
"""simple docstring"""







import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Tuple									=      dataset
			__lowerCAmelCase			:						List[Any]									=      process
			__lowerCAmelCase			:						List[str]									=      params

		def __len__(      self ):
			return len(self.dataset )







		def __getitem__(      self       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						str									=      self.dataset[i]
			__lowerCAmelCase			:						str									=      self.process(SCREAMING_SNAKE_CASE_       ,		**self.params )
			return processed




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None ):
			__lowerCAmelCase			:						Optional[Any]									=      loader
			__lowerCAmelCase			:						Optional[int]									=      infer
			__lowerCAmelCase			:						str									=      params
			if loader_batch_size == 1:
				# Let's spare some time by deactivating altogether
				__lowerCAmelCase			:						List[Any]									=      None
			__lowerCAmelCase			:						Tuple									=      loader_batch_size

			# Internal bookkeeping
			__lowerCAmelCase			:						Optional[Any]									=      None
			__lowerCAmelCase			:						List[Any]									=      None

		def __len__(      self ):
			return len(self.loader )

		def __iter__(      self ):
			__lowerCAmelCase			:						str									=      iter(self.loader )
			return self

		def 							__lowerCamelCase							(      self ):
			if isinstance(self._loader_batch_data       ,		torch.Tensor ):
				# Batch data is simple tensor, just fetch the slice
				__lowerCAmelCase			:						str									=      self._loader_batch_data[self._loader_batch_index]
			else:
				# Batch data is assumed to be BaseModelOutput (or dict)
				__lowerCAmelCase			:						str									=      {}
				for k, element in self._loader_batch_data.items():
					if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ ):
						# Convert ModelOutput to tuple first
						__lowerCAmelCase			:						Optional[int]									=      element.to_tuple()
						if isinstance(element[0]       ,		torch.Tensor ):
							__lowerCAmelCase			:						Dict									=      tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
						elif isinstance(element[0]       ,		np.ndarray ):
							__lowerCAmelCase			:						str									=      tuple(np.expand_dims(el[self._loader_batch_index]       ,		0 ) for el in element )
						continue
					if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ ):
						# Those are stored as lists of tensors so need specific unbatching.
						if isinstance(element[0]       ,		torch.Tensor ):
							__lowerCAmelCase			:						Optional[Any]									=      tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
						elif isinstance(element[0]       ,		np.ndarray ):
							__lowerCAmelCase			:						Dict									=      tuple(np.expand_dims(el[self._loader_batch_index]       ,		0 ) for el in element )
						continue
					if element is None:
						# This can happen for optional data that get passed around
						__lowerCAmelCase			:						Dict									=      None
					elif isinstance(element[self._loader_batch_index]       ,		torch.Tensor ):
						# Take correct batch data, but make it looked like batch_size=1
						# For compatibility with other methods within transformers

						__lowerCAmelCase			:						int									=      element[self._loader_batch_index].unsqueeze(0 )
					elif isinstance(element[self._loader_batch_index]       ,		np.ndarray ):
						# Take correct batch data, but make it looked like batch_size=1
						# For compatibility with other methods within transformers
						__lowerCAmelCase			:						int									=      np.expand_dims(element[self._loader_batch_index]       ,		0 )
					else:
						# This is typically a list, so no need to `unsqueeze`.
						__lowerCAmelCase			:						Any									=      element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
				__lowerCAmelCase			:						Union[str, Any]									=      self._loader_batch_data.__class__(SCREAMING_SNAKE_CASE_ )
			self._loader_batch_index += 1
			return result







		def 							__lowerCamelCase							(      self ):
			if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
				# We are currently unrolling a batch so we just need to return
				# the current item within a batch
				return self.loader_batch_item()

			# We're out of items within a batch
			__lowerCAmelCase			:						Optional[int]									=      next(self.iterator )
			__lowerCAmelCase			:						Any									=      self.infer(SCREAMING_SNAKE_CASE_       ,		**self.params )
			# We now have a batch of "inferred things".
			if self.loader_batch_size is not None:
				# Try to infer the size of the batch
				if isinstance(SCREAMING_SNAKE_CASE_       ,		torch.Tensor ):
					__lowerCAmelCase			:						Optional[Any]									=      processed
				else:
					__lowerCAmelCase			:						Optional[Any]									=      list(processed.keys() )[0]
					__lowerCAmelCase			:						Dict									=      processed[key]
				if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ ):
					__lowerCAmelCase			:						Union[str, Any]									=      len(SCREAMING_SNAKE_CASE_ )
				else:
					__lowerCAmelCase			:						Dict									=      first_tensor.shape[0]
				if 0 < observed_batch_size < self.loader_batch_size:
					# could be last batch so we can't unroll as many
					# elements.
					__lowerCAmelCase			:						Tuple									=      observed_batch_size
				# Setting internal index to unwrap the batch
				__lowerCAmelCase			:						List[Any]									=      processed
				__lowerCAmelCase			:						List[Any]									=      0
				return self.loader_batch_item()
			else:
				# We're not unrolling batches
				return processed




