




import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__SCREAMING_SNAKE_CASE    :   List[Any]       =logging.get_logger(__name__)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       Union[str, Any] = True						)					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				{}
      for name in model.attn_processors.keys():
            lowerCamelCase__        =				name.endswith('attn2.processor'						) and not (
                name.startswith('transformer_in'						) or "temp_attentions" in name.split('.'						)
            )
            lowerCamelCase__        =				model.config.cross_attention_dim if has_cross_attention else None
            if name.startswith('mid_block'						):
                  lowerCamelCase__        =				model.config.block_out_channels[-1]
            elif name.startswith('up_blocks'						):
                  lowerCamelCase__        =				int(name[len('up_blocks.'						)]						)
                  lowerCamelCase__        =				list(reversed(model.config.block_out_channels						)						)[block_id]
            elif name.startswith('down_blocks'						):
                  lowerCamelCase__        =				int(name[len('down_blocks.'						)]						)
                  lowerCamelCase__        =				model.config.block_out_channels[block_id]
            elif name.startswith('transformer_in'						):
                  # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                  lowerCamelCase__        =				8 * model.config.attention_head_dim

            lowerCamelCase__        =				LoRAAttnProcessor(hidden_size=lowerCamelCase     ,				cross_attention_dim=lowerCamelCase						)
            lowerCamelCase__        =				lora_attn_procs[name].to(model.device						)

            if mock_weights:
                  # add 1 to weights to mock trained weights
                  with torch.no_grad():
                        lora_attn_procs[name].to_q_lora.up.weight += 1
                        lora_attn_procs[name].to_k_lora.up.weight += 1
                        lora_attn_procs[name].to_v_lora.up.weight += 1
                        lora_attn_procs[name].to_out_lora.up.weight += 1

      return lora_attn_procs






@skip_mps
class 			A			(		a__			,						a__			,						unittest.TestCase		):
     """simple docstring"""

     _snake_case:	Tuple     =UNetaDConditionModel
     _snake_case:	Optional[int]     ="sample"




     @property
     def  __snake_case   (    self  :   Dict			)				->       List[str]:
           lowerCamelCase__        =				4
           lowerCamelCase__        =				4
           lowerCamelCase__        =				4
           lowerCamelCase__        =				(3_2, 3_2)

           lowerCamelCase__        =				floats_tensor((batch_size, num_channels, num_frames) + sizes			).to(__A			)
           lowerCamelCase__        =				torch.tensor([1_0]			).to(__A			)
           lowerCamelCase__        =				floats_tensor((batch_size, 4, 3_2)			).to(__A			)

           return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}




     @property
     def  __snake_case   (    self  :   int			)				->       Union[str, Any]:
           return (4, 4, 3_2, 3_2)




     @property
     def  __snake_case   (    self  :   List[Any]			)				->       Any:
           return (4, 4, 3_2, 3_2)




     def  __snake_case   (    self  :   Optional[Any]			)				->       Tuple:
           lowerCamelCase__        =				{
               "block_out_channels": (3_2, 6_4),
               "down_block_types": (
                   "CrossAttnDownBlock3D",
                   "DownBlock3D",
               ),
               "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
               "cross_attention_dim": 3_2,
               "attention_head_dim": 8,
               "out_channels": 4,
               "in_channels": 4,
               "layers_per_block": 1,
               "sample_size": 3_2,
           }
           lowerCamelCase__        =				self.dummy_input
           return init_dict, inputs_dict




     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()	,  reason='XFormers attention is only available with CUDA and `xformers` installed'	,  )
     def  __snake_case   (    self  :   List[str]			)				->       Tuple:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()
           lowerCamelCase__        =				self.model_class(**__A			)

           model.enable_xformers_memory_efficient_attention()

           assert (
               model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
               == "XFormersAttnProcessor"
           ), "xformers is not enabled"




     def  __snake_case   (    self  :   Optional[Any]			)				->       int:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				3_2

           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)
           model.eval()

