




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,
)


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

UpperCAmelCase__				     = "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Any=8				)    -> List[Any]:
							'''simple docstring'''


							__lowercase      =    height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__lowercase      =    width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __init__( self :     List[Any]				,							lowerCamelCase__ :     UNetaDConditionModel				,							lowerCamelCase__ :     DDPMScheduler				,							lowerCamelCase__ :     VQModel				,							)    ->						Any:
													"""simple docstring"""


													super().__init__()

													self.register_modules(
													    unet=lowerCamelCase__				,							scheduler=lowerCamelCase__				,							movq=lowerCamelCase__				,							)
													__lowercase      =    2 ** (len(self.movq.config.block_out_channels				) - 1)






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Tuple				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Any				)    ->						List[str]:
													"""simple docstring"""


													if latents is None:
																				__lowercase      =    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}'				)
																				__lowercase      =    latents.to(lowerCamelCase__				)

													__lowercase      =    latents * scheduler.init_noise_sigma
													return latents






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     int=0				)    ->						Optional[Any]:
													"""simple docstring"""


													if is_accelerate_available():
																				from accelerate import cpu_offload
													else:
																				raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

													__lowercase      =    torch.device(f'cuda:{gpu_id}'				)

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






						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     List[Any]=0				)    ->						Union[str, Any]:
													"""simple docstring"""


													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.'''				)

													__lowercase      =    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)

													__lowercase      =    None
													for cpu_offloaded_model in [self.unet, self.movq]:
																				__lowercase     ,     __lowercase      =    cpu_offload_with_hook(lowerCamelCase__				,							lowerCamelCase__				,							prev_module_hook=lowerCamelCase__				)

													# We'll offload the last model manually.
													__lowercase      =    hook






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[int]:
													"""simple docstring"""


													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 :     Union[str, Any]				,							lowerCamelCase__ :     Union[torch.FloatTensor, List[torch.FloatTensor]]				,							lowerCamelCase__ :     Union[torch.FloatTensor, List[torch.FloatTensor]]				,							lowerCamelCase__ :     torch.FloatTensor				,							lowerCamelCase__ :     int = 512				,							lowerCamelCase__ :     int = 512				,							lowerCamelCase__ :     int = 100				,							lowerCamelCase__ :     float = 4.0				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[torch.FloatTensor] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self._execution_device

													__lowercase      =    guidance_scale > 1.0

													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    torch.cat(lowerCamelCase__				,							dim=0				)
													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    torch.cat(lowerCamelCase__				,							dim=0				)
													if isinstance(lowerCamelCase__				,							lowerCamelCase__				):
																				__lowercase      =    torch.cat(lowerCamelCase__				,							dim=0				)

													__lowercase      =    image_embeds.shape[0] * num_images_per_prompt

													if do_classifier_free_guidance:
																				__lowercase      =    image_embeds.repeat_interleave(lowerCamelCase__				,							dim=0				)
																				__lowercase      =    negative_image_embeds.repeat_interleave(lowerCamelCase__				,							dim=0				)
																				__lowercase      =    hint.repeat_interleave(lowerCamelCase__				,							dim=0				)

													__lowercase      =    torch.cat([negative_image_embeds, image_embeds]				,							dim=0				).to(dtype=self.unet.dtype				,							device=lowerCamelCase__				)
													__lowercase      =    torch.cat([hint, hint]				,							dim=0				).to(dtype=self.unet.dtype				,							device=lowerCamelCase__				)
													self.scheduler.set_timesteps(lowerCamelCase__				,							device=lowerCamelCase__				)
													__lowercase      =    self.scheduler.timesteps

													__lowercase      =    self.movq.config.latent_channels

													__lowercase     ,     __lowercase      =    downscale_height_and_width(lowerCamelCase__				,							lowerCamelCase__				,							self.movq_scale_factor				)

													# create initial latent
													__lowercase      =    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
																				__lowercase      =    torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

																				__lowercase      =    {'''image_embeds''': image_embeds, '''hint''': hint}
																				__lowercase      =    self.unet(
																				    sample=lowerCamelCase__				,							timestep=lowerCamelCase__				,							encoder_hidden_states=lowerCamelCase__				,							added_cond_kwargs=lowerCamelCase__				,							return_dict=lowerCamelCase__				,							)[0]

																				if do_classifier_free_guidance:
																											__lowercase     ,     __lowercase      =    noise_pred.split(latents.shape[1]				,							dim=1				)
																											__lowercase     ,     __lowercase      =    noise_pred.chunk(2				)
																											__lowercase     ,     __lowercase      =    variance_pred.chunk(2				)
																											__lowercase      =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																											__lowercase      =    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"]
																				):
																											__lowercase     ,     __lowercase      =    noise_pred.split(latents.shape[1]				,							dim=1				)

																				# compute the previous noisy sample x_t -> x_t-1
																				__lowercase      =    self.scheduler.step(
																				    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				,							)[0]
													# post-processing
													__lowercase      =    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"]:
																				__lowercase      =    image * 0.5 + 0.5
																				__lowercase      =    image.clamp(0				,							1				)
																				__lowercase      =    image.cpu().permute(0				,							2				,							3				,							1				).float().numpy()

													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)












import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				     = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")







@require_sentencepiece
@require_tokenizers
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		GPTSwaTokenizer
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		True
						UpperCamelCase_	:       Optional[Any]       	=		False






						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													super().setUp()

													# We have a SentencePiece fixture for testing
													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				,							eos_token='''<unk>'''				,							bos_token='''<unk>'''				,							pad_token='''<unk>'''				)

													tokenizer.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Dict				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''This is a test'''
													__lowercase      =    '''This is a test'''
													return input_text, output_text






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''<s>'''
													__lowercase      =    1

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,							'''<unk>'''				)
													self.assertEqual(vocab_keys[1]				,							'''<s>'''				)
													self.assertEqual(vocab_keys[-1]				,							'''j'''				)
													self.assertEqual(len(lowerCamelCase__				)				,							2_000				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


													self.assertEqual(self.get_tokenizer().vocab_size				,							2_000				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)

													__lowercase      =    tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCamelCase__				,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)				,							[465, 287, 265, 631, 842]				)

													__lowercase      =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				,							)
													# fmt: on

													__lowercase      =    tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
													self.assertListEqual(
													    lowerCamelCase__				,							[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]				,							)

													__lowercase      =    tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				)
													# fmt: on






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)
													__lowercase      =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
													__lowercase      =    [
													    [465, 287, 265, 631, 842],
													    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
													]

													# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
													for text, expected_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertListEqual(tokenizer.encode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)

													# Test that decode_fast returns the input text
													for text, token_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertEqual(tokenizer.decode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    [
													    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
													    '''Hey there, how are you doing this fine day?''',
													    '''This is a text with a trailing spaces followed by a dot     .''',
													    '''Häj sväjs lillebrör! =)''',
													    '''Det är inget fel på Mr. Cool''',
													]

													# fmt: off
													__lowercase      =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
													# fmt: on
													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCamelCase__				,							model_name='''AI-Sweden/gpt-sw3-126m'''				,							sequences=lowerCamelCase__				,							)






