"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

lowerCamelCase      : str       ='''\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'''






@dataclass
class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			42







class 							__snake_case(					A_	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       ):




         '''simple docstring'''



         super().__init__()

         self.register_modules(
             prior=A_					,       text_encoder=A_					,       tokenizer=A_					,       scheduler=A_					,       renderer=A_					,       )
    def 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if latents is None:
              __A					:					int								=		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					:					Union[str, Any]								=		latents.to(A_	)

         __A					:					Union[str, Any]								=		latents * scheduler.init_noise_sigma
         return latents
    def 						_a							(  self					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`'	)

         __A					:					int								=		torch.device(F'cuda:{gpu_id}'	)

         __A					:					Tuple								=		[self.text_encoder, self.prior]
         for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                   cpu_offload(A_					,       A_	)
    def 						_a							(  self					,       __lowerCamelCase=0	):




         '''simple docstring'''



         if is_accelerate_available() and is_accelerate_version('>='					,       '0.17.0.dev0'	):
              from accelerate import cpu_offload_with_hook
         else:
              raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'	)

         __A					:					Optional[int]								=		torch.device(F'cuda:{gpu_id}'	)

         if self.device.type != "cpu":
              self.to('cpu'					,       silence_dtype_warnings=A_	)
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         __A					:					Union[str, Any]								=		None
         for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
              __A					:					Optional[int]								=		cpu_offload_with_hook(A_					,       A_					,       prev_module_hook=A_	)

         if self.safety_checker is not None:
              __A					:					Optional[Any]								=		cpu_offload_with_hook(self.safety_checker					,       A_					,       prev_module_hook=A_	)

         # We'll offload the last model manually.
         __A					:					Any								=		hook
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         if self.device != torch.device('meta'	) or not hasattr(self.text_encoder					,       '_hf_hook'	):
              return self.device
         for module in self.text_encoder.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					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       ):




         '''simple docstring'''



         len(A_	) if isinstance(A_					,       A_	) else 1

         # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
         __A					:					Dict								=		0
         # get prompt text embeddings
         __A					:					Any								=		self.tokenizer(
             A_					,       padding='max_length'					,       max_length=self.tokenizer.model_max_length					,       truncation=A_					,       return_tensors='pt'					,       )
         __A					:					List[Any]								=		text_inputs.input_ids
         __A					:					Any								=		self.tokenizer(A_					,       padding='longest'					,       return_tensors='pt'	).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A_					,       A_	):
              __A					:					Any								=		self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]	)
              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					:					Any								=		self.text_encoder(text_input_ids.to(A_	)	)
         __A					:					Union[str, Any]								=		text_encoder_output.text_embeds

         __A					:					Optional[Any]								=		prompt_embeds.repeat_interleave(A_					,       dim=0	)
         # in Shap-E it normalize the prompt_embeds and then later rescale it
         __A					:					Any								=		prompt_embeds / torch.linalg.norm(A_					,       dim=-1					,       keepdim=A_	)

         if do_classifier_free_guidance:
              __A					:					Union[str, Any]								=		torch.zeros_like(A_	)

              # 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					:					Union[str, Any]								=		torch.cat([negative_prompt_embeds, prompt_embeds]	)

         # Rescale the features to have unit variance
         __A					:					Tuple								=		math.sqrt(prompt_embeds.shape[1]	) * prompt_embeds

         return prompt_embeds

    @torch.no_grad()
    @replace_example_docstring(A_	)
    def __call__(  self					,       __lowerCamelCase					,       __lowerCamelCase = 1					,       __lowerCamelCase = 25					,       __lowerCamelCase = None					,       __lowerCamelCase = None					,       __lowerCamelCase = 4.0					,       __lowerCamelCase = 64					,       __lowerCamelCase = "pil"					,       __lowerCamelCase = True					,       ):




         '''simple docstring'''



         if isinstance(A_					,       A_	):
              __A					:					Tuple								=		1
         elif isinstance(A_					,       A_	):
              __A					:					List[Any]								=		len(A_	)
         else:
              raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(A_	)}'	)

         __A					:					int								=		self._execution_device

         __A					:					int								=		batch_size * num_images_per_prompt

         __A					:					List[Any]								=		guidance_scale > 1.0
         __A					:					Tuple								=		self._encode_prompt(A_					,       A_					,       A_					,       A_	)

         # prior

         self.scheduler.set_timesteps(A_					,       device=A_	)
         __A					:					List[str]								=		self.scheduler.timesteps

