

"""simple docstring"""


import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__A						   =			get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class     snake_case	(				_snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  XLNetTokenizer
				SCREAMING_SNAKE_CASE_   :      int         =  XLNetTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      List[str]         =  True

				def 							lowercase_     (	self							:				int)->   Optional[int]:



						'''simple docstring'''


						super().setUp()

						# We have a SentencePiece fixture for testing
						__lowerCAmelCase:       Dict          =			XLNetTokenizer(_lowerCamelCase     ,  keep_accents=_lowerCamelCase)
						tokenizer.sanitize_special_tokens()
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				Dict)->   str:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			'''<s>'''
						__lowerCAmelCase:       Optional[Any]          =			1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase)     ,  _lowerCamelCase)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase)     ,  _lowerCamelCase)

				def 							lowercase_     (	self							:				int)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]     ,  "<unk>")
						self.assertEqual(vocab_keys[1]     ,  "<s>")
						self.assertEqual(vocab_keys[-1]     ,  "<eod>")
						self.assertEqual(len(_lowerCamelCase)     ,  1_0_0_6)

				def 							lowercase_     (	self							:				Any)->   Tuple:



						'''simple docstring'''


						self.assertEqual(self.get_tokenizer().vocab_size     ,  1_0_0_0)

				def 							lowercase_     (	self							:				Tuple)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			XLNetTokenizer(_lowerCamelCase     ,  keep_accents=_lowerCamelCase)

						__lowerCAmelCase:       Dict          =			tokenizer.tokenize("This is a test")
						self.assertListEqual(_lowerCamelCase     ,  ["▁This", "▁is", "▁a", "▁t", "est"])

						self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase)     ,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])

						__lowerCAmelCase:       Optional[int]          =			tokenizer.tokenize("I was born in 92000, and this is falsé.")
						self.assertListEqual(
						    _lowerCamelCase     ,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "é",
						        ".",
						    ]     ,  )
						__lowerCAmelCase:       Union[str, Any]          =			tokenizer.convert_tokens_to_ids(_lowerCamelCase)
						self.assertListEqual(_lowerCamelCase     ,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])

						__lowerCAmelCase:       int          =			tokenizer.convert_ids_to_tokens(_lowerCamelCase)
						self.assertListEqual(
						    _lowerCamelCase     ,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "<unk>",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "<unk>",
						        ".",
						    ]     ,  )

				def 							lowercase_     (	self							:				Any)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			XLNetTokenizer(_lowerCamelCase     ,  do_lower_case=_lowerCamelCase)
						__lowerCAmelCase:       Dict          =			tokenizer.tokenize("I was born in 92000, and this is falsé.")
						self.assertListEqual(
						    _lowerCamelCase     ,  [
						        SPIECE_UNDERLINE + "",
						        "i",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "se",
						        ".",
						    ]     ,  )
						self.assertListEqual(tokenizer.tokenize("H\u00E9llo")     ,  ["▁he", "ll", "o"])

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



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			XLNetTokenizer(_lowerCamelCase     ,  do_lower_case=_lowerCamelCase)
						__lowerCAmelCase:       Optional[int]          =			tokenizer.tokenize("I was born in 92000, and this is falsé.")
						self.assertListEqual(
						    _lowerCamelCase     ,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "se",
						        ".",
						    ]     ,  )

				@slow
				def 							lowercase_     (	self							:				List[str])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			XLNetTokenizer.from_pretrained("xlnet-base-cased")

						__lowerCAmelCase:       Optional[Any]          =			tokenizer.encode("sequence builders"     ,  add_special_tokens=_lowerCamelCase)
						__lowerCAmelCase:       Any          =			tokenizer.encode("multi-sequence build"     ,  add_special_tokens=_lowerCamelCase)

						__lowerCAmelCase:       Dict          =			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase)
						__lowerCAmelCase:       Any          =			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase     ,  _lowerCamelCase)

						assert encoded_sentence == text + [4, 3]
						assert encoded_pair == text + [4] + text_a + [4, 3]




				@slow
				def 							lowercase_     (	self							:				Dict)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			{'''input_ids''': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=_lowerCamelCase     ,  model_name="xlnet-base-cased"     ,  revision="c841166438c31ec7ca9a106dee7bb312b73ae511"     ,  )






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


snake_case				:    Any       				=     logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case				:    Union[str, Any]       				=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		List[str]=8						):




      """simple docstring"""





      a						:List[str] 							=       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      a						:int 							=       width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class 		_snake_case  (      _snake_case							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase					,  scheduler=_lowerCamelCase					,  movq=_lowerCamelCase					,  )
       a						:Any 							=       2 ** (len(self.movq.config.block_out_channels    ) - 1)




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if latents is None:
             a						:str 							=       randn_tensor(_lowerCamelCase					,  generator=_lowerCamelCase					,  device=_lowerCamelCase					,  dtype=_lowerCamelCase    )
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
             a						:Any 							=       latents.to(_lowerCamelCase    )

       a						:Dict 							=       latents * scheduler.init_noise_sigma
       return latents