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			)

                 if isinstance(__A	,  __A			):
                       lowerCamelCase__        =				output.sample

           self.assertIsNotNone(__A			)
           lowerCamelCase__        =				inputs_dict["sample"].shape
           self.assertEqual(output.shape	,  __A	,  'Input and output shapes do not match'			)




     def  __snake_case   (    self  :   Optional[Any]			)				->       Tuple:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)
           model.eval()

           with torch.no_grad():
                 # Warmup pass when using mps (see #372)
                 if torch_device == "mps" and isinstance(__A	,  __A			):
                       model(**self.dummy_input			)

                 lowerCamelCase__        =				model(**__A			)
                 if isinstance(__A	,  __A			):
                       lowerCamelCase__        =				first.sample

                 lowerCamelCase__        =				model(**__A			)
                 if isinstance(__A	,  __A			):
                       lowerCamelCase__        =				second.sample

           lowerCamelCase__        =				first.cpu().numpy()
           lowerCamelCase__        =				second.cpu().numpy()
           lowerCamelCase__        =				out_a[~np.isnan(__A			)]
           lowerCamelCase__        =				out_a[~np.isnan(__A			)]
           lowerCamelCase__        =				np.amax(np.abs(out_a - out_a			)			)
           self.assertLessEqual(__A	,  1E-5			)




     def  __snake_case   (    self  :   List[str]			)				->       Optional[int]:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)
           model.eval()

           model.set_attention_slice('auto'			)
           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			)
           assert output is not None

           model.set_attention_slice('max'			)
           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			)
           assert output is not None

           model.set_attention_slice(2			)
           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			)
           assert output is not None




     def  __snake_case   (    self  :   Tuple			)				->       List[str]:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			).sample

           lowerCamelCase__        =				create_lora_layers(__A			)

           # make sure we can set a list of attention processors
           model.set_attn_processor(__A			)
           model.to(__A			)

           # test that attn processors can be set to itself
           model.set_attn_processor(model.attn_processors			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A	,  cross_attention_kwargs={'scale': 0.0}			).sample
                 lowerCamelCase__        =				model(**__A	,  cross_attention_kwargs={'scale': 0.5}			).sample
                 lowerCamelCase__        =				model(**__A	,  cross_attention_kwargs={'scale': 0.5}			).sample

           assert (samplea - samplea).abs().max() < 3E-3
           assert (samplea - samplea).abs().max() < 3E-3

           # sample 2 and sample 3 should be different
           assert (samplea - samplea).abs().max() > 3E-3




     def  __snake_case   (    self  :   Tuple			)				->       int:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           torch.manual_seed(0			)
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			).sample

           lowerCamelCase__        =				create_lora_layers(__A			)
           model.set_attn_processor(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A	,  cross_attention_kwargs={'scale': 0.5}			).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__A			)
                 self.assertTrue(os.path.isfile(os.path.join(__A	,  'pytorch_lora_weights.bin'			)			)			)
                 torch.manual_seed(0			)
                 lowerCamelCase__        =				self.model_class(**__A			)
                 new_model.to(__A			)
                 new_model.load_attn_procs(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				new_model(**__A	,  cross_attention_kwargs={'scale': 0.5}			).sample

           assert (sample - new_sample).abs().max() < 1E-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1E-4




     def  __snake_case   (    self  :   str			)				->       Union[str, Any]:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           torch.manual_seed(0			)
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			).sample

           lowerCamelCase__        =				create_lora_layers(__A			)
           model.set_attn_processor(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A	,  cross_attention_kwargs={'scale': 0.5}			).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__A	,  safe_serialization=__A			)
                 self.assertTrue(os.path.isfile(os.path.join(__A	,  'pytorch_lora_weights.safetensors'			)			)			)
                 torch.manual_seed(0			)
                 lowerCamelCase__        =				self.model_class(**__A			)
                 new_model.to(__A			)
                 new_model.load_attn_procs(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				new_model(**__A	,  cross_attention_kwargs={'scale': 0.5}			).sample

           assert (sample - new_sample).abs().max() < 3E-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1E-4




     def  __snake_case   (    self  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           torch.manual_seed(0			)
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)

