




'''simple docstring'''

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def 						__magic_name__       (							)						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       argparse.ArgumentParser()
   parser.add_argument(
       """-m"""							,	"""--pretrained_model_name_or_path"""							,	type=__UpperCAmelCase							,	default=__UpperCAmelCase							,	required=__UpperCAmelCase							,	help="""Path to pretrained model or model identifier from huggingface.co/models."""							,	)
   parser.add_argument(
       """-c"""							,	"""--caption"""							,	type=__UpperCAmelCase							,	default="""robotic cat with wings"""							,	help="""Text used to generate images."""							,	)
   parser.add_argument(
       """-n"""							,	"""--images_num"""							,	type=__UpperCAmelCase							,	default=4							,	help="""How much images to generate."""							,	)
   parser.add_argument(
       """-s"""							,	"""--seed"""							,	type=__UpperCAmelCase							,	default=42							,	help="""Seed for random process."""							,	)
   parser.add_argument(
       """-ci"""							,	"""--cuda_id"""							,	type=__UpperCAmelCase							,	default=0							,	help="""cuda_id."""							,	)
   __SCREAMING_SNAKE_CASE      							=       parser.parse_args()
   return args

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   if not len(__UpperCAmelCase      ) == rows * cols:
      raise ValueError("""The specified number of rows and columns are not correct."""      )

   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       imgs[0].size
   __SCREAMING_SNAKE_CASE      							=       Image.new("""RGB"""							,	size=(cols * w, rows * h)      )
   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       grid.size

   for i, img in enumerate(__UpperCAmelCase      ):
      grid.paste(__UpperCAmelCase							,	box=(i % cols * w, i // cols * h)      )
   return grid

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase="robotic cat with wings"							,	__UpperCAmelCase=7.5							,	__UpperCAmelCase=50							,	__UpperCAmelCase=1							,	__UpperCAmelCase=42							,	)						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       torch.Generator(pipeline.device      ).manual_seed(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       pipeline(
       __UpperCAmelCase							,	guidance_scale=__UpperCAmelCase							,	num_inference_steps=__UpperCAmelCase							,	generator=__UpperCAmelCase							,	num_images_per_prompt=__UpperCAmelCase							,	).images
   __SCREAMING_SNAKE_CASE      							=       int(math.sqrt(__UpperCAmelCase      )      )
   __SCREAMING_SNAKE_CASE      							=       image_grid(__UpperCAmelCase							,	rows=_rows							,	cols=num_images_per_prompt // _rows      )
   return grid, images


a        =						parse_args()
# Load models and create wrapper for stable diffusion
a        =						CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
a        =						CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
a        =						AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
a        =						UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

a        =						StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
a        =						lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
    a        =						load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, "unet", unet)
else:
    a        =						unet.to(torch.device("cuda", args.cuda_id))
a        =						pipeline.to(unet.device)
a  ,   a        =						generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
a        =						os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
    image.save(os.path.join(dirname, "{}.png".format(idx + 1)))












'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

a        =						logging.get_logger(__name__)







@add_end_docstrings(_snake_case			)
class __a (   _snake_case			):





   def __init__(					self				:			Union[str, Any]				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase    )

      requires_backends(self				,"""vision"""    )
      self.check_model_type(
          TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
          if self.framework == """tf"""
          else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





   def __call__(					self				:			Dict				,lowerCamelCase				:			Union[str, List[str], "Image", List["Image"]]				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      return super().__call__(lowerCamelCase				,**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,**lowerCamelCase				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       {}
      if "candidate_labels" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""candidate_labels"""]
      if "hypothesis_template" in kwargs:
         __SCREAMING_SNAKE_CASE      							=       kwargs["""hypothesis_template"""]

      return preprocess_params, {}, {}





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			List[Any]				,lowerCamelCase				:			Union[str, Any]=None				,lowerCamelCase				:			Union[str, Any]="This is a photo of {}."    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_image(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.image_processor(images=[image]				,return_tensors=self.framework    )
      __SCREAMING_SNAKE_CASE      							=       candidate_labels
      __SCREAMING_SNAKE_CASE      							=       [hypothesis_template.format(lowerCamelCase    ) for x in candidate_labels]
      __SCREAMING_SNAKE_CASE      							=       self.tokenizer(lowerCamelCase				,return_tensors=self.framework				,padding=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       [text_inputs]
      return inputs





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_inputs.pop("""text_inputs"""    )
      if isinstance(text_inputs[0]				,lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0]
      else:
         # Batching case.
         __SCREAMING_SNAKE_CASE      							=       text_inputs[0][0]

      __SCREAMING_SNAKE_CASE      							=       self.model(**lowerCamelCase				,**lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       {
          """candidate_labels""": candidate_labels,
          """logits""": outputs.logits_per_image,
      }
      return model_outputs





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       model_outputs.pop("""candidate_labels"""    )
      __SCREAMING_SNAKE_CASE      							=       model_outputs["""logits"""][0]
      if self.framework == "pt":
         __SCREAMING_SNAKE_CASE      							=       logits.softmax(dim=-1    ).squeeze(-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.tolist()
         if not isinstance(lowerCamelCase				,lowerCamelCase    ):
            __SCREAMING_SNAKE_CASE      							=       [scores]
      elif self.framework == "tf":
         __SCREAMING_SNAKE_CASE      							=       stable_softmax(lowerCamelCase				,axis=-1    )
         __SCREAMING_SNAKE_CASE      							=       probs.numpy().tolist()
      else:
         raise ValueError(f"""Unsupported framework: {self.framework}"""    )

      __SCREAMING_SNAKE_CASE      							=       [
          {"""score""": score, """label""": candidate_label}
          for score, candidate_label in sorted(zip(lowerCamelCase				,lowerCamelCase    )				,key=lambda lowerCamelCase    : -x[0]    )
      ]
      return result





