






'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class    A_	(a_							):
							"""simple docstring"""




							def __init__(		self			:Optional[int]       ,							lowerCAmelCase__			:List[Any]       ,							lowerCAmelCase__			:int				)				->  Optional[int]:


										'''simple docstring'''




										super().__init__()

										# make sure scheduler can always be converted to DDIM
										snake_case_				:					Union[str, Any]   =       DDIMScheduler.from_config(scheduler.config				)

										self.register_modules(unet=lowerCAmelCase__       ,							scheduler=lowerCAmelCase__				)






							@torch.no_grad()
							def __call__(		self			:List[Any]       ,							lowerCAmelCase__			:int = 1       ,							lowerCAmelCase__			:Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							lowerCAmelCase__			:float = 0.0       ,							lowerCAmelCase__			:int = 50       ,							lowerCAmelCase__			:Optional[bool] = None       ,							lowerCAmelCase__			:Optional[str] = "pil"       ,							lowerCAmelCase__			:bool = True       ,							)				->  Union[ImagePipelineOutput, Tuple]:


										'''simple docstring'''




										if isinstance(self.unet.config.sample_size       ,							lowerCAmelCase__				):
													snake_case_				:					Tuple   =       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
										else:
													snake_case_				:					Optional[int]   =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				) and len(lowerCAmelCase__				) != batch_size:
													raise ValueError(
													    F'''You have passed a list of generators of length {len(lowerCAmelCase__				)}, but requested an effective batch'''
													    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

										snake_case_				:					Optional[int]   =       randn_tensor(lowerCAmelCase__       ,							generator=lowerCAmelCase__       ,							device=self.device       ,							dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(lowerCAmelCase__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
													# 1. predict noise model_output
													snake_case_				:					int   =       self.unet(lowerCAmelCase__       ,							lowerCAmelCase__				).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													snake_case_				:					Any   =       self.scheduler.step(
													    lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							eta=lowerCAmelCase__       ,							use_clipped_model_output=lowerCAmelCase__       ,							generator=lowerCAmelCase__				).prev_sample

										snake_case_				:					Optional[Any]   =       (image / 2 + 0.5).clamp(0       ,							1				)
										snake_case_				:					Any   =       image.cpu().permute(0       ,							2       ,							3       ,							1				).numpy()
										if output_type == "pil":
													snake_case_				:					List[Any]   =       self.numpy_to_pil(lowerCAmelCase__				)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__				)












'''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    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MgpstrTokenizer
							a__   		=	False
							a__   		=	{}
							a__   		=	False

							def 							_A	(		self			:List[str]				)				->  List[str]:


										'''simple docstring'''




										super().setUp()

										# fmt: off
										snake_case_				:					Dict   =       ["[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
										snake_case_				:					List[str]   =       dict(zip(lowerCAmelCase__       ,							range(len(lowerCAmelCase__				)				)				)				)

										snake_case_				:					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(lowerCAmelCase__				) + "\n"				)

							def 							_A	(		self			:Optional[Any]       ,							**lowerCAmelCase__			:Optional[Any]				)				->  Dict:


										'''simple docstring'''




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

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Any				)				->  str:


										'''simple docstring'''




										snake_case_				:					Dict   =       "tester"
										snake_case_				:					Tuple   =       "tester"
										return input_text, output_text

							@unittest.skip("MGP-STR always lower cases letters."				)
							def 							_A	(		self			:Dict				)				->  str:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.get_tokenizers(do_lower_case=lowerCAmelCase__				)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																snake_case_				:					Tuple   =       "[SPECIAL_TOKEN]"

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

																snake_case_				:					Tuple   =       tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__				)
																self.assertTrue(special_token not in decoded				)

							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																snake_case_,	snake_case_				:					str   =       self.get_input_output_texts(lowerCAmelCase__				)

																snake_case_				:					Union[str, Any]   =       tokenizer.tokenize(lowerCAmelCase__				)
																snake_case_				:					List[Any]   =       tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
																snake_case_				:					Dict   =       tokenizer.encode(lowerCAmelCase__       ,							add_special_tokens=lowerCAmelCase__				)
																self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

																snake_case_				:					List[str]   =       tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
																self.assertNotEqual(len(lowerCAmelCase__				)       ,							0				)
																snake_case_				:					List[str]   =       tokenizer.decode(lowerCAmelCase__				)
																self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)

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

							@unittest.skip("MGP-STR tokenizer only handles one sequence."				)
							def 							_A	(		self			:Union[str, Any]				)				->  Any:


										'''simple docstring'''




										pass






							@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"				)
							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										pass




