




import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class       UpperCAmelCase_   ( __SCREAMING_SNAKE_CASE		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  RealmTokenizer
							a__    	=  RealmTokenizerFast
							a__    	=  True
							a__    	=  True
							a__    	=  filter_non_english





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Dict:
										super().setUp()

										SCREAMING_SNAKE_CASE     =       [
										    """[UNK]""",
										    """[CLS]""",
										    """[SEP]""",
										    """[PAD]""",
										    """[MASK]""",
										    """want""",
										    """##want""",
										    """##ed""",
										    """wa""",
										    """un""",
										    """runn""",
										    """##ing""",
										    """,""",
										    """low""",
										    """lowest""",
										]
										SCREAMING_SNAKE_CASE     =       os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES["""vocab_file"""]      )
										with open(self.vocab_file							,  """w"""							,  encoding="""utf-8"""      ) as vocab_writer:
													vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]      )      )





							def  _UpperCAmelCase  (       self							:						Tuple							,  a							:						Any      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       """UNwant\u00E9d,running"""
										SCREAMING_SNAKE_CASE     =       """unwanted, running"""
										return input_text, output_text





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       self.tokenizer_class(self.vocab_file      )

										SCREAMING_SNAKE_CASE     =       tokenizer.tokenize("""UNwant\u00E9d,running"""      )
										self.assertListEqual(lowerCamelCase_							,  ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]      )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_      )							,  [9, 6, 7, 12, 10, 11]      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Optional[int]:
										if not self.test_rust_tokenizer:
													return

										SCREAMING_SNAKE_CASE     =       self.get_tokenizer()
										SCREAMING_SNAKE_CASE     =       self.get_rust_tokenizer()

										SCREAMING_SNAKE_CASE     =       """UNwant\u00E9d,running"""

										SCREAMING_SNAKE_CASE     =       tokenizer.tokenize(lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       rust_tokenizer.tokenize(lowerCamelCase_      )
										self.assertListEqual(lowerCamelCase_							,  lowerCamelCase_      )

										SCREAMING_SNAKE_CASE     =       tokenizer.encode(lowerCamelCase_							,  add_special_tokens=lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       rust_tokenizer.encode(lowerCamelCase_							,  add_special_tokens=lowerCamelCase_      )
										self.assertListEqual(lowerCamelCase_							,  lowerCamelCase_      )

										SCREAMING_SNAKE_CASE     =       self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE     =       tokenizer.encode(lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       rust_tokenizer.encode(lowerCamelCase_      )
										self.assertListEqual(lowerCamelCase_							,  lowerCamelCase_      )

										# With lower casing
										SCREAMING_SNAKE_CASE     =       self.get_tokenizer(do_lower_case=lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       self.get_rust_tokenizer(do_lower_case=lowerCamelCase_      )

										SCREAMING_SNAKE_CASE     =       """UNwant\u00E9d,running"""

										SCREAMING_SNAKE_CASE     =       tokenizer.tokenize(lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       rust_tokenizer.tokenize(lowerCamelCase_      )
										self.assertListEqual(lowerCamelCase_							,  lowerCamelCase_      )

										SCREAMING_SNAKE_CASE     =       tokenizer.encode(lowerCamelCase_							,  add_special_tokens=lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       rust_tokenizer.encode(lowerCamelCase_							,  add_special_tokens=lowerCamelCase_      )
										self.assertListEqual(lowerCamelCase_							,  lowerCamelCase_      )

										SCREAMING_SNAKE_CASE     =       self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE     =       tokenizer.encode(lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       rust_tokenizer.encode(lowerCamelCase_      )
										self.assertListEqual(lowerCamelCase_							,  lowerCamelCase_      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer()

										self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""      )							,  ["""ah""", """\u535A""", """\u63A8""", """zz"""]      )





							def  _UpperCAmelCase  (       self							:						int      )      ->       Union[str, Any]:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """      )							,  ["""hello""", """!""", """how""", """are""", """you""", """?"""]      )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )							,  ["""hello"""]      )





							def  _UpperCAmelCase  (       self							:						Any      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_							,  strip_accents=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )							,  ["""hällo""", """!""", """how""", """are""", """you""", """?"""]      )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )							,  ["""h\u00E9llo"""]      )





							def  _UpperCAmelCase  (       self							:						Any      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_							,  strip_accents=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )							,  ["""hallo""", """!""", """how""", """are""", """you""", """?"""]      )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )							,  ["""hello"""]      )





