
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


SCREAMING_SNAKE_CASE__							=    getLogger(__name__)


SCREAMING_SNAKE_CASE__							=    4
SCREAMING_SNAKE_CASE__							=    7

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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						str:
				if isinstance(SCREAMING_SNAKE_CASE   ,							torch.Tensor      ):
								return image
				elif isinstance(SCREAMING_SNAKE_CASE   ,							PIL.Image.Image      ):
								__lowercase								= [image]

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

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

				return image
class      A__       (						lowerCAmelCase__    ):





							def __init__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       OnnxRuntimeModel					,			_UpperCAmelCase  :       OnnxRuntimeModel					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       OnnxRuntimeModel					,			_UpperCAmelCase  :       DDPMScheduler					,			_UpperCAmelCase  :       Any					,			_UpperCAmelCase  :       int = 3_50					,			)						->		List[Any]:



											"""simple docstring"""


											super().__init__(
											    vae=_UpperCAmelCase					,			text_encoder=_UpperCAmelCase					,			tokenizer=_UpperCAmelCase					,			unet=_UpperCAmelCase					,			low_res_scheduler=_UpperCAmelCase					,			scheduler=_UpperCAmelCase					,			safety_checker=_UpperCAmelCase					,			feature_extractor=_UpperCAmelCase					,			watermarker=_UpperCAmelCase					,			max_noise_level=_UpperCAmelCase					,			)





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



											"""simple docstring"""


											self.check_inputs(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

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

											__lowercase								= 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.
											__lowercase								= guidance_scale > 1.0

											# 3. Encode input prompt
											__lowercase								= self._encode_prompt(
											    _UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase					,			prompt_embeds=_UpperCAmelCase					,			negative_prompt_embeds=_UpperCAmelCase					,			)

											__lowercase								= ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

											# 4. Preprocess image
											__lowercase								= preprocess(_UpperCAmelCase    )
											__lowercase								= image.cpu()

											# 5. set timesteps
											self.scheduler.set_timesteps(_UpperCAmelCase					,			device=_UpperCAmelCase    )
											__lowercase								= self.scheduler.timesteps

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

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

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

											# 7. Check that sizes of image and latents match
											__lowercase								= 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
											__lowercase								= self.prepare_extra_step_kwargs(_UpperCAmelCase					,			_UpperCAmelCase    )

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

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

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

																			# timestep to tensor
																			__lowercase								= np.array([t]					,			dtype=_UpperCAmelCase    )

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

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

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

																			# call the callback, if provided
																			if i == len(_UpperCAmelCase    ) - 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(_UpperCAmelCase					,			_UpperCAmelCase					,			_UpperCAmelCase    )

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

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

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=_UpperCAmelCase    )





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       Optional[Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											__lowercase								= 1 / 0.08_333 * latents
											__lowercase								= self.vae(latent_sample=_UpperCAmelCase    )[0]
											__lowercase								= np.clip(image / 2 + 0.5					,			0					,			1    )
											__lowercase								= image.transpose((0, 2, 3, 1)    )
											return image





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       Union[str, List[str]]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       Optional[int]					,			_UpperCAmelCase  :       bool					,			_UpperCAmelCase  :       Optional[str]					,			_UpperCAmelCase  :       Optional[torch.FloatTensor] = None					,			_UpperCAmelCase  :       Optional[torch.FloatTensor] = None					,			)						->		Optional[int]:



											"""simple docstring"""


											if prompt is not None and isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= 1
											elif prompt is not None and isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= len(_UpperCAmelCase    )
											else:
															__lowercase								= prompt_embeds.shape[0]

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

															if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
															    _UpperCAmelCase					,			_UpperCAmelCase    ):
																			__lowercase								= 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
															__lowercase								= self.text_encoder(
															    input_ids=text_input_ids.int().to(_UpperCAmelCase    )					,			)
															__lowercase								= prompt_embeds[0]

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

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

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

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

															__lowercase								= self.text_encoder(
															    input_ids=uncond_input.input_ids.int().to(_UpperCAmelCase    )					,			)
															__lowercase								= uncond_embeddings[0]

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

											return prompt_embeds







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      A__       (						unittest.TestCase   ,						lowerCAmelCase__    ):





							def       a__			(			self  :       Any    )						->		Dict:



											"""simple docstring"""


											__lowercase								= load_tool('translation'    )
											self.tool.setup()
											__lowercase								= load_tool('translation'					,			remote=_UpperCAmelCase    )





							def       a__			(			self  :       int    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[int]:



											"""simple docstring"""


											__lowercase								= self.remote_tool('Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





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



											"""simple docstring"""


											__lowercase								= self.tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





							def       a__			(			self  :       Tuple    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= self.remote_tool(text='Hey, what\'s up?'					,			src_lang='English'					,			tgt_lang='French'    )
											self.assertEqual(_UpperCAmelCase					,			'- Hé, comment ça va?'    )





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



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											# There is a single output
											if len(self.tool.outputs    ) == 1:
															__lowercase								= [outputs]

											self.assertListEqual(output_types(_UpperCAmelCase    )					,			self.tool.outputs    )





							def       a__			(			self  :       List[str]    )						->		Any:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )

											for output, output_type in zip(_UpperCAmelCase					,			self.tool.outputs    ):
															__lowercase								= AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_UpperCAmelCase					,			_UpperCAmelCase    )    )





							def       a__			(			self  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= ['Hey, what\'s up?', 'English', 'Spanish']

											__lowercase								= []

											for _input, input_type in zip(_UpperCAmelCase					,			self.tool.inputs    ):
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																			_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
											__lowercase								= self.tool(*_UpperCAmelCase    )

											if not isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
															__lowercase								= [outputs]

											self.assertEqual(len(_UpperCAmelCase    )					,			len(self.tool.outputs    )    )





