


import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
UpperCAmelCase     =       False
UpperCAmelCase     =       Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
UpperCAmelCase     =       datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(      self						, _UpperCAmelCase=sys.argv[1:]   ):
       snake_case_			= None
       snake_case_			= None
       snake_case_			= None
       snake_case_			= 1
       snake_case_			,       snake_case_			= getopt.getopt(_UpperCAmelCase						, '''i:o:b:s'''						, ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']   )
       for opt, arg in opts:
             if opt in ("-i", "--inputdir"):
                   snake_case_			= arg
             elif opt in ("-o", "--outfile"):
                   snake_case_			= arg
             elif opt in ("-b", "--batch_size"):
                   snake_case_			= int(_UpperCAmelCase   )
             elif opt in ("-s", "--subset_list"):
                   snake_case_			= arg

       assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
       assert outputfile is not None and not os.path.isfile(_UpperCAmelCase   ), F'''{outputfile}'''
       if subset_list is not None:
             with open(os.path.realpath(_UpperCAmelCase   )   ) as f:
                   snake_case_			= {self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase   )}
       else:
             snake_case_			= None

       snake_case_			= CONFIG
       if torch.cuda.is_available():
             snake_case_			= '''cuda'''
       snake_case_			= os.path.realpath(_UpperCAmelCase   )
       snake_case_			= os.path.realpath(_UpperCAmelCase   )
       snake_case_			= Preprocess(self.config   )
       snake_case_			= GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''						, config=self.config   )
       snake_case_			= batch_size if batch_size != 0 else 1
       snake_case_			= DEFAULT_SCHEMA



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= int(file.split('''.'''   )[0].split('''_'''   )[-1]   )
       snake_case_			= os.path.join(self.inputdir						, _UpperCAmelCase   )
       return (img_id, filepath)



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= []
       for i, file in enumerate(os.listdir(self.inputdir   )   ):
             if self.subset_list is not None and i not in self.subset_list:
                   continue
             batch.append(self._vqa_file_split(_UpperCAmelCase   )   )
             if len(_UpperCAmelCase   ) == self.batch:
                   snake_case_			= batch
                   snake_case_			= []
                   yield list(map(_UpperCAmelCase						, zip(*_UpperCAmelCase   )   )   )

       for i in range(1   ):
             yield list(map(_UpperCAmelCase						, zip(*_UpperCAmelCase   )   )   )





 def __call__(      self   ):
       # make writer
       if not TEST:
             snake_case_			= datasets.ArrowWriter(features=self.schema						, path=self.outputfile   )
       # do file generator
       for i, (img_ids, filepaths) in enumerate(self.file_generator   ):
             snake_case_			,       snake_case_			,       snake_case_			= self.preprocess(_UpperCAmelCase   )
             snake_case_			= self.model(
                 _UpperCAmelCase						, _UpperCAmelCase						, scales_yx=_UpperCAmelCase						, padding='''max_detections'''						, max_detections=self.config.MAX_DETECTIONS						, pad_value=0						, return_tensors='''np'''						, location='''cpu'''						, )
             snake_case_			= output_dict.pop('''normalized_boxes'''   )
             if not TEST:
                   snake_case_			= np.array(_UpperCAmelCase   )
                   snake_case_			= self.schema.encode_batch(_UpperCAmelCase   )
                   writer.write_batch(_UpperCAmelCase   )
             if TEST:
                   break
             # finalizer the writer
       if not TEST:
             snake_case_			,       snake_case_			= writer.finalize()
             print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''   )






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    List[str]:




      """simple docstring"""

      try:
            snake_case_			= json.load(SCREAMING_SNAKE_CASE   )
            try:
                  snake_case_			= list(data.keys()   )
            except Exception:
                  snake_case_			= [d['''img_id'''] for d in data]
      except Exception:
            try:
                  snake_case_			= eval(stream.read()   )
            except Exception:
                  snake_case_			= stream.read().split('''\n'''   )
      return data


if __name__ == "__main__":
   UpperCAmelCase     =       Extract(sys.argv[1:])
   extract()
   if not TEST:
      UpperCAmelCase     =       datasets.Dataset.from_file(extract.outputfile)
      # wala!
      # print(np.array(dataset[0:2]["roi_features"]).shape)



import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    KandinskyVaaPriorEmbaEmbPipeline
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    ["prompt", "image"]
 __snake_case				=    [
     "num_images_per_prompt",
     "strength",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __snake_case				=    False



