

"""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      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
		__UpperCAmelCase      		= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(_lowercase						)
		__UpperCAmelCase      		= -1

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

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

		self.assertEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       Optional[Any]						):
		__UpperCAmelCase      		= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
		__UpperCAmelCase      		= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(_lowercase						)
		__UpperCAmelCase      		= -1

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

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

		self.assertEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
		__UpperCAmelCase      		= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(_lowercase						)
		__UpperCAmelCase      		= -1

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

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

		self.assertEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       Tuple						):
		# 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
		__UpperCAmelCase      		= AutoTokenizer.from_pretrained('''distilgpt2'''						)
		__UpperCAmelCase      		= AutoModelForCausalLM.from_pretrained('''distilgpt2'''						).to(_lowercase						)
		__UpperCAmelCase      		= -1

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

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



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						)
		__UpperCAmelCase      		= AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''						).to(_lowercase						)
		__UpperCAmelCase      		= -1

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

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





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

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

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Dict						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

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