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )							,  ["""hallo""", """!""", """how""", """are""", """you""", """?"""]      )
										self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""      )							,  ["""hello"""]      )





							def  _UpperCAmelCase  (       self							:						str      )      ->       int:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """      )							,  ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )





							def  _UpperCAmelCase  (       self							:						Any      )      ->       int:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_							,  strip_accents=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )							,  ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       int:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_							,  strip_accents=lowerCamelCase_      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """      )							,  ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       int:
										SCREAMING_SNAKE_CASE     =       BasicTokenizer(do_lower_case=lowerCamelCase_							,  never_split=["""[UNK]"""]      )

										self.assertListEqual(
										    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""      )							,  ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]      )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

										SCREAMING_SNAKE_CASE     =       {}
										for i, token in enumerate(lowerCamelCase_      ):
													SCREAMING_SNAKE_CASE     =       i
										SCREAMING_SNAKE_CASE     =       WordpieceTokenizer(vocab=lowerCamelCase_							,  unk_token="""[UNK]"""      )

										self.assertListEqual(tokenizer.tokenize(""""""      )							,  []      )

										self.assertListEqual(tokenizer.tokenize("""unwanted running"""      )							,  ["""un""", """##want""", """##ed""", """runn""", """##ing"""]      )

										self.assertListEqual(tokenizer.tokenize("""unwantedX running"""      )							,  ["""[UNK]""", """runn""", """##ing"""]      )





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       List[str]:
										self.assertTrue(_is_whitespace(""" """      )      )
										self.assertTrue(_is_whitespace("""\t"""      )      )
										self.assertTrue(_is_whitespace("""\r"""      )      )
										self.assertTrue(_is_whitespace("""\n"""      )      )
										self.assertTrue(_is_whitespace("""\u00A0"""      )      )

										self.assertFalse(_is_whitespace("""A"""      )      )
										self.assertFalse(_is_whitespace("""-"""      )      )





							def  _UpperCAmelCase  (       self							:						Any      )      ->       Optional[Any]:
										self.assertTrue(_is_control("""\u0005"""      )      )

										self.assertFalse(_is_control("""A"""      )      )
										self.assertFalse(_is_control(""" """      )      )
										self.assertFalse(_is_control("""\t"""      )      )
										self.assertFalse(_is_control("""\r"""      )      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Union[str, Any]:
										self.assertTrue(_is_punctuation("""-"""      )      )
										self.assertTrue(_is_punctuation("""$"""      )      )
										self.assertTrue(_is_punctuation("""`"""      )      )
										self.assertTrue(_is_punctuation("""."""      )      )

										self.assertFalse(_is_punctuation("""A"""      )      )
										self.assertFalse(_is_punctuation(""" """      )      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       int:
										SCREAMING_SNAKE_CASE     =       self.get_tokenizer()

										# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
										self.assertListEqual([tokenizer.tokenize(lowerCamelCase_      ) for t in ["""Test""", """\xad""", """test"""]]							,  [["""[UNK]"""], [], ["""[UNK]"""]]      )

										if self.test_rust_tokenizer:
													SCREAMING_SNAKE_CASE     =       self.get_rust_tokenizer()
													self.assertListEqual(
													    [rust_tokenizer.tokenize(lowerCamelCase_      ) for t in ["""Test""", """\xad""", """test"""]]							,  [["""[UNK]"""], [], ["""[UNK]"""]]      )





							@slow
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       self.tokenizer_class.from_pretrained("""bert-base-uncased"""      )

