'''simple docstring'''


from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase	:       Tuple						       =	logging.get_logger(__name__)  # pylint: disable=invalid-name






class 						A   (   UpperCAmelCase       ,	UpperCAmelCase   ):





							@register_to_config
							def __init__(      self				:							str     ,		__a				:							bool     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[int] = None	)							->	Any:
								super().__init__()

								__UpperCAmelCase							=			learnable

								if self.learnable:
									assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
									assert length is not None, "learnable=True requires `length` to be set"

									__UpperCAmelCase							=			torch.zeros(__a     ,		__a	)
								else:
									__UpperCAmelCase							=			None

								__UpperCAmelCase							=			torch.nn.Parameter(__a	)






class 						A   (   UpperCAmelCase   ):
							a_        =      42
							a_        =      42
							a_        =      42
							a_        =      42
							a_        =      42
							a_        =      42





							def __init__(      self				:							List[Any]     ,		__a				:							VQModel     ,		__a				:							CLIPTextModel     ,		__a				:							CLIPTokenizer     ,		__a				:							TransformeraDModel     ,		__a				:							VQDiffusionScheduler     ,		__a				:							LearnedClassifierFreeSamplingEmbeddings     ,		)							->	Optional[int]:
								super().__init__()

								self.register_modules(
								    vqvae=__a     ,		transformer=__a     ,		text_encoder=__a     ,		tokenizer=__a     ,		scheduler=__a     ,		learned_classifier_free_sampling_embeddings=__a     ,		)





							def       snake_case__						(      self				:							List[str]     ,		__a				:							int     ,		__a				:							List[Any]     ,		__a				:							Optional[Any]	)							->	int:
								__UpperCAmelCase							=			len(__a	) if isinstance(__a     ,		__a	) else 1

								# get prompt text embeddings
								__UpperCAmelCase							=			self.tokenizer(
								    __a     ,		padding='''max_length'''     ,		max_length=self.tokenizer.model_max_length     ,		return_tensors='''pt'''     ,		)
								__UpperCAmelCase							=			text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									__UpperCAmelCase							=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
									logger.warning(
									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""	)
									__UpperCAmelCase							=			text_input_ids[:, : self.tokenizer.model_max_length]
								__UpperCAmelCase							=			self.text_encoder(text_input_ids.to(self.device	)	)[0]

								# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
								# While CLIP does normalize the pooled output of the text transformer when combining
								# the image and text embeddings, CLIP does not directly normalize the last hidden state.
								#
								# CLIP normalizing the pooled output.
								# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
								__UpperCAmelCase							=			prompt_embeds / prompt_embeds.norm(dim=-1     ,		keepdim=__a	)

								# duplicate text embeddings for each generation per prompt
								__UpperCAmelCase							=			prompt_embeds.repeat_interleave(__a     ,		dim=0	)

								if do_classifier_free_guidance:
									if self.learned_classifier_free_sampling_embeddings.learnable:
										__UpperCAmelCase							=			self.learned_classifier_free_sampling_embeddings.embeddings
										__UpperCAmelCase							=			negative_prompt_embeds.unsqueeze(0	).repeat(__a     ,		1     ,		1	)
									else:
										__UpperCAmelCase							=			[''''''] * batch_size

										__UpperCAmelCase							=			text_input_ids.shape[-1]
										__UpperCAmelCase							=			self.tokenizer(
										    __a     ,		padding='''max_length'''     ,		max_length=__a     ,		truncation=__a     ,		return_tensors='''pt'''     ,		)
										__UpperCAmelCase							=			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
										# See comment for normalizing text embeddings
										__UpperCAmelCase							=			negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1     ,		keepdim=__a	)

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									__UpperCAmelCase							=			negative_prompt_embeds.shape[1]
									__UpperCAmelCase							=			negative_prompt_embeds.repeat(1     ,		__a     ,		1	)
									__UpperCAmelCase							=			negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,		__a     ,		-1	)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									__UpperCAmelCase							=			torch.cat([negative_prompt_embeds, prompt_embeds]	)

								return prompt_embeds





							@torch.no_grad()
							def __call__(      self				:							Optional[Any]     ,		__a				:							Union[str, List[str]]     ,		__a				:							int = 1_0_0     ,		__a				:							float = 5.0     ,		__a				:							float = 1.0     ,		__a				:							int = 1     ,		__a				:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__a				:							Optional[torch.FloatTensor] = None     ,		__a				:							Optional[str] = "pil"     ,		__a				:							bool = True     ,		__a				:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,		__a				:							int = 1     ,		)							->	Union[ImagePipelineOutput, Tuple]:
								if isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			1
								elif isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			len(__a	)
								else:
									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(__a	)}"""	)

								__UpperCAmelCase							=			batch_size * num_images_per_prompt

								__UpperCAmelCase							=			guidance_scale > 1.0

								__UpperCAmelCase							=			self._encode_prompt(__a     ,		__a     ,		__a	)

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(__a     ,		__a	) or callback_steps <= 0)
								):
									raise ValueError(
									    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
									    f""" {type(__a	)}."""	)

								# get the initial completely masked latents unless the user supplied it

								__UpperCAmelCase							=			(batch_size, self.transformer.num_latent_pixels)
								if latents is None:
									__UpperCAmelCase							=			self.transformer.num_vector_embeds - 1
									__UpperCAmelCase							=			torch.full(__a     ,		__a	).to(self.device	)
								else:
									if latents.shape != latents_shape:
										raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
									if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
										raise ValueError(
										    '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
										    f""" {self.transformer.num_vector_embeds - 1} (inclusive)."""	)
									__UpperCAmelCase							=			latents.to(self.device	)

