





'''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


_A:					List[str]							=				False






class UpperCAmelCase							(		unittest.TestCase							):
    pass






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



    def 					__lowerCamelCase						(  self					):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,			torch_dtype=torch.floataa					)
     pipe.to(__A					)
     pipe.set_progress_bar_config(disable=__A					)

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

     __UpperCAmelCase					=   torch.manual_seed(0					)
     __UpperCAmelCase					=   pipe.dual_guided(
         prompt='first prompt'     ,			image=__A     ,			text_to_image_strength=0.7_5     ,			generator=__A     ,			guidance_scale=7.5     ,			num_inference_steps=2     ,			output_type='numpy'     ,			).images

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

     __UpperCAmelCase					=   generator.manual_seed(0					)
     __UpperCAmelCase					=   pipe.dual_guided(
         prompt='first prompt'     ,			image=__A     ,			text_to_image_strength=0.7_5     ,			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 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,			torch_dtype=torch.floataa					)
     pipe.to(__A					)
     pipe.set_progress_bar_config(disable=__A					)

     __UpperCAmelCase					=   'cyberpunk 2077'
     __UpperCAmelCase					=   load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
     __UpperCAmelCase					=   torch.manual_seed(0					)
     __UpperCAmelCase					=   pipe.dual_guided(
         prompt=__A     ,			image=__A     ,			text_to_image_strength=0.7_5     ,			generator=__A     ,			guidance_scale=7.5     ,			num_inference_steps=50     ,			output_type='numpy'     ,			).images

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

     assert image.shape == (1, 512, 512, 3)
     __UpperCAmelCase					=   np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]					)

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

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

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

     assert image.shape == (1, 512, 512, 3)
     __UpperCAmelCase					=   np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]					)

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

     __UpperCAmelCase					=   pipe.image_variation(__A     ,			generator=__A     ,			output_type='numpy'					).images

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

     assert image.shape == (1, 512, 512, 3)
     __UpperCAmelCase					=   np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]					)

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












'''simple docstring'''




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       MgpstrTokenizer
    _A :			Any						   =       False
    _A :			List[Any]						   =       {}
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self					):
     super().setUp()

     # fmt: off
     __UpperCAmelCase					=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
     # fmt: on
     __UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)

     __UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']					)
     with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'					) as fp:
      fp.write(json.dumps(__A					) + '\n'					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   'tester'
     __UpperCAmelCase					=   'tester'
     return input_text, output_text



    @unittest.skip('MGP-STR always lower cases letters.'					)
    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers(do_lower_case=__A					)
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					=   '[SPECIAL_TOKEN]'

       tokenizer.add_special_tokens({'cls_token': special_token}					)
       __UpperCAmelCase					=   tokenizer.encode([special_token]     ,			add_special_tokens=__A					)
       self.assertEqual(len(__A					)     ,			1					)

       __UpperCAmelCase					=   tokenizer.decode(__A     ,			skip_special_tokens=__A					)
       self.assertTrue(special_token not in decoded					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_input_output_texts(__A					)

       __UpperCAmelCase					=   tokenizer.tokenize(__A					)
       __UpperCAmelCase					=   tokenizer.convert_tokens_to_ids(__A					)
       __UpperCAmelCase					=   tokenizer.encode(__A     ,			add_special_tokens=__A					)
       self.assertListEqual(__A     ,			__A					)

       __UpperCAmelCase					=   tokenizer.convert_ids_to_tokens(__A					)
       self.assertNotEqual(len(__A					)     ,			0					)
       __UpperCAmelCase					=   tokenizer.decode(__A					)
       self.assertIsInstance(__A     ,			__A					)

       self.assertEqual(text_a.replace(' '     ,			''					)     ,			__A					)



    @unittest.skip('MGP-STR tokenizer only handles one sequence.'					)
    def 					__lowerCamelCase						(  self					):
     pass




    @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'					)
    def 					__lowerCamelCase						(  self					):
     pass