										SCREAMING_SNAKE_CASE     =       tokenizer.encode("""sequence builders"""							,  add_special_tokens=lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       tokenizer.encode("""multi-sequence build"""							,  add_special_tokens=lowerCamelCase_      )

										SCREAMING_SNAKE_CASE     =       tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      )
										SCREAMING_SNAKE_CASE     =       tokenizer.build_inputs_with_special_tokens(lowerCamelCase_							,  lowerCamelCase_      )

										assert encoded_sentence == [101] + text + [102]
										assert encoded_pair == [101] + text + [102] + text_a + [102]





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       int:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																SCREAMING_SNAKE_CASE     =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase_							,  **lowerCamelCase_      )

																SCREAMING_SNAKE_CASE     =       f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																SCREAMING_SNAKE_CASE     =       tokenizer_r.encode_plus(
																    lowerCamelCase_							,  return_attention_mask=lowerCamelCase_							,  return_token_type_ids=lowerCamelCase_							,  return_offsets_mapping=lowerCamelCase_							,  add_special_tokens=lowerCamelCase_							,  )

																SCREAMING_SNAKE_CASE     =       tokenizer_r.do_lower_case if hasattr(lowerCamelCase_							,  """do_lower_case"""      ) else False
																SCREAMING_SNAKE_CASE     =       (
																    [
																        ((0, 0), tokenizer_r.cls_token),
																        ((0, 1), """A"""),
																        ((1, 2), ""","""),
																        ((3, 5), """na"""),
																        ((5, 6), """##ï"""),
																        ((6, 8), """##ve"""),
																        ((9, 15), tokenizer_r.mask_token),
																        ((16, 21), """Allen"""),
																        ((21, 23), """##NL"""),
																        ((23, 24), """##P"""),
																        ((25, 33), """sentence"""),
																        ((33, 34), """."""),
																        ((0, 0), tokenizer_r.sep_token),
																    ]
																    if not do_lower_case
																    else [
																        ((0, 0), tokenizer_r.cls_token),
																        ((0, 1), """a"""),
																        ((1, 2), ""","""),
																        ((3, 8), """naive"""),
																        ((9, 15), tokenizer_r.mask_token),
																        ((16, 21), """allen"""),
																        ((21, 23), """##nl"""),
																        ((23, 24), """##p"""),
																        ((25, 33), """sentence"""),
																        ((33, 34), """."""),
																        ((0, 0), tokenizer_r.sep_token),
																    ]
																)

																self.assertEqual(
																    [e[1] for e in expected_results]							,  tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]      )      )
																self.assertEqual([e[0] for e in expected_results]							,  tokens["""offset_mapping"""]      )







							@slow
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[str]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																SCREAMING_SNAKE_CASE     =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase_							,  **lowerCamelCase_      )
																SCREAMING_SNAKE_CASE     =       self.tokenizer_class.from_pretrained(lowerCamelCase_							,  **lowerCamelCase_      )
																SCREAMING_SNAKE_CASE     =       [["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]

																SCREAMING_SNAKE_CASE     =       tokenizer_r.batch_encode_candidates(lowerCamelCase_							,  max_length=10							,  return_tensors="""np"""      )
																SCREAMING_SNAKE_CASE     =       tokenizer_p.batch_encode_candidates(lowerCamelCase_							,  max_length=10							,  return_tensors="""np"""      )

																SCREAMING_SNAKE_CASE     =       (2, 2, 10)

																self.assertEqual(encoded_sentence_r["""input_ids"""].shape							,  lowerCamelCase_      )
																self.assertEqual(encoded_sentence_r["""attention_mask"""].shape							,  lowerCamelCase_      )
																self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape							,  lowerCamelCase_      )

																self.assertEqual(encoded_sentence_p["""input_ids"""].shape							,  lowerCamelCase_      )
																self.assertEqual(encoded_sentence_p["""attention_mask"""].shape							,  lowerCamelCase_      )
																self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape							,  lowerCamelCase_      )










