
"""simple docstring"""





import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers import AutoModelForCausalLM



@require_torch
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								A_								=    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(_snake_case						)
								A_								=    -1

								A_								=    ids_tensor((1, 5)							, vocab_size=model.config.vocab_size						).to(_snake_case						)
								A_								=    model.generate(_snake_case							, max_new_tokens=10							, do_sample=_snake_case						)
								A_								=    tokenizer.decode(greedy_ids[0]						)

								with CaptureStdout() as cs:
													A_								=    TextStreamer(_snake_case						)
													model.generate(_snake_case							, max_new_tokens=10							, do_sample=_snake_case							, streamer=_snake_case						)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								A_								=    cs.out[:-1]

								self.assertEqual(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								A_								=    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(_snake_case						)
								A_								=    -1

								A_								=    ids_tensor((1, 5)							, vocab_size=model.config.vocab_size						).to(_snake_case						)
								A_								=    model.generate(_snake_case							, max_new_tokens=10							, do_sample=_snake_case						)
								A_								=    tokenizer.decode(greedy_ids[0]						)

								A_								=    TextIteratorStreamer(_snake_case						)
								A_								=    {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
								A_								=    Thread(target=model.generate							, kwargs=_snake_case						)
								thread.start()
								A_								=    ""
								for new_text in streamer:
													streamer_text += new_text

								self.assertEqual(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       int						)   ->			Dict:







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								A_								=    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(_snake_case						)
								A_								=    -1

								A_								=    ids_tensor((1, 5)							, vocab_size=model.config.vocab_size						).to(_snake_case						)
								A_								=    model.generate(_snake_case							, max_new_tokens=10							, do_sample=_snake_case						)
								A_								=    greedy_ids[:, input_ids.shape[1] :]
								A_								=    tokenizer.decode(new_greedy_ids[0]						)

								with CaptureStdout() as cs:
													A_								=    TextStreamer(_snake_case							, skip_prompt=_snake_case						)
													model.generate(_snake_case							, max_new_tokens=10							, do_sample=_snake_case							, streamer=_snake_case						)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								A_								=    cs.out[:-1]

								self.assertEqual(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
								# with actual models -- the dummy models' tokenizers are not aligned with their models, and
								# `skip_special_tokens=True` has no effect on them
								A_								=    AutoTokenizer.from_pretrained("distilgpt2"						)
								A_								=    AutoModelForCausalLM.from_pretrained("distilgpt2"						).to(_snake_case						)
								A_								=    -1

								A_								=    torch.ones((1, 5)							, device=_snake_case						).long() * model.config.bos_token_id
								with CaptureStdout() as cs:
													A_								=    TextStreamer(_snake_case							, skip_special_tokens=_snake_case						)
													model.generate(_snake_case							, max_new_tokens=1							, do_sample=_snake_case							, streamer=_snake_case						)

								# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
								# re-tokenized, must only contain one token
								A_								=    cs.out[:-1]  # Remove the final "\n"
								A_								=    tokenizer(_snake_case							, return_tensors="pt"						)
								self.assertEqual(streamer_text_tokenized.input_ids.shape							, (1, 1)						)






			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			int:







								"""simple docstring"""
								A_								=    AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"						)
								A_								=    AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"						).to(_snake_case						)
								A_								=    -1

								A_								=    ids_tensor((1, 5)							, vocab_size=model.config.vocab_size						).to(_snake_case						)
								A_								=    TextIteratorStreamer(_snake_case							, timeout=0.0_0_1						)
								A_								=    {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
								A_								=    Thread(target=model.generate							, kwargs=_snake_case						)
								thread.start()

								# The streamer will timeout after 0.001 seconds, so an exception will be raised
								with self.assertRaises(_snake_case						):
													A_								=    ""
													for new_text in streamer:
																		streamer_text += new_text






"""simple docstring"""





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
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"""







			pass





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




			"""simple docstring"""







			@property
			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)




			@property
			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    ort.SessionOptions()
								A_								=    False
								return options




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=10							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]						)

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






			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo.png"						)
								A_								=    load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"						)
								A_								=    LMSDiscreteScheduler.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, subfolder="scheduler"							, revision="onnx"						)
								A_								=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    "runwayml/stable-diffusion-inpainting"							, revision="onnx"							, scheduler=_snake_case							, safety_checker=_snake_case							, feature_extractor=_snake_case							, provider=self.gpu_provider							, sess_options=self.gpu_options							, )
								pipe.set_progress_bar_config(disable=_snake_case						)

								A_								=    "A red cat sitting on a park bench"

								A_								=    np.random.RandomState(0						)
								A_								=    pipe(
								    prompt=_snake_case							, image=_snake_case							, mask_image=_snake_case							, guidance_scale=7.5							, num_inference_steps=20							, generator=_snake_case							, output_type="np"							, )
								A_								=    output.images
								A_								=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								A_								=    np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]						)

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




