
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_A						      =				False







class   lowerCamelCase	(						unittest.TestCase						):
     pass







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





     def 	UpperCAmelCase(self   :					List[Any]     )		->		List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		str:
         snake_case				  =      VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"     )
         # remove text_unet
         pipe.remove_unused_weights()
         pipe.to(A__     )
         pipe.set_progress_bar_config(disable=A__     )

         snake_case				  =      """A painting of a squirrel eating a burger """
         snake_case				  =      torch.manual_seed(0     )
         snake_case				  =      pipe(
             prompt=A__       ,					generator=A__       ,					guidance_scale=7.5       ,					num_inference_steps=2       ,					output_type="numpy"     ).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A__     )
             snake_case				  =      VersatileDiffusionTextToImagePipeline.from_pretrained(A__     )
         pipe.to(A__     )
         pipe.set_progress_bar_config(disable=A__     )

         snake_case				  =      generator.manual_seed(0     )
         snake_case				  =      pipe(
             prompt=A__       ,					generator=A__       ,					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 	UpperCAmelCase(self   :					List[str]     )		->		Optional[Any]:
         snake_case				  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
             "shi-labs/versatile-diffusion"       ,					torch_dtype=torch.floataa     )
         pipe.to(A__     )
         pipe.set_progress_bar_config(disable=A__     )

         snake_case				  =      """A painting of a squirrel eating a burger """
         snake_case				  =      torch.manual_seed(0     )
         snake_case				  =      pipe(
             prompt=A__       ,					generator=A__       ,					guidance_scale=7.5       ,					num_inference_steps=5_0       ,					output_type="numpy"     ).images

         snake_case				  =      image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case				  =      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-2



import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	PhobertTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         super().setUp()

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

         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         snake_case				  =      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(_A     )     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         kwargs.update(self.special_tokens_map     )
         return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[Any]     )		->		str:
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





     def 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         snake_case				  =      PhobertTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map     )
         snake_case				  =      "Tôi là VinAI Research"
         snake_case				  =      "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         snake_case				  =      tokenizer.tokenize(_A     )
         print(_A     )
         self.assertListEqual(_A       ,					_A     )

         snake_case				  =      tokens + [tokenizer.unk_token]

         snake_case				  =      [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(_A     )       ,					_A     )