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None ):
			super().__init__(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ )

		def __iter__(      self ):
			__lowerCAmelCase			:						str									=      iter(self.loader )
			__lowerCAmelCase			:						Union[str, Any]									=      None
			return self







		def 							__lowerCamelCase							(      self ):
			if self.subiterator is None:
				__lowerCAmelCase			:						Optional[int]									=      self.infer(next(self.iterator )       ,		**self.params )
			try:
				# Try to return next item
				__lowerCAmelCase			:						Optional[int]									=      next(self.subiterator )
			except StopIteration:
				# When a preprocess iterator ends, we can start lookig at the next item
				# ChunkIterator will keep feeding until ALL elements of iterator
				# all have created their subiterator and have been iterating against.
				#
				# Another way to look at it, is we're basically flattening lists of lists
				# into a single list, but with generators
				__lowerCAmelCase			:						List[Any]									=      self.infer(next(self.iterator )       ,		**self.params )
				__lowerCAmelCase			:						List[str]									=      next(self.subiterator )
			return processed




class  A__       (	_lowerCamelCase):

		def __iter__(      self ):
			__lowerCAmelCase			:						List[Any]									=      iter(self.loader )
			return self







		def 							__lowerCamelCase							(      self ):
			# Extremely similar to PipelineIterator in its unpacking mechanism
			# BUT, we have an extra required item which is the presence of `is_last`
			# That is because everything is flattened by `PipelineChunkIterator` we
			# need to keep track of how to regroup here in the original `process`
			# boundaries so that `process` and `postprocess` see the same data.

			# This iterator accumulates items (possibly while unbatching) until it
			# its a `is_last` and then just passes it on to the caller.
			__lowerCAmelCase			:						List[str]									=      False
			__lowerCAmelCase			:						str									=      []
			if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
				while self._loader_batch_index < self.loader_batch_size:
					__lowerCAmelCase			:						Any									=      self.loader_batch_item()
					__lowerCAmelCase			:						Union[str, Any]									=      item.pop('is_last' )
					accumulator.append(SCREAMING_SNAKE_CASE_ )
					if is_last:
						return accumulator

			while not is_last:
				__lowerCAmelCase			:						Optional[Any]									=      self.infer(next(self.iterator )       ,		**self.params )
				if self.loader_batch_size is not None:
					if isinstance(SCREAMING_SNAKE_CASE_       ,		torch.Tensor ):
						__lowerCAmelCase			:						Optional[int]									=      processed
					else:
						__lowerCAmelCase			:						List[str]									=      list(processed.keys() )[0]
						__lowerCAmelCase			:						int									=      processed[key]
					if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ ):
						__lowerCAmelCase			:						Optional[int]									=      len(SCREAMING_SNAKE_CASE_ )
					else:
						__lowerCAmelCase			:						int									=      first_tensor.shape[0]
					if 0 < observed_batch_size < self.loader_batch_size:
						# could be last batch so we can't unroll as many
						# elements.
						__lowerCAmelCase			:						Union[str, Any]									=      observed_batch_size
					__lowerCAmelCase			:						Optional[int]									=      processed
					__lowerCAmelCase			:						List[Any]									=      0
					while self._loader_batch_index < self.loader_batch_size:
						__lowerCAmelCase			:						Dict									=      self.loader_batch_item()
						__lowerCAmelCase			:						List[Any]									=      item.pop('is_last' )
						accumulator.append(SCREAMING_SNAKE_CASE_ )
						if is_last:
							return accumulator
				else:
					__lowerCAmelCase			:						str									=      processed
					__lowerCAmelCase			:						int									=      item.pop('is_last' )
					accumulator.append(SCREAMING_SNAKE_CASE_ )
			return accumulator




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Dict									=      dataset
			__lowerCAmelCase			:						Dict									=      key

		def __len__(      self ):
			return len(self.dataset )







		def __getitem__(      self       ,		_SCREAMING_SNAKE_CASE ):
			return self.dataset[i][self.key]




class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Optional[Any]									=      dataset
			__lowerCAmelCase			:						Any									=      keya
			__lowerCAmelCase			:						Any									=      keya

		def __len__(      self ):
			return len(self.dataset )







		def __getitem__(      self       ,		_SCREAMING_SNAKE_CASE ):
			return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

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)))