         __A					:					Dict								=		self.prior.config.num_embeddings
         __A					:					int								=		self.prior.config.embedding_dim

         __A					:					Optional[Any]								=		self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim)					,       prompt_embeds.dtype					,       A_					,       A_					,       A_					,       self.scheduler					,       )

         # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
         __A					:					int								=		latents.reshape(latents.shape[0]					,       A_					,       A_	)

         for i, t in enumerate(self.progress_bar(A_	)	):
              # expand the latents if we are doing classifier free guidance
              __A					:					Any								=		torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
              __A					:					Optional[int]								=		self.scheduler.scale_model_input(A_					,       A_	)

              __A					:					Optional[int]								=		self.prior(
                  A_					,       timestep=A_					,       proj_embedding=A_					,       ).predicted_image_embedding

              # remove the variance
              __A					:					Dict								=		noise_pred.split(
                  scaled_model_input.shape[2]					,       dim=2	)  # batch_size, num_embeddings, embedding_dim

              if do_classifier_free_guidance is not None:
                   __A					:					int								=		noise_pred.chunk(2	)
                   __A					:					Any								=		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

              __A					:					str								=		self.scheduler.step(
                  A_					,       timestep=A_					,       sample=A_					,       ).prev_sample

         if output_type == "latent":
              return ShapEPipelineOutput(images=A_	)

         __A					:					Optional[Any]								=		[]
         for i, latent in enumerate(A_	):
              __A					:					Optional[int]								=		self.renderer.decode(
                  latent[None, :]					,       A_					,       size=A_					,       ray_batch_size=4096					,       n_coarse_samples=64					,       n_fine_samples=128					,       )
              images.append(A_	)

         __A					:					Any								=		torch.stack(A_	)

         if output_type not in ["np", "pil"]:
              raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'	)

         __A					:					List[Any]								=		images.cpu().numpy()

         if output_type == "pil":
              __A					:					Optional[Any]								=		[self.numpy_to_pil(A_	) for image in images]

         # Offload last model to CPU
         if hasattr(self					,       'final_offload_hook'	) and self.final_offload_hook is not None:
              self.final_offload_hook.offload()

         if not return_dict:
              return (images,)

         return ShapEPipelineOutput(images=A_	)






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     argparse.ArgumentParser(
         usage=(
             "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
             " refer to `run_eval.py -h` for the complete list."
         )					)
     parser.add_argument(
         "--search"		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""		,				)
     parser.add_argument(
         "--bs"		,				type=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_SNAKE_CASE		,				const=datetime_now()		,				help=(
             "add custom notes to be printed before the results table. If no value is passed, the current datetime"
             " string will be used."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     parse_search_arg(args.search					)
     _lowerCAmelCase :       Optional[Any]	   =     task_score_names[task]  # score cols first
     _lowerCAmelCase :       int	   =     {col: len(str(SCREAMING_SNAKE_CASE					)					) for col in col_names}
     _lowerCAmelCase :       Optional[int]	   =     []
     for r in matrix:
          _lowerCAmelCase :       List[str]	   =     dict((x.replace("--"		,				""					).split() for x in r)					)
          _lowerCAmelCase :       Any	   =     " ".join(SCREAMING_SNAKE_CASE					).split()
          args_exp.extend(["--bs", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
          _lowerCAmelCase :       List[str]	   =     args_normal + args_exp

          # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

          # find widest entries
          for k, v in result.items():
               _lowerCAmelCase :       Any	   =     len(str(SCREAMING_SNAKE_CASE					)					)
               if l > col_widths[k]:
                    _lowerCAmelCase :       str	   =     l

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_SNAKE_CASE					)
     print(" | ".join([f'''{col:{col_widths[col]}}''' for col in col_names]					)					)
     print(" | ".join([f'''{'-'*col_widths[col]}''' for col in col_names]					)					)
     for row in results_sorted:
          print(" | ".join([f'''{row[col]:{col_widths[col]}}''' for col in col_names]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--bs", str(args.bs					)]
     if args.info:
          print(f'''\nInfo: {args.info}'''					)
     print("\nBest score args:"					)
     print(" ".join(args_main + best_args + dyn_args					)					)

     return results_sorted


if __name__ == "__main__":
  # Usage:
  # [normal-run_eval_search.py cmd plus] \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  #
  # Example:
  # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
  # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
  # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  run_search()






