



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 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		MgpstrTokenizer
					lowercase_								=		False
					lowercase_								=		{}
					lowercase_								=		False







					def 			snake_case					(							self :     int					):
									super().setUp()

									# fmt: off
									lowercase__					:   List[str]													=					["[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
									lowercase__					:   Any													=					dict(zip(SCREAMING_SNAKE_CASE				,       range(len(SCREAMING_SNAKE_CASE					)					)					)					)

									lowercase__					:   Tuple													=					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(SCREAMING_SNAKE_CASE					) + "\n"					)







					def 			snake_case					(							self :     Dict				,       **SCREAMING_SNAKE_CASE :     List[Any]					):
									return MgpstrTokenizer.from_pretrained(self.tmpdirname				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Optional[int]					):
									lowercase__					:   Tuple													=					"tester"
									lowercase__					:   Optional[Any]													=					"tester"
									return input_text, output_text







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







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[int]													=					self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE					)
									for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
																	lowercase__					:   Any													=					"[SPECIAL_TOKEN]"

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

																	lowercase__					:   Any													=					tokenizer.decode(SCREAMING_SNAKE_CASE				,       skip_special_tokens=SCREAMING_SNAKE_CASE					)
																	self.assertTrue(special_token not in decoded					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					self.get_tokenizers()
									for tokenizer in tokenizers:
													with self.subTest(f"""{tokenizer.__class__.__name__}"""					):
																	lowercase__							,							lowercase__					:   Optional[Any]													=					self.get_input_output_texts(SCREAMING_SNAKE_CASE					)

																	lowercase__					:   List[str]													=					tokenizer.tokenize(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[Any]													=					tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE					)
																	lowercase__					:   Optional[Any]													=					tokenizer.encode(SCREAMING_SNAKE_CASE				,       add_special_tokens=SCREAMING_SNAKE_CASE					)
																	self.assertListEqual(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

																	lowercase__					:   List[Any]													=					tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE					)
																	self.assertNotEqual(len(SCREAMING_SNAKE_CASE					)				,       0					)
																	lowercase__					:   Any													=					tokenizer.decode(SCREAMING_SNAKE_CASE					)
																	self.assertIsInstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)

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







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







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









import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@property
					def 			snake_case					(							self :     Any					):
									torch.manual_seed(0					)
									lowercase__					:   Tuple													=					UNetaDModel(
									    block_out_channels=(32, 64)				,       layers_per_block=2				,       sample_size=32				,       in_channels=3				,       out_channels=3				,       down_block_types=("DownBlock2D", "AttnDownBlock2D")				,       up_block_types=("AttnUpBlock2D", "UpBlock2D")				,       )
									return model







					@property
					def 			snake_case					(							self :     List[str]					):
									torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					VQModel(
									    block_out_channels=[32, 64]				,       in_channels=3				,       out_channels=3				,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,       latent_channels=3				,       )
									return model







					@property
					def 			snake_case					(							self :     Dict					):
									torch.manual_seed(0					)
									lowercase__					:   str													=					CLIPTextConfig(
									    bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1E-0_5				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1_000				,       )
									return CLIPTextModel(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   Any													=					self.dummy_uncond_unet
									lowercase__					:   Dict													=					DDIMScheduler()
									lowercase__					:   Optional[Any]													=					self.dummy_vq_model

									lowercase__					:   Union[str, Any]													=					LDMPipeline(unet=SCREAMING_SNAKE_CASE				,       vqvae=SCREAMING_SNAKE_CASE				,       scheduler=SCREAMING_SNAKE_CASE					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					torch.manual_seed(0					)
									lowercase__					:   Optional[int]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"					).images

									lowercase__					:   str													=					torch.manual_seed(0					)
									lowercase__					:   List[Any]													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=2				,       output_type="numpy"				,       return_dict=SCREAMING_SNAKE_CASE					)[0]

									lowercase__					:   Any													=					image[0, -3:, -3:, -1]
									lowercase__					:   Optional[Any]													=					image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									lowercase__					:   List[Any]													=					np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]					)
									lowercase__					:   Optional[Any]													=					1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice					).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < tolerance






@slow
@require_torch
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"					)
									ldm.to(SCREAMING_SNAKE_CASE					)
									ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					torch.manual_seed(0					)
									lowercase__					:   Tuple													=					ldm(generator=SCREAMING_SNAKE_CASE				,       num_inference_steps=5				,       output_type="numpy"					).images

									lowercase__					:   Tuple													=					image[0, -3:, -3:, -1]

									assert image.shape == (1, 256, 256, 3)
									lowercase__					:   Optional[Any]													=					np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]					)
									lowercase__					:   int													=					1E-2 if torch_device != "mps" else 3E-2

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