								# set timesteps
								self.scheduler.set_timesteps(__a     ,		device=self.device	)

								__UpperCAmelCase							=			self.scheduler.timesteps.to(self.device	)

								__UpperCAmelCase							=			latents

								for i, t in enumerate(self.progress_bar(__a	)	):
									# expand the sample if we are doing classifier free guidance
									__UpperCAmelCase							=			torch.cat([sample] * 2	) if do_classifier_free_guidance else sample

									# predict the un-noised image
									# model_output == `log_p_x_0`
									__UpperCAmelCase							=			self.transformer(__a     ,		encoder_hidden_states=__a     ,		timestep=__a	).sample

									if do_classifier_free_guidance:
										__UpperCAmelCase				,			__UpperCAmelCase							=			model_output.chunk(2	)
										__UpperCAmelCase							=			model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
										model_output -= torch.logsumexp(__a     ,		dim=1     ,		keepdim=__a	)

									__UpperCAmelCase							=			self.truncate(__a     ,		__a	)

									# remove `log(0)`'s (`-inf`s)
									__UpperCAmelCase							=			model_output.clamp(-7_0	)

									# compute the previous noisy sample x_t -> x_t-1
									__UpperCAmelCase							=			self.scheduler.step(__a     ,		timestep=__a     ,		sample=__a     ,		generator=__a	).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
										callback(__a     ,		__a     ,		__a	)

								__UpperCAmelCase							=			self.vqvae.config.vq_embed_dim
								__UpperCAmelCase							=			(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
								__UpperCAmelCase							=			self.vqvae.quantize.get_codebook_entry(__a     ,		shape=__a	)
								__UpperCAmelCase							=			self.vqvae.decode(__a     ,		force_not_quantize=__a	).sample

								__UpperCAmelCase							=			(image / 2 + 0.5).clamp(0     ,		1	)
								__UpperCAmelCase							=			image.cpu().permute(0     ,		2     ,		3     ,		1	).numpy()

								if output_type == "pil":
									__UpperCAmelCase							=			self.numpy_to_pil(__a	)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=__a	)






							def       snake_case__						(      self				:							List[Any]     ,		__a				:							torch.FloatTensor     ,		__a				:							float	)							->	torch.FloatTensor:
								__UpperCAmelCase				,			__UpperCAmelCase							=			torch.sort(__a     ,		1     ,		descending=__a	)
								__UpperCAmelCase							=			torch.exp(__a	)
								__UpperCAmelCase							=			sorted_p_x_0.cumsum(dim=1	) < truncation_rate

								# Ensure that at least the largest probability is not zeroed out
								__UpperCAmelCase							=			torch.full_like(keep_mask[:, 0:1, :]     ,		__a	)
								__UpperCAmelCase							=			torch.cat((all_true, keep_mask)     ,		dim=1	)
								__UpperCAmelCase							=			keep_mask[:, :-1, :]

								__UpperCAmelCase							=			keep_mask.gather(1     ,		indices.argsort(1	)	)

								__UpperCAmelCase							=			log_p_x_0.clone()

								__UpperCAmelCase							=			-torch.inf  # -inf = log(0)

								return rv

'''simple docstring'''


import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory





def       lowerCAmelCase					(		):


	"""simple docstring"""
	raise RuntimeError('''CUDA out of memory.'''       )






class 						A   (   nn.Module   ):





							def __init__(      self				:							Optional[Any]	)							->	int:
								super().__init__()
								__UpperCAmelCase							=			nn.Linear(3     ,		4	)
								__UpperCAmelCase							=			nn.BatchNormad(4	)
								__UpperCAmelCase							=			nn.Linear(4     ,		5	)






							def       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Optional[int]:
								return self.lineara(self.batchnorm(self.lineara(__a	)	)	)






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							Union[str, Any]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)





							def       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Optional[int]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()
									return batch_size, arga

								__UpperCAmelCase				,			__UpperCAmelCase							=			mock_training_loop_function('''hello'''	)
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)
								self.assertListEqual([bs, arga]     ,		[8, '''hello''']	)





							def       snake_case__						(      self				:							Any	)							->	int:
								@find_executable_batch_size(starting_batch_size=0	)
								def mock_training_loop_function(__a				:							Optional[int]	):
									pass

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''No executable batch size found, reached zero.'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Dict	):
									if batch_size > 0:
										raise_fake_out_of_memory()
									pass

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''No executable batch size found, reached zero.'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							int	):
									if batch_size != 8:
										raise raise_fake_out_of_memory()

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function(1_2_8     ,		'''hello'''     ,		'''world'''	)
									self.assertIn('''Batch size was passed into `f`'''     ,		cm.exception.args[0]	)
									self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Tuple	):
									raise ValueError('''Oops, we had an error!'''	)

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''Oops, we had an error!'''     ,		cm.exception.args[0]	)






							@require_cuda
							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			torch.cuda.memory_allocated()
								__UpperCAmelCase							=			ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()     ,		__a	)
								__UpperCAmelCase							=			release_memory(__a	)
								self.assertEqual(torch.cuda.memory_allocated()     ,		__a	)
