

"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			Dict	  =			False


class 		_A       (			unittest.TestCase					):
						pass






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





						def 					_lowerCamelCase  (  self		)   ->					Any:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





						def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
									lowerCamelCase__						=			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,						torch_dtype=torch.floataa		)
									pipe.to(lowerCamelCase__		)
									pipe.set_progress_bar_config(disable=lowerCamelCase__		)

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

									lowerCamelCase__						=			torch.manual_seed(0		)
									lowerCamelCase__						=			pipe.dual_guided(
									    prompt="first prompt"    ,						image=lowerCamelCase__    ,						text_to_image_strength=0.75    ,						generator=lowerCamelCase__    ,						guidance_scale=7.5    ,						num_inference_steps=2    ,						output_type="numpy"    ,						).images

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

									lowerCamelCase__						=			generator.manual_seed(0		)
									lowerCamelCase__						=			pipe.dual_guided(
									    prompt="first prompt"    ,						image=lowerCamelCase__    ,						text_to_image_strength=0.75    ,						generator=lowerCamelCase__    ,						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		)   ->					Optional[int]:
									lowerCamelCase__						=			VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"    ,						torch_dtype=torch.floataa		)
									pipe.to(lowerCamelCase__		)
									pipe.set_progress_bar_config(disable=lowerCamelCase__		)

									lowerCamelCase__						=			'''cyberpunk 2077'''
									lowerCamelCase__						=			load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
									lowerCamelCase__						=			torch.manual_seed(0		)
									lowerCamelCase__						=			pipe.dual_guided(
									    prompt=lowerCamelCase__    ,						image=lowerCamelCase__    ,						text_to_image_strength=0.75    ,						generator=lowerCamelCase__    ,						guidance_scale=7.5    ,						num_inference_steps=50    ,						output_type="numpy"    ,						).images

									lowerCamelCase__						=			image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									lowerCamelCase__						=			np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]		)

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

									lowerCamelCase__						=			'''A painting of a squirrel eating a burger '''
									lowerCamelCase__						=			torch.manual_seed(0		)
									lowerCamelCase__						=			pipe.text_to_image(
									    prompt=lowerCamelCase__    ,						generator=lowerCamelCase__    ,						guidance_scale=7.5    ,						num_inference_steps=50    ,						output_type="numpy"		).images

									lowerCamelCase__						=			image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									lowerCamelCase__						=			np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

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

									lowerCamelCase__						=			pipe.image_variation(lowerCamelCase__    ,						generator=lowerCamelCase__    ,						output_type="numpy"		).images

									lowerCamelCase__						=			image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									lowerCamelCase__						=			np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]		)

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







"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 		_A       (			__a				,       unittest.TestCase					):
      __a								=	PhobertTokenizer
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					Any:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCamelCase__						=			["T@@", "i", "I", "R@@", "r", "e@@"]
         lowerCamelCase__						=			dict(zip(SCREAMING_SNAKE_CASE__    ,						range(len(SCREAMING_SNAKE_CASE__		)		)		)		)
         lowerCamelCase__						=			["#version: 0.2", "l à</w>"]
         lowerCamelCase__						=			{"unk_token": "<unk>"}

         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"]		)

         with open(self.vocab_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            for token in vocab_tokens:
               fp.write(f'{token} {vocab_tokens[token]}\n'		)
         with open(self.merges_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            fp.write("\n".join(SCREAMING_SNAKE_CASE__		)		)





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         kwargs.update(self.special_tokens_map		)
         return PhobertTokenizer.from_pretrained(self.tmpdirname    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			PhobertTokenizer(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map		)
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         lowerCamelCase__						=			tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
         print(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			tokens + [tokenizer.unk_token]

         lowerCamelCase__						=			[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)




