


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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							if not len(_SCREAMING_SNAKE_CASE				) == rows * cols:
														raise ValueError("""The specified number of rows and columns are not correct."""				)

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

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







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE="robotic cat with wings"				, _SCREAMING_SNAKE_CASE=7.5				, _SCREAMING_SNAKE_CASE=50				, _SCREAMING_SNAKE_CASE=1				, _SCREAMING_SNAKE_CASE=42				, )    ->				Dict:
							snake_case_           =		torch.Generator(pipeline.device				).manual_seed(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		pipeline(
							    _SCREAMING_SNAKE_CASE				, guidance_scale=_SCREAMING_SNAKE_CASE				, num_inference_steps=_SCREAMING_SNAKE_CASE				, generator=_SCREAMING_SNAKE_CASE				, num_images_per_prompt=_SCREAMING_SNAKE_CASE				, ).images
							snake_case_           =		int(math.sqrt(_SCREAMING_SNAKE_CASE				)				)
							snake_case_           =		image_grid(_SCREAMING_SNAKE_CASE				, rows=_rows				, cols=num_images_per_prompt // _rows				)
							return grid, images


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

__SCREAMING_SNAKE_CASE							:    str		=    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    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							:    Optional[Any]		=    load(args.pretrained_model_name_or_path, model=unet)
				unet.eval()
				setattr(pipeline, 'unet', unet)
else:
				__SCREAMING_SNAKE_CASE							:    Any		=    unet.to(torch.device('cuda', args.cuda_id))
__SCREAMING_SNAKE_CASE							:    Tuple		=    pipeline.to(unet.device)
__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE							:    List[str]		=    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							:    Dict		=    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)))







"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    str		=    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Any							      =    """mctct"""



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      List[Any]=8_065		,       UpperCAmelCase_							:      Tuple=1_536		,       UpperCAmelCase_							:      Optional[Any]=36		,       UpperCAmelCase_							:      int=6_144		,       UpperCAmelCase_							:      Dict=4		,       UpperCAmelCase_							:      Any=384		,       UpperCAmelCase_							:      List[str]=920		,       UpperCAmelCase_							:      Any=1E-5		,       UpperCAmelCase_							:      Any=0.3		,       UpperCAmelCase_							:      Tuple="relu"		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Dict=0.3		,       UpperCAmelCase_							:      str=0.3		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=0		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      str=1		,       UpperCAmelCase_							:      Tuple=0.3		,       UpperCAmelCase_							:      Optional[Any]=1		,       UpperCAmelCase_							:      Optional[Any]=(7,)		,       UpperCAmelCase_							:      Optional[Any]=(3,)		,       UpperCAmelCase_							:      List[str]=80		,       UpperCAmelCase_							:      Tuple=1		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[str]="sum"		,       UpperCAmelCase_							:      Union[str, Any]=False		,       **UpperCAmelCase_							:      Any		,       )					->Dict:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_		,       pad_token_id=UpperCAmelCase_		,       bos_token_id=UpperCAmelCase_		,       eos_token_id=UpperCAmelCase_       )
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		intermediate_size
														snake_case_           =		num_attention_heads
														snake_case_           =		attention_head_dim
														snake_case_           =		max_position_embeddings
														snake_case_           =		layer_norm_eps
														snake_case_           =		layerdrop
														snake_case_           =		hidden_act
														snake_case_           =		initializer_range
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		pad_token_id
														snake_case_           =		bos_token_id
														snake_case_           =		eos_token_id
														snake_case_           =		conv_glu_dim
														snake_case_           =		conv_dropout
														snake_case_           =		num_conv_layers
														snake_case_           =		input_feat_per_channel
														snake_case_           =		input_channels
														snake_case_           =		conv_channels
														snake_case_           =		ctc_loss_reduction
														snake_case_           =		ctc_zero_infinity

														# prevents config testing fail with exporting to json
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )

														if len(self.conv_kernel       ) != self.num_conv_layers:
																					raise ValueError(
																					    """Configuration for convolutional module is incorrect. """
																					    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
																					    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel       )}`, """
																					    F"""`config.num_conv_layers = {self.num_conv_layers}`."""       )



