
import math

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


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'data2vec-audio'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Any=3_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			int=1_2       ,							SCREAMING_SNAKE_CASE_  :			Dict=1_2       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Dict="gelu"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.0       ,							SCREAMING_SNAKE_CASE_  :			int=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-5       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=(5, 2, 2, 2, 2, 2, 2)       ,							SCREAMING_SNAKE_CASE_  :			str=(1_0, 3, 3, 3, 3, 2, 2)       ,							SCREAMING_SNAKE_CASE_  :			Any=False       ,							SCREAMING_SNAKE_CASE_  :			Tuple=1_6       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=1_9       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.05       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_0       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.0       ,							SCREAMING_SNAKE_CASE_  :			str=1_0       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=0       ,							SCREAMING_SNAKE_CASE_  :			int="sum"       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=False       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			str=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)       ,							SCREAMING_SNAKE_CASE_  :			Any=(5, 3, 3, 1, 1)       ,							SCREAMING_SNAKE_CASE_  :			Any=(1, 2, 3, 1, 1)       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0       ,							SCREAMING_SNAKE_CASE_  :			str=1       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=3       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=2       ,							SCREAMING_SNAKE_CASE_  :			Any=3       ,							SCREAMING_SNAKE_CASE_  :			Any=None       ,							**SCREAMING_SNAKE_CASE_  :			Any       ,							)       ->     List[str]:
					super().__init__(**SCREAMING_SNAKE_CASE_       ,							pad_token_id=SCREAMING_SNAKE_CASE_       ,							bos_token_id=SCREAMING_SNAKE_CASE_       ,							eos_token_id=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					hidden_size
					lowercase_       =					feat_extract_activation
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					conv_bias
					lowercase_       =					num_conv_pos_embeddings
					lowercase_       =					num_conv_pos_embedding_groups
					lowercase_       =					conv_pos_kernel_size
					lowercase_       =					len(self.conv_dim       )
					lowercase_       =					num_hidden_layers
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_dropout
					lowercase_       =					attention_dropout
					lowercase_       =					activation_dropout
					lowercase_       =					feat_proj_dropout
					lowercase_       =					final_dropout
					lowercase_       =					layerdrop
					lowercase_       =					layer_norm_eps
					lowercase_       =					initializer_range
					lowercase_       =					vocab_size
					lowercase_       =					use_weighted_layer_sum

					if (
					    (len(self.conv_stride       ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim       ) != self.num_feat_extract_layers)
					):
									raise ValueError(
									    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
									    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
									    f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
									    f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					lowercase_       =					mask_time_prob
					lowercase_       =					mask_time_length
					lowercase_       =					mask_time_min_masks
					lowercase_       =					mask_feature_prob
					lowercase_       =					mask_feature_length
					lowercase_       =					mask_feature_min_masks

					# ctc loss
					lowercase_       =					ctc_loss_reduction
					lowercase_       =					ctc_zero_infinity

					# adapter
					lowercase_       =					add_adapter
					lowercase_       =					adapter_kernel_size
					lowercase_       =					adapter_stride
					lowercase_       =					num_adapter_layers
					lowercase_       =					output_hidden_size or hidden_size

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					lowercase_       =					classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					xvector_output_dim




	@property
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					return math.prod(self.conv_stride       )






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)))




