelCase				=      3 * [inputs["prompt"]]

     # forward
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		)
     _lowerCAmelCase				=      output.images[0, -3:, -3:, -1]

     _lowerCAmelCase				=      self.get_dummy_inputs()
     _lowerCAmelCase				=      3 * [inputs.pop("prompt"		)]

     _lowerCAmelCase				=      pipe.tokenizer(
         _lowerCAmelCase			,     padding="max_length"			,     max_length=pipe.tokenizer.model_max_length			,     truncation=_lowerCAmelCase			,     return_tensors="np"			,     )
     _lowerCAmelCase				=      text_inputs["input_ids"]

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

     _lowerCAmelCase				=      prompt_embeds

     # forward
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		)
     _lowerCAmelCase				=      output.images[0, -3:, -3:, -1]

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





 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint			,     provider="CPUExecutionProvider"		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_dummy_inputs()
     _lowerCAmelCase				=      3 * ["this is a negative prompt"]
     _lowerCAmelCase				=      negative_prompt
     _lowerCAmelCase				=      3 * [inputs["prompt"]]

     # forward
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		)
     _lowerCAmelCase				=      output.images[0, -3:, -3:, -1]

     _lowerCAmelCase				=      self.get_dummy_inputs()
     _lowerCAmelCase				=      3 * [inputs.pop("prompt"		)]

     _lowerCAmelCase				=      []
     for p in [prompt, negative_prompt]:
         _lowerCAmelCase				=      pipe.tokenizer(
             _lowerCAmelCase			,     padding="max_length"			,     max_length=pipe.tokenizer.model_max_length			,     truncation=_lowerCAmelCase			,     return_tensors="np"			,     )
         _lowerCAmelCase				=      text_inputs["input_ids"]

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

     _lowerCAmelCase			,			_lowerCAmelCase				=      embeds

     # forward
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		)
     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )




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




 def 							_snake_case   (	self		)    ->							Dict:
     # using the PNDM scheduler by default
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"			,     revision="onnx"			,     safety_checker=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      "A painting of a squirrel eating a burger"
     np.random.seed(0		)
     _lowerCAmelCase				=      sd_pipe([prompt]			,     guidance_scale=6.0			,     num_inference_steps=10			,     output_type="np"		)
     _lowerCAmelCase				=      output.images

     _lowerCAmelCase				=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     _lowerCAmelCase				=      np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]		)

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




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      DDIMScheduler.from_pretrained(
         "runwayml/stable-diffusion-v1-5"			,     subfolder="scheduler"			,     revision="onnx"		)
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"			,     revision="onnx"			,     scheduler=_lowerCAmelCase			,     safety_checker=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      "open neural network exchange"
     _lowerCAmelCase				=      np.random.RandomState(0		)
     _lowerCAmelCase				=      sd_pipe([prompt]			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=_lowerCAmelCase			,     output_type="np"		)
     _lowerCAmelCase				=      output.images
     _lowerCAmelCase				=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     _lowerCAmelCase				=      np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]		)

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




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      LMSDiscreteScheduler.from_pretrained(
         "runwayml/stable-diffusion-v1-5"			,     subfolder="scheduler"			,     revision="onnx"		)
     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"			,     revision="onnx"			,     scheduler=_lowerCAmelCase			,     safety_checker=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      "open neural network exchange"
     _lowerCAmelCase				=      np.random.RandomState(0		)
     _lowerCAmelCase				=      sd_pipe([prompt]			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=_lowerCAmelCase			,     output_type="np"		)
     _lowerCAmelCase				=      output.images
     _lowerCAmelCase				=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     _lowerCAmelCase				=      np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]		)

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




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      0

     def test_callback_fn(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		) -> None:
         _lowerCAmelCase				=      True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 0:
             assert latents.shape == (1, 4, 64, 64)
             _lowerCAmelCase				=      latents[0, -3:, -3:, -1]
             _lowerCAmelCase				=      np.array(
                 [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]		)

             assert np.abs(latents_slice.flatten() - expected_slice		).max() < 1E-3
         elif step == 5:
             assert latents.shape == (1, 4, 64, 64)
             _lowerCAmelCase				=      latents[0, -3:, -3:, -1]
             _lowerCAmelCase				=      np.array(
                 [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]		)

