


"""simple docstring"""


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


a_											=     logging.get_logger(__name__)
class snake_case   :







		def __init__(       self   :							Union[str, Any]     , a__   :							List[str]     , a__   :							Union[str, Any]=21     , a__   :							List[Any]=[]     , a__   :							Optional[int]=None		)							->    Tuple:





						'''simple docstring'''







						_A       	=      model
						_A       	=      trace_batch_nums
						_A       	=      abort_after_batch_num

						# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
						_A       	=      collections.deque([]     , a__		)
						_A       	=      []
						_A       	=      0
						_A       	=      0
						_A       	=      False
						_A       	=      "                 "

						self.analyse_model()

						self.register_forward_hook()







		def 			a_			(       self   :							int     , a__   :							str=None		)							->    List[str]:





						'''simple docstring'''







						if frame is not None:
										self.expand_frame(a__		)
						self.frames.append("\n".join(self.frame		)		)
						_A       	=      []  # start a new frame







		def 			a_			(       self   :							Optional[int]     , a__   :							Tuple		)							->    List[Any]:





						'''simple docstring'''







						self.frame.append(a__		)







		def 			a_			(       self   :							Optional[int]		)							->    int:





						'''simple docstring'''







						print("\n".join(self.frames		)		)
						_A       	=      []







		def 			a_			(       self   :							Optional[int]		)							->    str:





						'''simple docstring'''







						_A       	=      []







		def 			a_			(       self   :							Dict		)							->    Tuple:





						'''simple docstring'''







						print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""		)
						print(F"""Last {len(self.frames		)} forward frames:"""		)
						print(F"""{'abs min':8} {'abs max':8} metadata"""		)
						print("\n".join(self.frames		)		)
						print("\n\n"		)
						_A       	=      []







		def 			a_			(       self   :							str		)							->    Dict:





						'''simple docstring'''







						_A       	=      {m: name for name, m in self.model.named_modules()}
						# self.longest_module_name = max(len(v) for v in self.module_names.values())







		def 			a_			(       self   :							Dict     , a__   :							Dict     , a__   :							List[Any]		)							->    Any:





						'''simple docstring'''







						if torch.is_tensor(a__		):
										self.expand_frame(get_abs_min_max(a__     , a__		)		)
										if detect_overflow(a__     , a__		):
														_A       	=      True
						elif var is None:
										self.expand_frame(F"""{'None':>17} {ctx}"""		)
						else:
										self.expand_frame(F"""{'not a tensor':>17} {ctx}"""		)







		def 			a_			(       self   :							str		)							->    Optional[int]:





						'''simple docstring'''







						self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""		)
						self.expand_frame(F"""{'abs min':8} {'abs max':8} metadata"""		)







		def 			a_			(       self   :							Union[str, Any]		)							->    Tuple:





						'''simple docstring'''







						self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""		)







		def 			a_			(       self   :							Optional[Any]     , a__   :							Dict     , a__   :							Any     , a__   :							Union[str, Any]		)							->    Union[str, Any]:





						'''simple docstring'''







						self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""		)

						# params
						for name, p in module.named_parameters(recurse=a__		):
										self.analyse_variable(a__     , a__		)

						# inputs
						if isinstance(a__     , a__		):
										for i, x in enumerate(a__		):
														self.analyse_variable(a__     , F"""input[{i}]"""		)
						else:
										self.analyse_variable(a__     , "input"		)

						# outputs
						if isinstance(a__     , a__		):
										for i, x in enumerate(a__		):
														# possibly a tuple of tuples
														if isinstance(a__     , a__		):
																		for j, y in enumerate(a__		):
																						self.analyse_variable(a__     , F"""output[{i}][{j}]"""		)
														else:
																		self.analyse_variable(a__     , F"""output[{i}]"""		)
						else:
										self.analyse_variable(a__     , "output"		)

						self.save_frame()







		def 			a_			(       self   :							Optional[int]		)							->    str:





						'''simple docstring'''







						self.model.apply(self._register_forward_hook		)







		def 			a_			(       self   :							int     , a__   :							Optional[int]		)							->    Dict:





						'''simple docstring'''







						module.register_forward_hook(self.forward_hook		)







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Any]     , a__   :							Optional[Any]     , a__   :							List[str]		)							->    List[str]:





						'''simple docstring'''







						_A       	=      False

						_A       	=      True if self.batch_number in self.trace_batch_nums else False
						if trace_mode:
										self.reset_saved_frames()

						if self.total_calls == 0:
										self.batch_start_frame()
						self.total_calls += 1

						# count batch numbers - the very first forward hook of the batch will be called when the
						# batch completes - i.e. it gets called very last - we know this batch has finished
						if module == self.model:
										self.batch_number += 1
										_A       	=      True

						self.create_frame(a__     , a__     , a__		)

						# if last_frame_of_batch:
						#     self.batch_end_frame()

						if trace_mode:
										self.trace_frames()

						if last_frame_of_batch:
										self.batch_start_frame()

						if self.detected_overflow and not trace_mode:
										self.dump_saved_frames()