"""simple docstring"""







from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__a      			= getLogger(__name__)


__a      			= 4
__a      			= 7

__a      			= {
    "float16": torch.floataa,
    "float32": torch.floataa,
}






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						if isinstance(_UpperCamelCase						,      torch.Tensor			):
												return image
						elif isinstance(_UpperCamelCase						,      PIL.Image.Image			):
												UpperCAmelCase__												=     [image]

						if isinstance(image[0]						,      PIL.Image.Image			):
												UpperCAmelCase__						,					UpperCAmelCase__												=     image[0].size
												UpperCAmelCase__						,					UpperCAmelCase__												=     (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												UpperCAmelCase__												=     [np.array(i.resize((w, h)			)			)[None, :] for i in image]
												UpperCAmelCase__												=     np.concatenate(_UpperCamelCase						,      axis=0			)
												UpperCAmelCase__												=     np.array(_UpperCamelCase			).astype(np.floataa			) / 255.0
												UpperCAmelCase__												=     image.transpose(0						,      3						,      1						,      2			)
												UpperCAmelCase__												=     2.0 * image - 1.0
												UpperCAmelCase__												=     torch.from_numpy(_UpperCamelCase			)
						elif isinstance(image[0]						,      torch.Tensor			):
												UpperCAmelCase__												=     torch.cat(_UpperCamelCase						,      dim=0			)

						return image


class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							def __init__(    self			:  Optional[int]		,			lowerCamelCase_			:  OnnxRuntimeModel		,			lowerCamelCase_			:  OnnxRuntimeModel		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  OnnxRuntimeModel		,			lowerCamelCase_			:  DDPMScheduler		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  int = 350		,			):
													super().__init__(
													    vae=lowerCamelCase_		,			text_encoder=lowerCamelCase_		,			tokenizer=lowerCamelCase_		,			unet=lowerCamelCase_		,			low_res_scheduler=lowerCamelCase_		,			scheduler=lowerCamelCase_		,			safety_checker=lowerCamelCase_		,			feature_extractor=lowerCamelCase_		,			watermarker=lowerCamelCase_		,			max_noise_level=lowerCamelCase_		,			)






							def __call__(    self			:  Optional[int]		,			lowerCamelCase_			:  Union[str, List[str]]		,			lowerCamelCase_			:  Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]		,			lowerCamelCase_			:  int = 75		,			lowerCamelCase_			:  float = 9.0		,			lowerCamelCase_			:  int = 20		,			lowerCamelCase_			:  Optional[Union[str, List[str]]] = None		,			lowerCamelCase_			:  Optional[int] = 1		,			lowerCamelCase_			:  float = 0.0		,			lowerCamelCase_			:  Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowerCamelCase_			:  Optional[torch.FloatTensor] = None		,			lowerCamelCase_			:  Optional[np.ndarray] = None		,			lowerCamelCase_			:  Optional[np.ndarray] = None		,			lowerCamelCase_			:  Optional[str] = "pil"		,			lowerCamelCase_			:  bool = True		,			lowerCamelCase_			:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowerCamelCase_			:  Optional[int] = 1		,			):
													self.check_inputs(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

													# 2. Define call parameters
													if prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     1
													elif prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     len(lowerCamelCase_					)
													else:
																			UpperCAmelCase__												=     prompt_embeds.shape[0]

													UpperCAmelCase__												=     self._execution_device
													# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
													# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
													# corresponds to doing no classifier free guidance.
													UpperCAmelCase__												=     guidance_scale > 1.0

													# 3. Encode input prompt
													UpperCAmelCase__												=     self._encode_prompt(
													    lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			prompt_embeds=lowerCamelCase_		,			negative_prompt_embeds=lowerCamelCase_		,			)

													UpperCAmelCase__												=     ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

													# 4. Preprocess image
													UpperCAmelCase__												=     preprocess(lowerCamelCase_					)
													UpperCAmelCase__												=     image.cpu()

