






'''simple docstring'''



import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =ShapEPipeline
      __a	       =["prompt"]
      __a	       =["prompt"]
      __a	       =[
          "num_images_per_prompt",
          "num_inference_steps",
          "generator",
          "latents",
          "guidance_scale",
          "frame_size",
          "output_type",
          "return_dict",
      ]
      __a	       =False




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         return 32




      @property
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         return 32




      @property
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         return self.time_input_dim * 4




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return 8




      @property
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
         return tokenizer




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         torch.manual_seed(0   )
         __a					  =     CLIPTextConfig(
             bos_token_id=0			, eos_token_id=2			, hidden_size=self.text_embedder_hidden_size			, projection_dim=self.text_embedder_hidden_size			, intermediate_size=37			, layer_norm_eps=1e-05			, num_attention_heads=4			, num_hidden_layers=5			, pad_token_id=1			, vocab_size=1000			, )
         return CLIPTextModelWithProjection(lowerCamelCase   )




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         torch.manual_seed(0   )

         __a					  =     {
             'num_attention_heads': 2,
             'attention_head_dim': 16,
             'embedding_dim': self.time_input_dim,
             'num_embeddings': 32,
             'embedding_proj_dim': self.text_embedder_hidden_size,
             'time_embed_dim': self.time_embed_dim,
             'num_layers': 1,
             'clip_embed_dim': self.time_input_dim * 2,
             'additional_embeddings': 0,
             'time_embed_act_fn': 'gelu',
             'norm_in_type': 'layer',
             'encoder_hid_proj_type': None,
             'added_emb_type': None,
         }

         __a					  =     PriorTransformer(**lowerCamelCase   )
         return model




      @property
      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''simple docstring'''
         torch.manual_seed(0   )

         __a					  =     {
             'param_shapes': (
                 (self.renderer_dim, 93),
                 (self.renderer_dim, 8),
                 (self.renderer_dim, 8),
                 (self.renderer_dim, 8),
             ),
             'd_latent': self.time_input_dim,
             'd_hidden': self.renderer_dim,
             'n_output': 12,
             'background': (
                 0.1,
                 0.1,
                 0.1,
             ),
         }
         __a					  =     ShapERenderer(**lowerCamelCase   )
         return model




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     self.dummy_prior
         __a					  =     self.dummy_text_encoder
         __a					  =     self.dummy_tokenizer
         __a					  =     self.dummy_renderer

         __a					  =     HeunDiscreteScheduler(
             beta_schedule='exp'			, num_train_timesteps=1024			, prediction_type='sample'			, use_karras_sigmas=lowerCamelCase			, clip_sample=lowerCamelCase			, clip_sample_range=1.0			, )
         __a					  =     {
             'prior': prior,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'renderer': renderer,
             'scheduler': scheduler,
         }

         return components




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=0   )				->int:



         '''simple docstring'''
         if str(lowerCamelCase   ).startswith('mps'   ):
            __a					  =     torch.manual_seed(lowerCamelCase   )
         else:
            __a					  =     torch.Generator(device=lowerCamelCase   ).manual_seed(lowerCamelCase   )
         __a					  =     {
             'prompt': 'horse',
             'generator': generator,
             'num_inference_steps': 1,
             'frame_size': 32,
             'output_type': 'np',
         }
         return inputs




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



         '''simple docstring'''
         __a					  =     'cpu'

         __a					  =     self.get_dummy_components()

         __a					  =     self.pipeline_class(**lowerCamelCase   )
         __a					  =     pipe.to(lowerCamelCase   )

         pipe.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     pipe(**self.get_dummy_inputs(lowerCamelCase   )   )
         __a					  =     output.images[0]
         __a					  =     image[0, -3:, -3:, -1]

         assert image.shape == (20, 32, 32, 3)

         __a					  =     np.array(
             [
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
                 0.00039216,
             ]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]   )




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     torch_device == 'cpu'
         __a					  =     True

         self._test_inference_batch_single_identical(
             batch_size=2			, test_max_difference=lowerCamelCase			, relax_max_difference=lowerCamelCase			, )






      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     self.get_dummy_components()
         __a					  =     self.pipeline_class(**lowerCamelCase   )
         __a					  =     pipe.to(lowerCamelCase   )
         pipe.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     1
         __a					  =     2

         __a					  =     self.get_dummy_inputs(lowerCamelCase   )

         for key in inputs.keys():
            if key in self.batch_params:
               __a					  =     batch_size * [inputs[key]]

         __a					  =     pipe(**lowerCamelCase			, num_images_per_prompt=lowerCamelCase   )[0]

         assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/shap_e/test_shap_e_np_out.npy'   )
         __a					  =     ShapEPipeline.from_pretrained('openai/shap-e'   )
         __a					  =     pipe.to(lowerCamelCase   )
         pipe.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     torch.Generator(device=lowerCamelCase   ).manual_seed(0   )

