



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class   UpperCAmelCase_    (  unittest.TestCase			):

						'''simple docstring'''




						def 			_A      (       self						):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						@property
						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   1
													__SCREAMING_SNAKE_CASE										=   3
													__SCREAMING_SNAKE_CASE										=   (32, 32)

													__SCREAMING_SNAKE_CASE										=   floats_tensor((batch_size, num_channels) + sizes ,				rng=random.Random(0						)						).to(_lowerCAmelCase						)
													return image






						@property
						def 			_A      (       self						):







													'''simple docstring'''

													torch.manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   UNetaDConditionModel(
													    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=4 ,				out_channels=4 ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,				cross_attention_dim=32 ,				)
													return model






						@property
						def 			_A      (       self						):







													'''simple docstring'''

													torch.manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   AutoencoderKL(
													    block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,				latent_channels=4 ,				)
													return model






						@property
						def 			_A      (       self						):







													'''simple docstring'''

													torch.manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   CLIPTextConfig(
													    bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1e-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1_000 ,				)
													return CLIPTextModel(_lowerCAmelCase						)






						@property
						def 			_A      (       self						):







													'''simple docstring'''

													def extract(*_A ,				**_A						):

																				class   UpperCAmelCase_    :

																										'''simple docstring'''




																										def __init__(       self						):







																																	'''simple docstring'''

																																	__SCREAMING_SNAKE_CASE										=   torch.ones([0]						)






																										def 			_A      (       self ,				_A						):







																																	'''simple docstring'''

																																	self.pixel_values.to(_lowerCAmelCase						)
																																	return self







																				return Out()

													return extract






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'cpu'  # ensure determinism for the device-dependent torch.Generator
													__SCREAMING_SNAKE_CASE										=   self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE										=   DDIMScheduler(
													    beta_start=0.0_0_0_8_5 ,				beta_end=0.0_1_2 ,				beta_schedule='scaled_linear' ,				clip_sample=_lowerCAmelCase ,				set_alpha_to_one=_lowerCAmelCase ,				)

													__SCREAMING_SNAKE_CASE										=   self.dummy_vae
													__SCREAMING_SNAKE_CASE										=   self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE										=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline(
													    unet=_lowerCAmelCase ,				scheduler=_lowerCAmelCase ,				vae=_lowerCAmelCase ,				text_encoder=_lowerCAmelCase ,				tokenizer=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase ,				feature_extractor=self.dummy_extractor ,				)
													__SCREAMING_SNAKE_CASE										=   sd_pipe.to(_lowerCAmelCase						)
													sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   'A painting of a squirrel eating a burger'

													__SCREAMING_SNAKE_CASE										=   torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe([prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np'						)
													__SCREAMING_SNAKE_CASE										=   output.images

													__SCREAMING_SNAKE_CASE										=   torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np' ,				return_dict=_lowerCAmelCase ,				)[0]

													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE										=   np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]						)

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






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   'cpu'  # ensure determinism for the device-dependent torch.Generator
													__SCREAMING_SNAKE_CASE										=   self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE										=   PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   self.dummy_vae
													__SCREAMING_SNAKE_CASE										=   self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE										=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline(
													    unet=_lowerCAmelCase ,				scheduler=_lowerCAmelCase ,				vae=_lowerCAmelCase ,				text_encoder=_lowerCAmelCase ,				tokenizer=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase ,				feature_extractor=self.dummy_extractor ,				)
													__SCREAMING_SNAKE_CASE										=   sd_pipe.to(_lowerCAmelCase						)
													sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   'A painting of a squirrel eating a burger'
													__SCREAMING_SNAKE_CASE										=   torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe([prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np'						)

													__SCREAMING_SNAKE_CASE										=   output.images

													__SCREAMING_SNAKE_CASE										=   torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=6.0 ,				num_inference_steps=2 ,				output_type='np' ,				return_dict=_lowerCAmelCase ,				)[0]

													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													__SCREAMING_SNAKE_CASE										=   np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]						)

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






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline.from_pretrained(
													    'hf-internal-testing/tiny-stable-diffusion-lms-pipe' ,				safety_checker=_lowerCAmelCase						)
													assert isinstance(_lowerCAmelCase ,				_lowerCAmelCase						)
													assert isinstance(pipe.scheduler ,				_lowerCAmelCase						)
													assert pipe.safety_checker is None

													__SCREAMING_SNAKE_CASE										=   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						)
																				__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline.from_pretrained(_lowerCAmelCase						)

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






						@unittest.skipIf(torch_device != 'cuda' ,				'This test requires a GPU'						)
						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.dummy_cond_unet
													__SCREAMING_SNAKE_CASE										=   PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   self.dummy_vae
													__SCREAMING_SNAKE_CASE										=   self.dummy_text_encoder
													__SCREAMING_SNAKE_CASE										=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

