



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_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		MgpstrTokenizer
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		{}
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														# fmt: off
														_lowerCAmelCase     :							Union[str, Any]    =    ["[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
														_lowerCAmelCase     :							List[str]    =    dict(zip(__a,			range(len(__a))))

														_lowerCAmelCase     :							Optional[int]    =    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    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return MgpstrTokenizer.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    "tester"
														_lowerCAmelCase     :							List[str]    =    "tester"
														return input_text, output_text







							@unittest.skip("MGP-STR always lower cases letters.")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    self.get_tokenizers(do_lower_case=__a)
														for tokenizer in tokenizers:
																					with self.subTest(f"{tokenizer.__class__.__name__}"):
																												_lowerCAmelCase     :							str    =    "[SPECIAL_TOKEN]"

																												tokenizer.add_special_tokens({"cls_token": special_token})
																												_lowerCAmelCase     :							List[str]    =    tokenizer.encode([special_token],			add_special_tokens=__a)
																												self.assertEqual(len(__a),			1)

																												_lowerCAmelCase     :							Optional[Any]    =    tokenizer.decode(__a,			skip_special_tokens=__a)
																												self.assertTrue(special_token not in decoded)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(f"{tokenizer.__class__.__name__}"):
																												_lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    self.get_input_output_texts(__a)

																												_lowerCAmelCase     :							int    =    tokenizer.tokenize(__a)
																												_lowerCAmelCase     :							Dict    =    tokenizer.convert_tokens_to_ids(__a)
																												_lowerCAmelCase     :							Dict    =    tokenizer.encode(__a,			add_special_tokens=__a)
																												self.assertListEqual(__a,			__a)

																												_lowerCAmelCase     :							Dict    =    tokenizer.convert_ids_to_tokens(__a)
																												self.assertNotEqual(len(__a),			0)
																												_lowerCAmelCase     :							List[str]    =    tokenizer.decode(__a)
																												self.assertIsInstance(__a,			__a)

																												self.assertEqual(text_a.replace(" ",			""),			__a)







							@unittest.skip("MGP-STR tokenizer only handles one sequence.")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass








"""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


_snake_case          =   False






class        UpperCamelCase			(	unittest.TestCase      ):
 pass







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


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


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Tuple             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[str]             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

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

   _a  :       Optional[Any]             =	generator.manual_seed(0							)
   _a  :       str             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						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 _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       Optional[int]             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

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

   _a  :       List[str]             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[int]             =	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

   _a  :       int             =	"""A painting of a squirrel eating a burger """
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.text_to_image(
       prompt=UpperCAmelCase__							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							).images

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

   assert image.shape == (1, 512, 512, 3)
   _a  :       int             =	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

   _a  :       str             =	pipe.image_variation(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       str             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	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