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

     _lowerCAmelCase				=      False

     _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"			,     revision="onnx"			,     safety_checker=_lowerCAmelCase			,     feature_extractor=_lowerCAmelCase			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      "Andromeda galaxy in a bottle"

     _lowerCAmelCase				=      np.random.RandomState(0		)
     pipe(
         prompt=_lowerCAmelCase			,     num_inference_steps=5			,     guidance_scale=7.5			,     generator=_lowerCAmelCase			,     callback=_lowerCAmelCase			,     callback_steps=1			,     )
     assert test_callback_fn.has_been_called
     assert number_of_steps == 6





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

     _lowerCAmelCase				=      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(_lowerCAmelCase		)
         _lowerCAmelCase				=      OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase		)

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









'''simple docstring'''


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer

class    lowerCAmelCase_		:




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     raise NotImplementedError()





 def 							_snake_case   (	self		)    ->							Optional[int]:
     raise NotImplementedError()







class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False			,     **_lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      tokenizer
     _lowerCAmelCase				=      skip_prompt
     _lowerCAmelCase				=      decode_kwargs

     # variables used in the streaming process
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      0
     _lowerCAmelCase				=      True




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[Any]:
     if len(value.shape		) > 1 and value.shape[0] > 1:
         raise ValueError("TextStreamer only supports batch size 1"		)
     elif len(value.shape		) > 1:
         _lowerCAmelCase				=      value[0]

     if self.skip_prompt and self.next_tokens_are_prompt:
         _lowerCAmelCase				=      False
         return

     # Add the new token to the cache and decodes the entire thing.
     self.token_cache.extend(value.tolist()		)
     _lowerCAmelCase				=      self.tokenizer.decode(self.token_cache			,     **self.decode_kwargs		)

     # After the symbol for a new line, we flush the cache.
     if text.endswith("\n"		):
         _lowerCAmelCase				=      text[self.print_len :]
         _lowerCAmelCase				=      []
         _lowerCAmelCase				=      0
     # If the last token is a CJK character, we print the characters.
     elif len(_lowerCAmelCase		) > 0 and self._is_chinese_char(ord(text[-1]		)		):
         _lowerCAmelCase				=      text[self.print_len :]
         self.print_len += len(_lowerCAmelCase		)
     # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
     # which may change with the subsequent token -- there are probably smarter ways to do this!)
     else:
         _lowerCAmelCase				=      text[self.print_len : text.rfind(" "		) + 1]
         self.print_len += len(_lowerCAmelCase		)

     self.on_finalized_text(_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     # Flush the cache, if it exists
     if len(self.token_cache		) > 0:
         _lowerCAmelCase				=      self.tokenizer.decode(self.token_cache			,     **self.decode_kwargs		)
         _lowerCAmelCase				=      text[self.print_len :]
         _lowerCAmelCase				=      []
         _lowerCAmelCase				=      0
     else:
         _lowerCAmelCase				=      ""

     _lowerCAmelCase				=      True
     self.on_finalized_text(_lowerCAmelCase			,     stream_end=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							List[Any]:
     print(_lowerCAmelCase			,     flush=_lowerCAmelCase			,     end="" if not stream_end else None		)





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     # This defines a "chinese character" as anything in the CJK Unicode block:
     #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
     #
     # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
     # despite its name. The modern Korean Hangul alphabet is a different block,
     # as is Japanese Hiragana and Katakana. Those alphabets are used to write
     # space-separated words, so they are not treated specially and handled
     # like the all of the other languages.
     if (
         (cp >= 0X4E_00 and cp <= 0X9F_FF)
         or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
         or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
         or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
         or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
         or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
         or (cp >= 0XF9_00 and cp <= 0XFA_FF)
         or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
     ):  #
         return True

     return False







class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False			,     _lowerCAmelCase = None			,     **_lowerCAmelCase		)    ->							Dict:
     super().__init__(_lowerCAmelCase			,     _lowerCAmelCase			,     **_lowerCAmelCase		)
     _lowerCAmelCase				=      Queue()
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      timeout




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							Any:
     self.text_queue.put(_lowerCAmelCase			,     timeout=self.timeout		)
     if stream_end:
         self.text_queue.put(self.stop_signal			,     timeout=self.timeout		)




 def __iter__(	self		)    ->							int:
     return self





 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.text_queue.get(timeout=self.timeout		)
     if value == self.stop_signal:
         raise StopIteration()
     else:
         return value