													# put models in fp16
													__SCREAMING_SNAKE_CASE										=   unet.half()
													__SCREAMING_SNAKE_CASE										=   vae.half()
													__SCREAMING_SNAKE_CASE										=   bert.half()

													# make sure here that pndm scheduler skips prk
													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline(
													    unet=_lowerCAmelCase ,				scheduler=_lowerCAmelCase ,				vae=_lowerCAmelCase ,				text_encoder=_lowerCAmelCase ,				tokenizer=_lowerCAmelCase ,				safety_checker=_lowerCAmelCase ,				feature_extractor=self.dummy_extractor ,				)
													__SCREAMING_SNAKE_CASE										=   sd_pipe.to(_lowerCAmelCase						)
													sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   'A painting of a squirrel eating a burger'
													__SCREAMING_SNAKE_CASE										=   sd_pipe([prompt] ,				num_inference_steps=2 ,				output_type='np'						).images

													assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class   UpperCAmelCase_    (  unittest.TestCase			):

						'''simple docstring'''




						def 			_A      (       self						):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe.to(_lowerCAmelCase						)
													sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   (
													    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
													    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
													    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
													    ' children from bahnhof zoo, detailed '
													)
													__SCREAMING_SNAKE_CASE										=   4_003_660_346
													__SCREAMING_SNAKE_CASE										=   7

													# without safety guidance (sld_guidance_scale = 0)
													__SCREAMING_SNAKE_CASE										=   torch.manual_seed(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=_lowerCAmelCase ,				num_inference_steps=50 ,				output_type='np' ,				width=512 ,				height=512 ,				sld_guidance_scale=0 ,				)

													__SCREAMING_SNAKE_CASE										=   output.images
													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

													assert image.shape == (1, 512, 512, 3)

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

													# without safety guidance (strong configuration)
													__SCREAMING_SNAKE_CASE										=   torch.manual_seed(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=_lowerCAmelCase ,				num_inference_steps=50 ,				output_type='np' ,				width=512 ,				height=512 ,				sld_guidance_scale=2_000 ,				sld_warmup_steps=7 ,				sld_threshold=0.0_2_5 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

													__SCREAMING_SNAKE_CASE										=   output.images
													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

													assert image.shape == (1, 512, 512, 3)

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






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' ,				safety_checker=_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe.to(_lowerCAmelCase						)
													sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   'padme amidala taking a bath artwork, safe for work, no nudity'
													__SCREAMING_SNAKE_CASE										=   2_734_971_755
													__SCREAMING_SNAKE_CASE										=   7

													__SCREAMING_SNAKE_CASE										=   torch.manual_seed(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=_lowerCAmelCase ,				num_inference_steps=50 ,				output_type='np' ,				width=512 ,				height=512 ,				sld_guidance_scale=0 ,				)

													__SCREAMING_SNAKE_CASE										=   output.images
													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

													assert image.shape == (1, 512, 512, 3)

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

													__SCREAMING_SNAKE_CASE										=   torch.manual_seed(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=_lowerCAmelCase ,				num_inference_steps=50 ,				output_type='np' ,				width=512 ,				height=512 ,				sld_guidance_scale=2_000 ,				sld_warmup_steps=7 ,				sld_threshold=0.0_2_5 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

													__SCREAMING_SNAKE_CASE										=   output.images
													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

													assert image.shape == (1, 512, 512, 3)

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






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe.to(_lowerCAmelCase						)
													sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

													__SCREAMING_SNAKE_CASE										=   (
													    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
													    ' leyendecker'
													)
													__SCREAMING_SNAKE_CASE										=   1_044_355_234
													__SCREAMING_SNAKE_CASE										=   12

													__SCREAMING_SNAKE_CASE										=   torch.manual_seed(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=_lowerCAmelCase ,				num_inference_steps=50 ,				output_type='np' ,				width=512 ,				height=512 ,				sld_guidance_scale=0 ,				)

													__SCREAMING_SNAKE_CASE										=   output.images
													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]						)

													assert image.shape == (1, 512, 512, 3)

													assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-7

													__SCREAMING_SNAKE_CASE										=   torch.manual_seed(_lowerCAmelCase						)
													__SCREAMING_SNAKE_CASE										=   sd_pipe(
													    [prompt] ,				generator=_lowerCAmelCase ,				guidance_scale=_lowerCAmelCase ,				num_inference_steps=50 ,				output_type='np' ,				width=512 ,				height=512 ,				sld_guidance_scale=2_000 ,				sld_warmup_steps=7 ,				sld_threshold=0.0_2_5 ,				sld_momentum_scale=0.5 ,				sld_mom_beta=0.7 ,				)

													__SCREAMING_SNAKE_CASE										=   output.images
													__SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
													__SCREAMING_SNAKE_CASE										=   np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]						)
													assert image.shape == (1, 512, 512, 3)

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













'''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		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

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

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # 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
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

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

     # 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
     _lowerCAmelCase						      =   cs.out[:-1]  # Remove the final "\n"
     _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase      ,  timeout=0.001		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()

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