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=0    ):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

       a						:int 							=       torch.device(F'''cuda:{gpu_id}'''    )

       a						:int 							=       [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(_lowerCamelCase					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=0    ):
       if is_accelerate_available() and is_accelerate_version('''>='''					,  '''0.17.0.dev0'''    ):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''    )

       a						:Any 							=       torch.device(F'''cuda:{gpu_id}'''    )

       if self.device.type != "cpu":
             self.to('''cpu'''					,  silence_dtype_warnings=_lowerCamelCase    )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       a						:Tuple 							=       None
       for cpu_offloaded_model in [self.unet, self.movq]:
             a							,				a						:List[str] 							=       cpu_offload_with_hook(_lowerCamelCase					,  _lowerCamelCase					,  prev_module_hook=_lowerCamelCase    )

       # We'll offload the last model manually.
       a						:str 							=       hook




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if not hasattr(self.unet					,  '''_hf_hook'''    ):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_lowerCamelCase					,  '''_hf_hook'''    )
                 and hasattr(module._hf_hook					,  '''execution_device'''    )
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device    )
       return self.device




 @torch.no_grad()
 @replace_example_docstring(_lowerCamelCase    )
 def __call__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase = 512					,  _lowerCamelCase = 512					,  _lowerCamelCase = 100					,  _lowerCamelCase = 4.0					,  _lowerCamelCase = 1					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = "pil"					,  _lowerCamelCase = True					,  ):
       a						:int 							=       self._execution_device

       a						:Optional[Any] 							=       guidance_scale > 1.0

       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Union[str, Any] 							=       torch.cat(_lowerCamelCase					,  dim=0    )
       a						:Any 							=       image_embeds.shape[0] * num_images_per_prompt
       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:List[str] 							=       torch.cat(_lowerCamelCase					,  dim=0    )

       if do_classifier_free_guidance:
             a						:Union[str, Any] 							=       image_embeds.repeat_interleave(_lowerCamelCase					,  dim=0    )
             a						:Optional[int] 							=       negative_image_embeds.repeat_interleave(_lowerCamelCase					,  dim=0    )

       a						:Optional[int] 							=       torch.cat([negative_image_embeds, image_embeds]					,  dim=0    ).to(dtype=self.unet.dtype					,  device=_lowerCamelCase    )

       self.scheduler.set_timesteps(_lowerCamelCase					,  device=_lowerCamelCase    )
       a						:Optional[Any] 							=       self.scheduler.timesteps

       a						:List[str] 							=       self.unet.config.in_channels

       a							,				a						:str 							=       downscale_height_and_width(_lowerCamelCase					,  _lowerCamelCase					,  self.movq_scale_factor    )

       # create initial latent
       a						:int 							=       self.prepare_latents(
           (batch_size, num_channels_latents, height, width)					,  image_embeds.dtype					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  self.scheduler					,  )

       for i, t in enumerate(self.progress_bar(_lowerCamelCase    )    ):
             # expand the latents if we are doing classifier free guidance
             a						:Tuple 							=       torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             a						:Union[str, Any] 							=       {'''image_embeds''': image_embeds}
             a						:Optional[Any] 							=       self.unet(
                 sample=_lowerCamelCase					,  timestep=_lowerCamelCase					,  encoder_hidden_states=_lowerCamelCase					,  added_cond_kwargs=_lowerCamelCase					,  return_dict=_lowerCamelCase					,  )[0]

             if do_classifier_free_guidance:
                   a							,				a						:Any 							=       noise_pred.split(latents.shape[1]					,  dim=1    )
                   a							,				a						:List[str] 							=       noise_pred.chunk(2    )
                   a							,				a						:int 							=       variance_pred.chunk(2    )
                   a						:List[Any] 							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   a						:Optional[int] 							=       torch.cat([noise_pred, variance_pred_text]					,  dim=1    )

             if not (
                 hasattr(self.scheduler.config					,  '''variance_type'''    )
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                   a							,				a						:Tuple 							=       noise_pred.split(latents.shape[1]					,  dim=1    )

             # compute the previous noisy sample x_t -> x_t-1
             a						:int 							=       self.scheduler.step(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  generator=_lowerCamelCase					,  )[0]
       # post-processing
       a						:int 							=       self.movq.decode(_lowerCamelCase					,  force_not_quantize=_lowerCamelCase    )['''sample''']

       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''    )

       if output_type in ["np", "pil"]:
             a						:str 							=       image * 0.5 + 0.5
             a						:List[Any] 							=       image.clamp(0					,  1    )
             a						:str 							=       image.cpu().permute(0					,  2					,  3					,  1    ).float().numpy()

       if output_type == "pil":
             a						:str 							=       self.numpy_to_pil(_lowerCamelCase    )

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase    )