         __a					  =     pipe(
             'a shark'			, generator=lowerCamelCase			, guidance_scale=15.0			, num_inference_steps=64			, frame_size=64			, output_type='np'			, ).images[0]

         assert images.shape == (20, 64, 64, 3)

         assert_mean_pixel_difference(lowerCamelCase			, lowerCamelCase   )







'''simple docstring'''



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__UpperCamelCase   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

   __a					  =     AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
   logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

   __a					  =     time.time()
   # update config with task specific params
   use_task_specific_params(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   if prefix is None:
      __a					  =     prefix or getattr(model.config,       'prefix',       ''     ) or ''
   for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )     )     ):
      __a					  =     [prefix + text for text in examples_chunk]
      __a					  =     tokenizer(SCREAMING_SNAKE_CASE__,       return_tensors='pt',       truncation=SCREAMING_SNAKE_CASE__,       padding='longest'     ).to(SCREAMING_SNAKE_CASE__     )
      __a					  =     model.generate(
          input_ids=batch.input_ids,       attention_mask=batch.attention_mask,       **SCREAMING_SNAKE_CASE__,       )
      __a					  =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__,       skip_special_tokens=SCREAMING_SNAKE_CASE__,       clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     )
      for hypothesis in dec:
         fout.write(hypothesis + '\n'     )
         fout.flush()
   fout.close()
   __a					  =     int(time.time() - start_time     )  # seconds
   __a					  =     len(SCREAMING_SNAKE_CASE__     )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs,       4     )}



def  __UpperCAmelCase						(					)	->      List[str]:

   """simple docstring"""
   return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

   """simple docstring"""
   __a					  =     argparse.ArgumentParser()
   parser.add_argument('model_name',       type=SCREAMING_SNAKE_CASE__,       help='like facebook/bart-large-cnn,t5-base, etc.'     )
   parser.add_argument('input_path',       type=SCREAMING_SNAKE_CASE__,       help='like cnn_dm/test.source'     )
   parser.add_argument('save_path',       type=SCREAMING_SNAKE_CASE__,       help='where to save summaries'     )
   parser.add_argument('--reference_path',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       help='like cnn_dm/test.target'     )
   parser.add_argument('--score_path',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default='metrics.json',       help='where to save metrics'     )
   parser.add_argument('--device',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default=SCREAMING_SNAKE_CASE__,       help='cuda, cuda:1, cpu etc.'     )
   parser.add_argument(
       '--prefix',       type=SCREAMING_SNAKE_CASE__,       required=SCREAMING_SNAKE_CASE__,       default=SCREAMING_SNAKE_CASE__,       help='will be added to the begininng of src examples'     )
   parser.add_argument('--task',       type=SCREAMING_SNAKE_CASE__,       default='summarization',       help='used for task_specific_params + metrics'     )
   parser.add_argument('--bs',       type=SCREAMING_SNAKE_CASE__,       default=8,       required=SCREAMING_SNAKE_CASE__,       help='batch size'     )
   parser.add_argument(
       '--n_obs',       type=SCREAMING_SNAKE_CASE__,       default=-1,       required=SCREAMING_SNAKE_CASE__,       help='How many observations. Defaults to all.'     )
   parser.add_argument('--fp16',       action='store_true'     )
   parser.add_argument('--dump-args',       action='store_true',       help='print the custom hparams with the results'     )
   parser.add_argument(
       '--info',       nargs='?',       type=SCREAMING_SNAKE_CASE__,       const=datetime_now(),       help=(
           'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
           ' lang=en-ru. If no value is passed, the current datetime string will be used.'
       ),       )
   # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
   __a  ,      __a					  =     parser.parse_known_args()
   __a					  =     parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__     )
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}"""     )
   __a					  =     [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
   if args.n_obs > 0:
      __a					  =     examples[: args.n_obs]
   Path(args.save_path     ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )

   if args.reference_path is None and Path(args.score_path     ).exists():
      warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""     )

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError('Can\'t mix --fp16 and --device cpu'     )

   __a					  =     generate_summaries_or_translations(
       SCREAMING_SNAKE_CASE__,       args.save_path,       args.model_name,       batch_size=args.bs,       device=args.device,       fpaa=args.fpaa,       task=args.task,       prefix=args.prefix,       **SCREAMING_SNAKE_CASE__,       )

   if args.reference_path is None:
      return {}

   # Compute scores
   __a					  =     calculate_bleu if 'translation' in args.task else calculate_rouge
   __a					  =     [x.rstrip() for x in open(args.save_path     ).readlines()]
   __a					  =     [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(SCREAMING_SNAKE_CASE__     )]
   __a					  =     score_fn(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   scores.update(SCREAMING_SNAKE_CASE__     )

   if args.dump_args:
      scores.update(SCREAMING_SNAKE_CASE__     )
   if args.info:
      __a					  =     args.info

   if verbose:
      print(SCREAMING_SNAKE_CASE__     )

   if args.score_path is not None:
      json.dump(SCREAMING_SNAKE_CASE__,       open(args.score_path,       'w'     )     )

   return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)