										# now we can abort, as it's pointless to continue running
										raise ValueError(
										    "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
										    "Please scroll up above this traceback to see the activation values prior to this event."		)

						# abort after certain batch if requested to do so
						if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
										raise ValueError(
										    F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
										    F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""		)






def   a__			(					__lowercase  ,      __lowercase )						->						Dict:
				_A       	=      var.abs()
				return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""






def   a__			(					__lowercase  ,      __lowercase )						->						List[Any]:
				_A       	=      False
				if torch.isnan(__lowercase ).any().item():
								_A       	=      True
								print(f"""{ctx} has nans""" )
				if torch.isinf(__lowercase ).any().item():
								_A       	=      True
								print(f"""{ctx} has infs""" )

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								_A       	=      var[torch.ge(var.abs()  ,      100 )]
								if naaa.numel() > 0:
												print(f"""{ctx}:  n100={naaa.numel()}""" )
								_A       	=      var[torch.ge(var.abs()  ,      1000 )]
								if naaaa.numel() > 0:
												print(f"""{ctx}: n1000={naaaa.numel()}""" )
								_A       	=      var[torch.ge(var.abs()  ,      1_0000 )]
								if naaaaa.numel() > 0:
												print(f"""{ctx}: n10000={naaaaa.numel()}""" )

				if 0:
								print(f"""min={var.min():9.2e} max={var.max():9.2e}""" )

				if 0:
								print(f"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})""" )

				return detected




class snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      'underflow_overflow'
		__UpperCamelCase							     =      'tpu_metrics_debug'



"""simple docstring"""


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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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






def   a__			(					self  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						Tuple:
				_A       	=      image.to(device=__lowercase )
				_A       	=      image  # take image as image_embeddings
				_A       	=      image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				_A	,		_A	,		_A       	=      image_embeddings.shape
				_A       	=      image_embeddings.repeat(1  ,      __lowercase  ,      1 )
				_A       	=      image_embeddings.view(bs_embed * num_images_per_prompt  ,      __lowercase  ,      -1 )

				if do_classifier_free_guidance:
								_A       	=      torch.zeros_like(__lowercase )

								# 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
								_A       	=      torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings




class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Dict     , a__   :							PriorTransformer     , a__   :							CLIPTokenizer     , a__   :							CLIPTextModelWithProjection     , a__   :							UnCLIPScheduler     , a__   :							Optional[dict] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						super().__init__()

						_A       	=      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

						_A       	=      decoder_pipe_kwargs.get("torch_dtype"     , a__		) or prior.dtype

						_A       	=      StableDiffusionImageVariationPipeline.from_pretrained(
						    "lambdalabs/sd-image-variations-diffusers"     , **a__		)

						# replace `_encode_image` method
						_A       	=      types.MethodType(_encode_image     , self.decoder_pipe		)

						self.register_modules(
						    prior=a__     , tokenizer=a__     , text_encoder=a__     , prior_scheduler=a__     , )







		def 			a_			(       self   :							Optional[Any]     , a__   :							str     , a__   :							Optional[Any]     , a__   :							int     , a__   :							Optional[int]     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , )							->    Optional[Any]:





						'''simple docstring'''







						if text_model_output is None:
										_A       	=      len(a__		) if isinstance(a__     , a__		) else 1
										# get prompt text embeddings
										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , return_tensors="pt"     , )
										_A       	=      text_inputs.input_ids
										_A       	=      text_inputs.attention_mask.bool().to(a__		)

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_A       	=      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}"""		)
														_A       	=      text_input_ids[:, : self.tokenizer.model_max_length]

										_A       	=      self.text_encoder(text_input_ids.to(a__		)		)

										_A       	=      text_encoder_output.text_embeds
										_A       	=      text_encoder_output.last_hidden_state

						else:
										_A       	=      text_model_output[0].shape[0]
										_A	,		_A       	=      text_model_output[0], text_model_output[1]
										_A       	=      text_attention_mask

						_A       	=      text_embeddings.repeat_interleave(a__     , dim=0		)
						_A       	=      text_encoder_hidden_states.repeat_interleave(a__     , dim=0		)
						_A       	=      text_mask.repeat_interleave(a__     , dim=0		)

						if do_classifier_free_guidance:
										_A       	=      [""] * batch_size

										_A       	=      self.tokenizer(
										    a__     , padding="max_length"     , max_length=self.tokenizer.model_max_length     , truncation=a__     , return_tensors="pt"     , )
										_A       	=      uncond_input.attention_mask.bool().to(a__		)
										_A       	=      self.text_encoder(uncond_input.input_ids.to(a__		)		)

										_A       	=      uncond_embeddings_text_encoder_output.text_embeds
										_A       	=      uncond_embeddings_text_encoder_output.last_hidden_state

										# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

										_A       	=      uncond_embeddings.shape[1]
										_A       	=      uncond_embeddings.repeat(1     , a__		)
										_A       	=      uncond_embeddings.view(batch_size * num_images_per_prompt     , a__		)

										_A       	=      uncond_text_encoder_hidden_states.shape[1]
										_A       	=      uncond_text_encoder_hidden_states.repeat(1     , a__     , 1		)
										_A       	=      uncond_text_encoder_hidden_states.view(
										    batch_size * num_images_per_prompt     , a__     , -1		)
										_A       	=      uncond_text_mask.repeat_interleave(a__     , dim=0		)

										# done duplicates

										# 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
										_A       	=      torch.cat([uncond_embeddings, text_embeddings]		)
										_A       	=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

										_A       	=      torch.cat([uncond_text_mask, text_mask]		)

						return text_embeddings, text_encoder_hidden_states, text_mask







		@property
		def 			a_			(       self   :							int		)							->    Tuple:





						'''simple docstring'''







						if self.device != torch.device("meta"		) or not hasattr(self.prior     , "_hf_hook"		):
										return self.device
						for module in self.prior.modules():
										if (
										    hasattr(a__     , "_hf_hook"		)
										    and hasattr(module._hf_hook     , "execution_device"		)
										    and module._hf_hook.execution_device is not None
										):
														return torch.device(module._hf_hook.execution_device		)
						return self.device







		def 			a_			(       self   :							str     , a__   :							int     , a__   :							str     , a__   :							List[str]     , a__   :							Dict     , a__   :							Optional[Any]     , a__   :							Dict		)							->    int:





						'''simple docstring'''







						if latents is None:
										_A       	=      randn_tensor(a__     , generator=a__     , device=a__     , dtype=a__		)
						else:
										if latents.shape != shape:
														raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
										_A       	=      latents.to(a__		)

						_A       	=      latents * scheduler.init_noise_sigma
						return latents







		def 			a_			(       self   :							List[str]     , a__   :							Optional[Union[str, torch.device]] = None		)							->    Any:





						'''simple docstring'''







						self.decoder_pipe.to(a__		)
						super().to(a__		)







		@torch.no_grad()
		def __call__(       self   :							List[Any]     , a__   :							Optional[Union[str, List[str]]] = None     , a__   :							Optional[int] = None     , a__   :							Optional[int] = None     , a__   :							int = 1     , a__   :							int = 25     , a__   :							Optional[torch.Generator] = None     , a__   :							Optional[torch.FloatTensor] = None     , a__   :							Optional[Union[CLIPTextModelOutput, Tuple]] = None     , a__   :							Optional[torch.Tensor] = None     , a__   :							float = 4.0     , a__   :							float = 8.0     , a__   :							int = 50     , a__   :							Optional[int] = 1     , a__   :							float = 0.0     , a__   :							Optional[str] = "pil"     , a__   :							bool = True     , )							->    str:





						'''simple docstring'''







						if prompt is not None:
										if isinstance(a__     , a__		):
														_A       	=      1
										elif isinstance(a__     , a__		):
														_A       	=      len(a__		)
										else:
														raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(a__		)}"""		)
						else:
										_A       	=      text_model_output[0].shape[0]

						_A       	=      self._execution_device

						_A       	=      batch_size * num_images_per_prompt

						_A       	=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

						_A	,		_A	,		_A       	=      self._encode_prompt(
						    a__     , a__     , a__     , a__     , a__     , a__		)

						# prior

						self.prior_scheduler.set_timesteps(a__     , device=a__		)
						_A       	=      self.prior_scheduler.timesteps

						_A       	=      self.prior.config.embedding_dim

						_A       	=      self.prepare_latents(
						    (batch_size, embedding_dim)     , text_embeddings.dtype     , a__     , a__     , a__     , self.prior_scheduler     , )

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

										_A       	=      self.prior(
										    a__     , timestep=a__     , proj_embedding=a__     , encoder_hidden_states=a__     , attention_mask=a__     , ).predicted_image_embedding

										if do_classifier_free_guidance:
														_A	,		_A       	=      predicted_image_embedding.chunk(2		)
														_A       	=      predicted_image_embedding_uncond + prior_guidance_scale * (
														    predicted_image_embedding_text - predicted_image_embedding_uncond
														)

										if i + 1 == prior_timesteps_tensor.shape[0]:
														_A       	=      None
										else:
														_A       	=      prior_timesteps_tensor[i + 1]

										_A       	=      self.prior_scheduler.step(
										    a__     , timestep=a__     , sample=a__     , generator=a__     , prev_timestep=a__     , ).prev_sample

						_A       	=      self.prior.post_process_latents(a__		)

						_A       	=      prior_latents

						_A       	=      self.decoder_pipe(
						    image=a__     , height=a__     , width=a__     , num_inference_steps=a__     , guidance_scale=a__     , generator=a__     , output_type=a__     , return_dict=a__     , num_images_per_prompt=a__     , eta=a__     , )
						return output