
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:List[str]         =	'layoutlmv3'
	def __init__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			str=5_0_2_6_5       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			Dict=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			int="gelu"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=1e-5       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			str=0       ,							SCREAMING_SNAKE_CASE_  :			List[str]=2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=1_0_2_4       ,							SCREAMING_SNAKE_CASE_  :			Tuple=1_2_8       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_2_8       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_2       ,							SCREAMING_SNAKE_CASE_  :			Dict=1_2_8       ,							SCREAMING_SNAKE_CASE_  :			Tuple=6_4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=True       ,							SCREAMING_SNAKE_CASE_  :			List[str]=True       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2_2_4       ,							SCREAMING_SNAKE_CASE_  :			List[str]=3       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_6       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=None       ,							**SCREAMING_SNAKE_CASE_  :			Tuple       ,							)       ->     str:
					super().__init__(
					    vocab_size=SCREAMING_SNAKE_CASE_       ,							hidden_size=SCREAMING_SNAKE_CASE_       ,							num_hidden_layers=SCREAMING_SNAKE_CASE_       ,							num_attention_heads=SCREAMING_SNAKE_CASE_       ,							intermediate_size=SCREAMING_SNAKE_CASE_       ,							hidden_act=SCREAMING_SNAKE_CASE_       ,							hidden_dropout_prob=SCREAMING_SNAKE_CASE_       ,							attention_probs_dropout_prob=SCREAMING_SNAKE_CASE_       ,							max_position_embeddings=SCREAMING_SNAKE_CASE_       ,							type_vocab_size=SCREAMING_SNAKE_CASE_       ,							initializer_range=SCREAMING_SNAKE_CASE_       ,							layer_norm_eps=SCREAMING_SNAKE_CASE_       ,							pad_token_id=SCREAMING_SNAKE_CASE_       ,							bos_token_id=SCREAMING_SNAKE_CASE_       ,							eos_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)
					lowercase_       =					max_ad_position_embeddings
					lowercase_       =					coordinate_size
					lowercase_       =					shape_size
					lowercase_       =					has_relative_attention_bias
					lowercase_       =					rel_pos_bins
					lowercase_       =					max_rel_pos
					lowercase_       =					has_spatial_attention_bias
					lowercase_       =					rel_ad_pos_bins
					lowercase_       =					max_rel_ad_pos
					lowercase_       =					text_embed
					lowercase_       =					visual_embed
					lowercase_       =					input_size
					lowercase_       =					num_channels
					lowercase_       =					patch_size
					lowercase_       =					classifier_dropout

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Tuple         =	version.parse('1.12'							)
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Mapping[str, Mapping[int, str]]:
					# The order of inputs is different for question answering and sequence classification
					if self.task in ["question-answering", "sequence-classification"]:
									return OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
									        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
									    ]       )
					else:
									return OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
									        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
									        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
									    ]       )
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     float:
					return 1e-5
	@property
	def 						_lowercase  (       self  :			Any       )       ->     int:
					return 1_2




	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			"ProcessorMixin"       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			int = -1       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional["TensorType"] = None       ,							SCREAMING_SNAKE_CASE_  :			int = 3       ,							SCREAMING_SNAKE_CASE_  :			int = 4_0       ,							SCREAMING_SNAKE_CASE_  :			int = 4_0       ,							)       ->     Mapping[str, Any]:
					setattr(processor.image_processor       ,							'''apply_ocr'''       ,							SCREAMING_SNAKE_CASE_       )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					lowercase_       =					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE_       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0       )
					# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
					lowercase_       =					processor.tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE_       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=SCREAMING_SNAKE_CASE_       )
					# Generate dummy inputs according to compute batch and sequence
					lowercase_       =					[[''' '''.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					lowercase_       =					[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					lowercase_       =					self._generate_dummy_images(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					dict(
					    processor(
					        SCREAMING_SNAKE_CASE_       ,							text=SCREAMING_SNAKE_CASE_       ,							boxes=SCREAMING_SNAKE_CASE_       ,							return_tensors=SCREAMING_SNAKE_CASE_       ,							)       )

					return inputs






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 				a						(							):


				'''simple docstring'''
				lowercase_       =					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=42						,   help='''Seed for random process.'''						,   )
				parser.add_argument(
				    '''-ci'''						,   '''--cuda_id'''						,   type=snake_case__						,   default=0						,   help='''cuda_id.'''						,   )
				lowercase_       =					parser.parse_args()
				return args




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Tuple						,   snake_case__:					Union[str, Any]					):


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

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

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




def 				a						(							snake_case__:					Tuple						,   snake_case__:					Union[str, Any]="robotic cat with wings"						,   snake_case__:					Union[str, Any]=7.5						,   snake_case__:					List[str]=50						,   snake_case__:					List[Any]=1						,   snake_case__:					Optional[int]=42						,   ):


				'''simple docstring'''
				lowercase_       =					torch.Generator(pipeline.device					).manual_seed(snake_case__					)
				lowercase_       =					pipeline(
				    snake_case__						,   guidance_scale=snake_case__						,   num_inference_steps=snake_case__						,   generator=snake_case__						,   num_images_per_prompt=snake_case__						,   ).images
				lowercase_       =					int(math.sqrt(snake_case__					)					)
				lowercase_       =					image_grid(snake_case__						,   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)))




