



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  __lowerCamelCase (       ):

				"""simple docstring"""


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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


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

				lowercase__							,							lowercase__					:   int													=					imgs[0].size
				lowercase__					:   Optional[Any]													=					Image.new("RGB"					,			size=(cols * w, rows * h)						)
				lowercase__							,							lowercase__					:   Optional[int]													=					grid.size

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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__="robotic cat with wings"					,			lowerCamelCase__=7.5					,			lowerCamelCase__=50					,			lowerCamelCase__=1					,			lowerCamelCase__=42					,			):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					torch.Generator(pipeline.device						).manual_seed(lowerCamelCase__						)
				lowercase__					:   str													=					pipeline(
				    lowerCamelCase__					,			guidance_scale=lowerCamelCase__					,			num_inference_steps=lowerCamelCase__					,			generator=lowerCamelCase__					,			num_images_per_prompt=lowerCamelCase__					,			).images
				lowercase__					:   Any													=					int(math.sqrt(lowerCamelCase__						)						)
				lowercase__					:   List[Any]													=					image_grid(lowerCamelCase__					,			rows=_rows					,			cols=num_images_per_prompt // _rows						)
				return grid, images


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

lowerCAmelCase__					  =  StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__					  =  lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
				lowerCAmelCase__					  =  load(args.pretrained_model_name_or_path, model=unet)
				unet.eval()
				setattr(pipeline, '''unet''', unet)
else:
				lowerCAmelCase__					  =  unet.to(torch.device('''cuda''', args.cuda_id))
lowerCAmelCase__					  =  pipeline.to(unet.device)
lowerCAmelCase__  ,						lowerCAmelCase__					  =  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()))))
lowerCAmelCase__					  =  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)))









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