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return 32



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim



 @property
 def 		UpperCamelCase__	(      self   ):
       return self.time_input_dim * 4



 @property
 def 		UpperCamelCase__	(      self   ):
       return 1_00



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
       return tokenizer



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= 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=10_00						, )
       return CLIPTextModelWithProjection(_UpperCAmelCase   )



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )

       snake_case_			= {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 12,
           '''embedding_dim''': self.text_embedder_hidden_size,
           '''num_layers''': 1,
       }

       snake_case_			= PriorTransformer(**_UpperCAmelCase   )
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       snake_case_			= nn.Parameter(torch.ones(model.clip_std.shape   )   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       torch.manual_seed(0   )
       snake_case_			= CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size						, image_size=2_24						, projection_dim=self.text_embedder_hidden_size						, intermediate_size=37						, num_attention_heads=4						, num_channels=3						, num_hidden_layers=5						, patch_size=14						, )

       snake_case_			= CLIPVisionModelWithProjection(_UpperCAmelCase   )
       return model



 @property
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= CLIPImageProcessor(
           crop_size=2_24						, do_center_crop=_UpperCAmelCase						, do_normalize=_UpperCAmelCase						, do_resize=_UpperCAmelCase						, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]						, image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]						, resample=3						, size=2_24						, )

       return image_processor



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.dummy_prior
       snake_case_			= self.dummy_image_encoder
       snake_case_			= self.dummy_text_encoder
       snake_case_			= self.dummy_tokenizer
       snake_case_			= self.dummy_image_processor

       snake_case_			= UnCLIPScheduler(
           variance_type='''fixed_small_log'''						, prediction_type='''sample'''						, num_train_timesteps=10_00						, clip_sample=_UpperCAmelCase						, clip_sample_range=10.0						, )

       snake_case_			= {
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''scheduler''': scheduler,
           '''image_processor''': image_processor,
       }

       return components



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 64, 64)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )
       snake_case_			= image.cpu().permute(0						, 2						, 3						, 1   )[0]
       snake_case_			= Image.fromarray(np.uinta(_UpperCAmelCase   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

       snake_case_			= {
           '''prompt''': '''horse''',
           '''image''': init_image,
           '''strength''': 0.5,
           '''generator''': generator,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= '''cpu'''

       snake_case_			= self.get_dummy_components()

       snake_case_			= self.pipeline_class(**_UpperCAmelCase   )
       snake_case_			= pipe.to(_UpperCAmelCase   )

       pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

       snake_case_			= pipe(**self.get_dummy_inputs(_UpperCAmelCase   )   )
       snake_case_			= output.image_embeds

       snake_case_			= pipe(
           **self.get_dummy_inputs(_UpperCAmelCase   )						, return_dict=_UpperCAmelCase						, )[0]

       snake_case_			= image[0, -10:]
       snake_case_			= image_from_tuple[0, -10:]

       assert image.shape == (1, 32)

       snake_case_			= np.array(
           [
               0.1_071_284,
               1.3_330_271,
               0.61_260_223,
               -0.6_691_065,
               -0.3_846_852,
               -1.0_303_661,
               0.22_716_111,
               0.03_348_901,
               0.30_040_675,
               -0.24_805_029,
           ]   )

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



 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= True
       snake_case_			= False

       self._test_inference_batch_single_identical(
           test_max_difference=_UpperCAmelCase						, relax_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )





 @skip_mps
 def 		UpperCamelCase__	(      self   ):
       snake_case_			= torch_device == '''cpu'''
       snake_case_			= False

       self._test_attention_slicing_forward_pass(
           test_max_difference=_UpperCAmelCase						, test_mean_pixel_difference=_UpperCAmelCase						, )