


"""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


UpperCAmelCase											=	getLogger(__name__)


UpperCAmelCase											=	4
UpperCAmelCase											=	7

UpperCAmelCase											=	{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}






def 					lowerCamelCase    (a_  :Any)   ->							Optional[int]:


       if isinstance(a_ , torch.Tensor):
              return image
       elif isinstance(a_ , PIL.Image.Image):
              lowercase						:List[str]											= [image]

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

              lowercase						:List[Any]											= [np.array(i.resize((w, h)))[None, :] for i in image]
              lowercase						:Dict											= np.concatenate(a_ , axis=0)
              lowercase						:Any											= np.array(a_).astype(np.floataa) / 2_55.0
              lowercase						:Any											= image.transpose(0 , 3 , 1 , 2)
              lowercase						:List[Any]											= 2.0 * image - 1.0
              lowercase						:str											= torch.from_numpy(a_)
       elif isinstance(image[0] , torch.Tensor):
              lowercase						:Union[str, Any]											= torch.cat(a_ , dim=0)

       return image




class     __magic_name__						(	__UpperCAmelCase   ):






       def __init__(      self     :			Union[str, Any]     ,   snake_case__     :			List[Any]     ,   snake_case__     :			int     ,   snake_case__     :			List[str]     ,   snake_case__     :			Optional[int]     ,   snake_case__     :			Union[str, Any]     ,   snake_case__     :			int     ,   snake_case__     :			Optional[int] = 3_5_0     ,   ):

              '''simple docstring'''






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






       def __call__(      self     :			List[str]     ,   snake_case__     :			List[str]     ,   snake_case__     :			Tuple     ,   snake_case__     :			List[str] = 7_5     ,   snake_case__     :			str = 9.0     ,   snake_case__     :			Optional[Any] = 2_0     ,   snake_case__     :			Union[str, Any] = None     ,   snake_case__     :			Tuple = 1     ,   snake_case__     :			Dict = 0.0     ,   snake_case__     :			Any = None     ,   snake_case__     :			int = None     ,   snake_case__     :			Union[str, Any] = None     ,   snake_case__     :			List[str] = None     ,   snake_case__     :			Optional[Any] = "pil"     ,   snake_case__     :			List[Any] = True     ,   snake_case__     :			Optional[int] = None     ,   snake_case__     :			List[Any] = 1     ,   ):

              '''simple docstring'''






              self.check_inputs(__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase )

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

              lowercase						:List[Any]											= 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						:int											= guidance_scale > 1.0

              # 3. Encode input prompt
              lowercase						:Optional[int]											= self._encode_prompt(
                  __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   prompt_embeds=__lowercase     ,   negative_prompt_embeds=__lowercase     ,   )

              lowercase						:str											= ORT_TO_PT_TYPE[str(text_embeddings.dtype )]

              # 4. Preprocess image
              lowercase						:List[str]											= preprocess(__lowercase )
              lowercase						:List[str]											= image.cpu()

              # 5. set timesteps
              self.scheduler.set_timesteps(__lowercase     ,   device=__lowercase )
              lowercase						:int											= self.scheduler.timesteps

              # 5. Add noise to image
              lowercase						:Optional[Any]											= torch.tensor([noise_level]     ,   dtype=torch.long     ,   device=__lowercase )
              lowercase						:List[Any]											= torch.randn(image.shape     ,   generator=__lowercase     ,   device=__lowercase     ,   dtype=__lowercase )
              lowercase						:Tuple											= self.low_res_scheduler.add_noise(__lowercase     ,   __lowercase     ,   __lowercase )

              lowercase						:Dict											= 2 if do_classifier_free_guidance else 1
              lowercase						:Any											= np.concatenate([image] * batch_multiplier * num_images_per_prompt )
              lowercase						:str											= np.concatenate([noise_level] * image.shape[0] )

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

              # 7. Check that sizes of image and latents match
              lowercase						:List[str]											= 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						:Dict											= self.prepare_extra_step_kwargs(__lowercase     ,   __lowercase )

