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


__UpperCamelCase		:       Any									=     False





class 					lowercase__						(   unittest.TestCase):
						pass






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


						def        __A       (       self	:				Any						):





												'''simple docstring'''





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


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''    ,		torch_dtype=torch.floataa						)
												pipe.to(UpperCamelCase__						)
												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

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

												SCREAMING_SNAKE_CASE							:    Dict							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Any							=							pipe.dual_guided(
												    prompt='''first prompt'''    ,		image=UpperCamelCase__    ,		text_to_image_strength=0.75    ,		generator=UpperCamelCase__    ,		guidance_scale=7.5    ,		num_inference_steps=2    ,		output_type='''numpy'''    ,		).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__    ,		torch_dtype=torch.floataa						)
												pipe.to(UpperCamelCase__						)
												pipe.set_progress_bar_config(disable=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    str							=							generator.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    str							=							pipe.dual_guided(
												    prompt='''first prompt'''    ,		image=UpperCamelCase__    ,		text_to_image_strength=0.75    ,		generator=UpperCamelCase__    ,		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        __A       (       self	:				Tuple						):





												'''simple docstring'''





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

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

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

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:    Dict							=							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							:    Optional[int]							=							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.manual_seed(0						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipe.text_to_image(
												    prompt=UpperCamelCase__    ,		generator=UpperCamelCase__    ,		guidance_scale=7.5    ,		num_inference_steps=50    ,		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							:    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							:    Dict							=							pipe.image_variation(UpperCamelCase__    ,		generator=UpperCamelCase__    ,		output_type='''numpy'''						).images

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

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:    Dict							=							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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