													# 5. set timesteps
													self.scheduler.set_timesteps(lowerCamelCase_		,			device=lowerCamelCase_					)
													UpperCAmelCase__												=     self.scheduler.timesteps

													# 5. Add noise to image
													UpperCAmelCase__												=     torch.tensor([noise_level]		,			dtype=torch.long		,			device=lowerCamelCase_					)
													UpperCAmelCase__												=     torch.randn(image.shape		,			generator=lowerCamelCase_		,			device=lowerCamelCase_		,			dtype=lowerCamelCase_					)
													UpperCAmelCase__												=     self.low_res_scheduler.add_noise(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

													UpperCAmelCase__												=     2 if do_classifier_free_guidance else 1
													UpperCAmelCase__												=     np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
													UpperCAmelCase__												=     np.concatenate([noise_level] * image.shape[0]					)

													# 6. Prepare latent variables
													UpperCAmelCase__						,					UpperCAmelCase__												=     image.shape[2:]
													UpperCAmelCase__												=     self.prepare_latents(
													    batch_size * num_images_per_prompt		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			)

													# 7. Check that sizes of image and latents match
													UpperCAmelCase__												=     image.shape[1]
													if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																			raise ValueError(
																			    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
																			    f""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
																			    f""" `num_channels_image`: {num_channels_image} """
																			    f""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
																			    ''' `pipeline.unet` or your `image` input.'''					)

													# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
													UpperCAmelCase__												=     self.prepare_extra_step_kwargs(lowerCamelCase_		,			lowerCamelCase_					)

													UpperCAmelCase__												=     next(
													    (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')		,			'''tensor(float)'''					)
													UpperCAmelCase__												=     ORT_TO_NP_TYPE[timestep_dtype]

													# 9. Denoising loop
													UpperCAmelCase__												=     len(lowerCamelCase_					) - num_inference_steps * self.scheduler.order
													with self.progress_bar(total=lowerCamelCase_					) as progress_bar:
																			for i, t in enumerate(lowerCamelCase_					):
																									# expand the latents if we are doing classifier free guidance
																									UpperCAmelCase__												=     np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

																									# concat latents, mask, masked_image_latents in the channel dimension
																									UpperCAmelCase__												=     self.scheduler.scale_model_input(lowerCamelCase_		,			lowerCamelCase_					)
																									UpperCAmelCase__												=     np.concatenate([latent_model_input, image]		,			axis=1					)

																									# timestep to tensor
																									UpperCAmelCase__												=     np.array([t]		,			dtype=lowerCamelCase_					)

																									# predict the noise residual
																									UpperCAmelCase__												=     self.unet(
																									    sample=lowerCamelCase_		,			timestep=lowerCamelCase_		,			encoder_hidden_states=lowerCamelCase_		,			class_labels=noise_level.astype(np.intaa					)		,			)[0]

																									# perform guidance
																									if do_classifier_free_guidance:
																															UpperCAmelCase__						,					UpperCAmelCase__												=     np.split(lowerCamelCase_		,			2					)
																									UpperCAmelCase__												=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									# compute the previous noisy sample x_t -> x_t-1
																									UpperCAmelCase__												=     self.scheduler.step(
																									    torch.from_numpy(lowerCamelCase_					)		,			lowerCamelCase_		,			lowerCamelCase_		,			**lowerCamelCase_					).prev_sample

																									# call the callback, if provided
																									if i == len(lowerCamelCase_					) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																															progress_bar.update()
																															if callback is not None and i % callback_steps == 0:
																																					callback(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

        # 10. Post-processing
													UpperCAmelCase__												=     self.decode_latents(latents.float()					)

													# 11. Convert to PIL
													if output_type == "pil":
																			UpperCAmelCase__												=     self.numpy_to_pil(lowerCamelCase_					)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			lowerCamelCase_			:  List[Any]					):
													UpperCAmelCase__												=     1 / 0.08333 * latents
													UpperCAmelCase__												=     self.vae(latent_sample=lowerCamelCase_					)[0]
													UpperCAmelCase__												=     np.clip(image / 2 + 0.5		,			0		,			1					)
													UpperCAmelCase__												=     image.transpose((0, 2, 3, 1)					)
													return image