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

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

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

                            # timestep to tensor
                            lowercase						:Optional[int]											= np.array([t]     ,   dtype=__lowercase )

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

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

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

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

        # 10. Post-processing
              lowercase						:Dict											= self.decode_latents(latents.float() )

              # 11. Convert to PIL
              if output_type == "pil":
                     lowercase						:List[str]											= self.numpy_to_pil(__lowercase )

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__lowercase )






       def        __snake_case  (      self     :			int     ,   snake_case__     :			str ):

              '''simple docstring'''






              lowercase						:Dict											= 1 / 0.0_83_33 * latents
              lowercase						:List[Any]											= self.vae(latent_sample=__lowercase )[0]
              lowercase						:List[Any]											= np.clip(image / 2 + 0.5     ,   0     ,   1 )
              lowercase						:List[Any]											= image.transpose((0, 2, 3, 1) )
              return image







       def        __snake_case  (      self     :			int     ,   snake_case__     :			Optional[Any]     ,   snake_case__     :			Union[str, Any]     ,   snake_case__     :			Optional[Any]     ,   snake_case__     :			Union[str, Any]     ,   snake_case__     :			Any     ,   snake_case__     :			Union[str, Any] = None     ,   snake_case__     :			Tuple = None     ,   ):

              '''simple docstring'''






              if prompt is not None and isinstance(__lowercase     ,   __lowercase ):
                     lowercase						:Tuple											= 1
              elif prompt is not None and isinstance(__lowercase     ,   __lowercase ):
                     lowercase						:Union[str, Any]											= len(__lowercase )
              else:
                     lowercase						:Any											= prompt_embeds.shape[0]

              if prompt_embeds is None:
                     lowercase						:Dict											= self.tokenizer(
                         __lowercase     ,   padding='''max_length'''     ,   max_length=self.tokenizer.model_max_length     ,   truncation=__lowercase     ,   return_tensors='''pt'''     ,   )
                     lowercase						:List[str]											= text_inputs.input_ids
                     lowercase						:Tuple											= self.tokenizer(__lowercase     ,   padding='''longest'''     ,   return_tensors='''pt''' ).input_ids

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

              lowercase						:Optional[Any]											= prompt_embeds.shape
              # duplicate text embeddings for each generation per prompt, using mps friendly method
              lowercase						:str											= prompt_embeds.repeat(1     ,   __lowercase )
              lowercase						:Optional[int]											= prompt_embeds.reshape(bs_embed * num_images_per_prompt     ,   __lowercase     ,   -1 )

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

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

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

                     lowercase						:Dict											= self.text_encoder(
                         input_ids=uncond_input.input_ids.int().to(__lowercase )     ,   )
                     lowercase						:Optional[Any]											= uncond_embeddings[0]

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

              return prompt_embeds







import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
UpperCamelCase__              =						'src/diffusers'
UpperCamelCase__              =						'docs/source/en'
UpperCamelCase__              =						'.'






def 					snake_case					(							A						,   A						,   A			)							->							int:




	'''simple docstring'''


	with open(A						,   'r'						,   encoding='utf-8'						,   newline='\n'			) as f:
		lowerCamelCase_							:		int							   =							f.readlines()
	# Find the start prompt.
	lowerCamelCase_							:		Tuple							   =							0
	while not lines[start_index].startswith(A			):
		start_index += 1
	start_index += 1

	lowerCamelCase_							:		Tuple							   =							start_index
	while not lines[end_index].startswith(A			):
		end_index += 1
	end_index -= 1

	while len(lines[start_index]			) <= 1:
		start_index += 1
	while len(lines[end_index]			) <= 1:
		end_index -= 1
	end_index += 1
	return "".join(lines[start_index:end_index]			), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
UpperCamelCase__              =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__              =						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
UpperCamelCase__              =						re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
UpperCamelCase__              =						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase__              =						importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
UpperCamelCase__              =						spec.loader.load_module()






def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,   A			)
	return [m.group(0			) for m in matches]






def 					snake_case					(							A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							2 if text == '✅' or text == '❌' else len(A			)
	lowerCamelCase_							:		List[str]							   =							(width - text_length) // 2
	lowerCamelCase_							:		int							   =							width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 					snake_case					(							)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	lowerCamelCase_							:		Optional[Any]							   =							{
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	lowerCamelCase_							:		Optional[int]							   =							{name: config.replace('ConfigMixin'						,   ''			) for name, config in model_name_to_config.items()}

	# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
	lowerCamelCase_							:		Tuple							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Any							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		int							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Optional[int]							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Dict							   =							collections.defaultdict(A			)

	# Let's lookup through all diffusers object (once).
	for attr_name in dir(A			):
		lowerCamelCase_							:		Tuple							   =							None
		if attr_name.endswith('Tokenizer'			):
			lowerCamelCase_							:		Union[str, Any]							   =							slow_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-9]
		elif attr_name.endswith('TokenizerFast'			):
			lowerCamelCase_							:		Any							   =							fast_tokenizers
			lowerCamelCase_							:		Optional[Any]							   =							attr_name[:-13]
		elif _re_tf_models.match(A			) is not None:
			lowerCamelCase_							:		Tuple							   =							tf_models
			lowerCamelCase_							:		Optional[int]							   =							_re_tf_models.match(A			).groups()[0]
		elif _re_flax_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							flax_models
			lowerCamelCase_							:		Union[str, Any]							   =							_re_flax_models.match(A			).groups()[0]
		elif _re_pt_models.match(A			) is not None:
			lowerCamelCase_							:		Union[str, Any]							   =							pt_models
			lowerCamelCase_							:		Optional[int]							   =							_re_pt_models.match(A			).groups()[0]

		if lookup_dict is not None:
			while len(A			) > 0:
				if attr_name in model_name_to_prefix.values():
					lowerCamelCase_							:		List[Any]							   =							True
					break
				# Try again after removing the last word in the name
				lowerCamelCase_							:		int							   =							''.join(camel_case_split(A			)[:-1]			)

    # Let's build that table!
	lowerCamelCase_							:		Optional[int]							   =							list(model_name_to_config.keys()			)
	model_names.sort(key=str.lower			)
	lowerCamelCase_							:		str							   =							['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
	# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
	lowerCamelCase_							:		str							   =							[len(A			) + 2 for c in columns]
	lowerCamelCase_							:		Optional[int]							   =							max([len(A			) for name in model_names]			) + 2

	# Build the table per se
	lowerCamelCase_							:		Optional[Any]							   =							'|' + '|'.join([_center_text(A						,   A			) for c, w in zip(A						,   A			)]			) + '|\n'
	# Use ":-----:" format to center-aligned table cell texts
	table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]			) + "|\n"

	lowerCamelCase_							:		str							   =							{True: '✅', False: '❌'}
	for name in model_names:
		lowerCamelCase_							:		List[str]							   =							model_name_to_prefix[name]
		lowerCamelCase_							:		Tuple							   =							[
		    name,
		    check[slow_tokenizers[prefix]],
		    check[fast_tokenizers[prefix]],
		    check[pt_models[prefix]],
		    check[tf_models[prefix]],
		    check[flax_models[prefix]],
		]
		table += "|" + "|".join([_center_text(A						,   A			) for l, w in zip(A						,   A			)]			) + "|\n"
	return table






def 					snake_case					(							A=False			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							_find_text_in_file(
	    filename=os.path.join(A						,   'index.mdx'			)						,   start_prompt='<!--This table is updated automatically from the auto modules'						,   end_prompt='<!-- End table-->'						,   )
	lowerCamelCase_							:		Any							   =							get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(A						,   'index.mdx'			)						,   'w'						,   encoding='utf-8'						,   newline='\n'			) as f:
				f.writelines(lines[:start_index] + [new_table] + lines[end_index:]			)
		else:
			raise ValueError(
			    'The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this.'			)


if __name__ == "__main__":
							UpperCamelCase__              =						argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							UpperCamelCase__              =						parser.parse_args()

							check_model_table(args.fix_and_overwrite)