           lowerCamelCase__        =				create_lora_layers(__A	,  mock_weights=__A			)
           model.set_attn_processor(__A			)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__A			)
                 self.assertTrue(os.path.isfile(os.path.join(__A	,  'pytorch_lora_weights.bin'			)			)			)
                 torch.manual_seed(0			)
                 lowerCamelCase__        =				self.model_class(**__A			)
                 new_model.to(__A			)
                 new_model.load_attn_procs(__A	,  weight_name='pytorch_lora_weights.bin'			)




     def  __snake_case   (    self  :   Dict			)				->       List[str]:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           torch.manual_seed(0			)
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)

           lowerCamelCase__        =				create_lora_layers(__A	,  mock_weights=__A			)
           model.set_attn_processor(__A			)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(__A			)
                 self.assertTrue(os.path.isfile(os.path.join(__A	,  'pytorch_lora_weights.bin'			)			)			)
                 torch.manual_seed(0			)
                 lowerCamelCase__        =				self.model_class(**__A			)
                 new_model.to(__A			)
                 with self.assertRaises(__A			) as e:
                       new_model.load_attn_procs(__A	,  use_safetensors=__A			)
                 self.assertIn('Error no file named pytorch_lora_weights.safetensors'	,  str(e.exception			)			)




     def  __snake_case   (    self  :   List[str]			)				->       Tuple:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				8

           torch.manual_seed(0			)
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			).sample

           lowerCamelCase__        =				create_lora_layers(__A			)
           model.set_attn_processor(__A			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A	,  cross_attention_kwargs={'scale': 0.0}			).sample

           model.set_attn_processor(AttnProcessor()			)

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			).sample

           assert (sample - new_sample).abs().max() < 1E-4
           assert (sample - old_sample).abs().max() < 3E-3




     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()	,  reason='XFormers attention is only available with CUDA and `xformers` installed'	,  )
     def  __snake_case   (    self  :   Any			)				->       int:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()

           lowerCamelCase__        =				4

           torch.manual_seed(0			)
           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)
           lowerCamelCase__        =				create_lora_layers(__A			)
           model.set_attn_processor(__A			)

           # default
           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			).sample

                 model.enable_xformers_memory_efficient_attention()
                 lowerCamelCase__        =				model(**__A			).sample

                 model.disable_xformers_memory_efficient_attention()
                 lowerCamelCase__        =				model(**__A			).sample

           assert (sample - on_sample).abs().max() < 1E-4
           assert (sample - off_sample).abs().max() < 1E-4




     def  __snake_case   (    self  :   List[str]			)				->       Tuple:
           lowerCamelCase__        =				self.prepare_init_args_and_inputs_for_common()
           lowerCamelCase__        =				3_2

           lowerCamelCase__        =				self.model_class(**__A			)
           model.to(__A			)
           model.eval()

           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
                 lowerCamelCase__        =				model(**__A			)[0]

           self.assertEqual(output.shape	,  output_a.shape	,  'Shape doesn\'t match'			)
           assert np.abs(output.cpu() - output_a.cpu()			).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.





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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =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, 2_048), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				arg

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

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




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           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(A__			)			)
                 if len(A__			) == self.batch:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




     def __call__(    self  :   List[Any]			)				->       Optional[Any]:
           # make writer
           if not TEST:
                 lowerCamelCase__        =				datasets.ArrowWriter(features=self.schema	,  path=self.outputfile			)
           # do file generator
           for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                 lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				self.preprocess(A__			)
                 lowerCamelCase__        =				self.model(
                     A__	,  A__	,  scales_yx=A__	,  padding='max_detections'	,  max_detections=self.config.MAX_DETECTIONS	,  pad_value=0	,  return_tensors='np'	,  location='cpu'	,  )
                 lowerCamelCase__        =				output_dict.pop('normalized_boxes'			)
                 if not TEST:
                       lowerCamelCase__        =				np.array(A__			)
                       lowerCamelCase__        =				self.schema.encode_batch(A__			)
                       writer.write_batch(A__			)
                 if TEST:
                       break
                 # finalizer the writer
           if not TEST:
                 lowerCamelCase__  ,		lowerCamelCase__        =				writer.finalize()
                 print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"""			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




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


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