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  Union[str, List[str]]		,			lowerCamelCase_			:  Union[str, Any]		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  bool		,			lowerCamelCase_			:  Optional[str]		,			lowerCamelCase_			:  Optional[torch.FloatTensor] = None		,			lowerCamelCase_			:  Optional[torch.FloatTensor] = None		,			):
													if prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     1
													elif prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     len(lowerCamelCase_					)
													else:
																			UpperCAmelCase__												=     prompt_embeds.shape[0]

													if prompt_embeds is None:
																			UpperCAmelCase__												=     self.tokenizer(
																			    lowerCamelCase_		,			padding='''max_length'''		,			max_length=self.tokenizer.model_max_length		,			truncation=lowerCamelCase_		,			return_tensors='''pt'''		,			)
																			UpperCAmelCase__												=     text_inputs.input_ids
																			UpperCAmelCase__												=     self.tokenizer(lowerCamelCase_		,			padding='''longest'''		,			return_tensors='''pt'''					).input_ids

																			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																			    lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     self.tokenizer.batch_decode(
																									    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
																									logger.warning(
																									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)

																			# no positional arguments to text_encoder
																			UpperCAmelCase__												=     self.text_encoder(
																			    input_ids=text_input_ids.int().to(lowerCamelCase_					)		,			)
																			UpperCAmelCase__												=     prompt_embeds[0]

													UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     prompt_embeds.shape
													# duplicate text embeddings for each generation per prompt, using mps friendly method
													UpperCAmelCase__												=     prompt_embeds.repeat(1		,			lowerCamelCase_					)
													UpperCAmelCase__												=     prompt_embeds.reshape(bs_embed * num_images_per_prompt		,			lowerCamelCase_		,			-1					)

													# get unconditional embeddings for classifier free guidance
													if do_classifier_free_guidance and negative_prompt_embeds is None:
																			UpperCAmelCase__												=     42
																			if negative_prompt is None:
																									UpperCAmelCase__												=     [''''''] * batch_size
																			elif type(lowerCamelCase_					) is not type(lowerCamelCase_					):
																									raise TypeError(
																									    f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_					)} !="""
																									    f""" {type(lowerCamelCase_					)}."""					)
																			elif isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     [negative_prompt]
																			elif batch_size != len(lowerCamelCase_					):
																									raise ValueError(
																									    f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_					)}, but `prompt`:"""
																									    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																									    ''' the batch size of `prompt`.'''					)
																			else:
																									UpperCAmelCase__												=     negative_prompt

																			UpperCAmelCase__												=     text_input_ids.shape[-1]
																			UpperCAmelCase__												=     self.tokenizer(
																			    lowerCamelCase_		,			padding='''max_length'''		,			max_length=lowerCamelCase_		,			truncation=lowerCamelCase_		,			return_tensors='''pt'''		,			)

																			# if hasattr(uncond_input, "attention_mask"):
																			#     attention_mask = uncond_input.attention_mask.to(device)
																			# else:
																			#     attention_mask = None

																			UpperCAmelCase__												=     self.text_encoder(
																			    input_ids=uncond_input.input_ids.int().to(lowerCamelCase_					)		,			)
																			UpperCAmelCase__												=     uncond_embeddings[0]

													if do_classifier_free_guidance:
																			UpperCAmelCase__												=     uncond_embeddings.shape[1]
																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																			UpperCAmelCase__												=     uncond_embeddings.repeat(1		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     uncond_embeddings.reshape(batch_size * num_images_per_prompt		,			lowerCamelCase_		,			-1					)

																			# For classifier free guidance, we need to do two forward passes.
																			# Here we concatenate the unconditional and text embeddings into a single batch
																			# to avoid doing two forward passes
																			UpperCAmelCase__												=     np.concatenate([uncond_embeddings, prompt_embeds]					)

													return prompt_embeds