






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      :			Tuple							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			Dict							=		{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''big_bird'''







				def __init__(						self       ,					__lowerCAmelCase=5_0_3_5_8       ,					__lowerCAmelCase=7_6_8       ,					__lowerCAmelCase=1_2       ,					__lowerCAmelCase=1_2       ,					__lowerCAmelCase=3_0_7_2       ,					__lowerCAmelCase="gelu_new"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=4_0_9_6       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=1E-12       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=0       ,					__lowerCAmelCase=1       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=6_6       ,					__lowerCAmelCase="block_sparse"       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=False       ,					__lowerCAmelCase=6_4       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=None       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								super().__init__(
								    pad_token_id=__lowerCAmelCase       ,					bos_token_id=__lowerCAmelCase       ,					eos_token_id=__lowerCAmelCase       ,					sep_token_id=__lowerCAmelCase       ,					**__lowerCAmelCase       ,					)

								__magic_name__		:Any        =      vocab_size
								__magic_name__		:List[Any]        =      max_position_embeddings
								__magic_name__		:Any        =      hidden_size
								__magic_name__		:List[Any]        =      num_hidden_layers
								__magic_name__		:Tuple        =      num_attention_heads
								__magic_name__		:List[Any]        =      intermediate_size
								__magic_name__		:Optional[int]        =      hidden_act
								__magic_name__		:int        =      hidden_dropout_prob
								__magic_name__		:int        =      attention_probs_dropout_prob
								__magic_name__		:Tuple        =      initializer_range
								__magic_name__		:str        =      type_vocab_size
								__magic_name__		:List[str]        =      layer_norm_eps
								__magic_name__		:Optional[Any]        =      use_cache

								__magic_name__		:Dict        =      rescale_embeddings
								__magic_name__		:Optional[Any]        =      attention_type
								__magic_name__		:Tuple        =      use_bias
								__magic_name__		:Dict        =      block_size
								__magic_name__		:List[Any]        =      num_random_blocks
								__magic_name__		:List[str]        =      classifier_dropout







class 						lowerCamelCase_				(      lowerCamelCase ):







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								if self.task == "multiple-choice":
												__magic_name__		:Union[str, Any]        =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
												__magic_name__		:List[Any]        =      {0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								    ]     )














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      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      argparse.ArgumentParser()
				parser.add_argument(
				    '''-m''',  '''--pretrained_model_name_or_path''',  type=snake_case,  default=snake_case,  required=snake_case,  help='''Path to pretrained model or model identifier from huggingface.co/models.''',  )
				parser.add_argument(
				    '''-c''',  '''--caption''',  type=snake_case,  default='''robotic cat with wings''',  help='''Text used to generate images.''',  )
				parser.add_argument(
				    '''-n''',  '''--images_num''',  type=snake_case,  default=4,  help='''How much images to generate.''',  )
				parser.add_argument(
				    '''-s''',  '''--seed''',  type=snake_case,  default=4_2,  help='''Seed for random process.''',  )
				parser.add_argument(
				    '''-ci''',  '''--cuda_id''',  type=snake_case,  default=0,  help='''cuda_id.''',  )
				__magic_name__		:Optional[Any]        =      parser.parse_args()
				return args

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

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

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






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


SCREAMING_SNAKE_CASE__      :			Any							=		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__      :			int							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE__      :			str							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE__      :			int							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__      :			str							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					SCREAMING_SNAKE_CASE__      :			str							=		load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					SCREAMING_SNAKE_CASE__      :			Dict							=		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE__      :			Any							=		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		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()))))
SCREAMING_SNAKE_CASE__      :			int							=		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)))






