
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	'time_series_transformer'
	a			:int         =	{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	    'num_hidden_layers': 'encoder_layers',
	}
	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			str = "student_t"       ,							SCREAMING_SNAKE_CASE_  :			str = "nll"       ,							SCREAMING_SNAKE_CASE_  :			int = 1       ,							SCREAMING_SNAKE_CASE_  :			List[int] = [1, 2, 3, 4, 5, 6, 7]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, bool]] = "mean"       ,							SCREAMING_SNAKE_CASE_  :			int = 0       ,							SCREAMING_SNAKE_CASE_  :			int = 0       ,							SCREAMING_SNAKE_CASE_  :			int = 0       ,							SCREAMING_SNAKE_CASE_  :			int = 0       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[int]] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[int]] = None       ,							SCREAMING_SNAKE_CASE_  :			int = 3_2       ,							SCREAMING_SNAKE_CASE_  :			int = 3_2       ,							SCREAMING_SNAKE_CASE_  :			int = 2       ,							SCREAMING_SNAKE_CASE_  :			int = 2       ,							SCREAMING_SNAKE_CASE_  :			int = 2       ,							SCREAMING_SNAKE_CASE_  :			int = 2       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							SCREAMING_SNAKE_CASE_  :			str = "gelu"       ,							SCREAMING_SNAKE_CASE_  :			int = 6_4       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			float = 0.1       ,							SCREAMING_SNAKE_CASE_  :			int = 1_0_0       ,							SCREAMING_SNAKE_CASE_  :			float = 0.02       ,							SCREAMING_SNAKE_CASE_  :			Dict=True       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					# time series specific configuration
					lowercase_       =					prediction_length
					lowercase_       =					context_length or prediction_length
					lowercase_       =					distribution_output
					lowercase_       =					loss
					lowercase_       =					input_size
					lowercase_       =					num_time_features
					lowercase_       =					lags_sequence
					lowercase_       =					scaling
					lowercase_       =					num_dynamic_real_features
					lowercase_       =					num_static_real_features
					lowercase_       =					num_static_categorical_features
					if cardinality and num_static_categorical_features > 0:
									if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
													raise ValueError(
													    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''       )
									lowercase_       =					cardinality
					else:
									lowercase_       =					[0]
					if embedding_dimension and num_static_categorical_features > 0:
									if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
													raise ValueError(
													    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''       )
									lowercase_       =					embedding_dimension
					else:
									lowercase_       =					[min(5_0       ,							(cat + 1) // 2       ) for cat in self.cardinality]
					lowercase_       =					num_parallel_samples

					# Transformer architecture configuration
					lowercase_       =					input_size * len(SCREAMING_SNAKE_CASE_       ) + self._number_of_features
					lowercase_       =					d_model
					lowercase_       =					encoder_attention_heads
					lowercase_       =					decoder_attention_heads
					lowercase_       =					encoder_ffn_dim
					lowercase_       =					decoder_ffn_dim
					lowercase_       =					encoder_layers
					lowercase_       =					decoder_layers

					lowercase_       =					dropout
					lowercase_       =					attention_dropout
					lowercase_       =					activation_dropout
					lowercase_       =					encoder_layerdrop
					lowercase_       =					decoder_layerdrop

					lowercase_       =					activation_function
					lowercase_       =					init_std

					lowercase_       =					use_cache

					super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	@property
	def 						_lowercase  (       self  :			str       )       ->     int:
					return (
					    sum(self.embedding_dimension       )
					    + self.num_dynamic_real_features
					    + self.num_time_features
					    + self.num_static_real_features
					    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
					)






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)))




