
"""simple docstring"""




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import BridgeTowerImageProcessor

class 		a__						(      unittest.TestCase						):
   def __init__(			self	,    _a	,    _a = True	,    _a = None	,    _a = 32	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = True	,    _a = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]	,    _a = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]	,    _a = True	,    _a=7	,    _a=30	,    _a=400	,    _a=3	,    ):
          lowercase    :				int       		=							parent
          lowercase    :				Optional[int]       		=							do_resize
          lowercase    :				List[str]       		=							size if size is not None else {"shortest_edge": 288}
          lowercase    :				int       		=							size_divisor
          lowercase    :				str       		=							do_rescale
          lowercase    :				List[Any]       		=							rescale_factor
          lowercase    :				Optional[int]       		=							do_normalize
          lowercase    :				Dict       		=							do_center_crop
          lowercase    :				Optional[int]       		=							image_mean
          lowercase    :				Optional[Any]       		=							image_std
          lowercase    :				List[str]       		=							do_pad
          lowercase    :				List[Any]       		=							batch_size
          lowercase    :				Optional[Any]       		=							num_channels
          lowercase    :				Optional[int]       		=							min_resolution
          lowercase    :				Union[str, Any]       		=							max_resolution
   def 			__magic_name__			(			self				):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }

   def 			__magic_name__			(			self	,    _a	,    _a=False				):
          if not batched:
                 lowercase    :				Union[str, Any]       		=							self.size["shortest_edge"]
                 lowercase    :				Dict       		=							image_inputs[0]
                 if isinstance(_a	,    Image.Image				):
                        lowercase		,    lowercase    :				int       		=							image.size
                 else:
                        lowercase		,    lowercase    :				Optional[int]       		=							image.shape[1], image.shape[2]
                 lowercase    :				Optional[int]       		=							size / min(_a	,    _a				)
                 if h < w:
                        lowercase		,    lowercase    :				Tuple       		=							size, scale * w
                 else:
                        lowercase		,    lowercase    :				Optional[int]       		=							scale * h, size

                 lowercase    :				List[Any]       		=							int((1_333 / 800) * size				)
                 if max(_a	,    _a				) > max_size:
                        lowercase    :				int       		=							max_size / max(_a	,    _a				)
                        lowercase    :				Tuple       		=							newh * scale
                        lowercase    :				Union[str, Any]       		=							neww * scale

                 lowercase		,    lowercase    :				Dict       		=							int(newh + 0.5				), int(neww + 0.5				)
                 lowercase		,    lowercase    :				Optional[Any]       		=							(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

          else:
                 lowercase    :				Tuple       		=							[]
                 for image in image_inputs:
                        lowercase		,    lowercase    :				Union[str, Any]       		=							self.get_expected_values([image]				)
                        expected_values.append((expected_height, expected_width)				)
                 lowercase    :				int       		=							max(_a	,    key=lambda _a				: item[0]				)[0]
                 lowercase    :				Union[str, Any]       		=							max(_a	,    key=lambda _a				: item[1]				)[1]

          return expected_height, expected_width


@require_torch
@require_vision
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				BridgeTowerImageProcessor if is_vision_available() else None
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							BridgeTowerImageProcessingTester(self				)
   @property
   def 			__magic_name__			(			self				):
          return self.image_processor_tester.prepare_image_processor_dict()
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(_a	,    "image_mean"				)				)
          self.assertTrue(hasattr(_a	,    "image_std"				)				)
          self.assertTrue(hasattr(_a	,    "do_normalize"				)				)
          self.assertTrue(hasattr(_a	,    "do_resize"				)				)
          self.assertTrue(hasattr(_a	,    "size"				)				)
          self.assertTrue(hasattr(_a	,    "size_divisor"				)				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          # Initialize image processor
          lowercase    :				Any       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          lowercase    :				Union[str, Any]       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    Image.Image				)

          # Test not batched input
          lowercase    :				Any       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Optional[Any]       		=							self.image_processor_tester.get_expected_values(_a				)
          self.assertEqual(
              encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

          # Test batched
          lowercase    :				Dict       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				List[Any]       		=							self.image_processor_tester.get_expected_values(_a	,    batched=_a				)
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,    )
   def 			__magic_name__			(			self				):
          # Initialize image processor
          lowercase    :				Tuple       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          lowercase    :				Dict       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a	,    numpify=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    np.ndarray				)

          # Test not batched input
          lowercase    :				List[Any]       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Dict       		=							self.image_processor_tester.get_expected_values(_a				)
          self.assertEqual(
              encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

          # Test batched
          lowercase    :				str       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Dict       		=							self.image_processor_tester.get_expected_values(_a	,    batched=_a				)
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,    )

   def 			__magic_name__			(			self				):
          # Initialize image processor
          lowercase    :				Tuple       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          lowercase    :				int       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a	,    torchify=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    torch.Tensor				)

          # Test not batched input
          lowercase    :				Tuple       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Dict       		=							self.image_processor_tester.get_expected_values(_a				)
          self.assertEqual(
              encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

          # Test batched
          lowercase    :				Tuple       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Tuple       		=							self.image_processor_tester.get_expected_values(_a	,    batched=_a				)
          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,    )




"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							DDIMScheduler(
           timesteps=config.model.params.timesteps     , beta_schedule="scaled_linear"     , beta_start=config.model.params.linear_start     , beta_end=config.model.params.linear_end     , clip_sample=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", type=str, required=True)
   parser.add_argument("""--config_path""", type=str, required=True)
   parser.add_argument("""--output_path""", type=str, required=True)
   _A			:       Dict   		=       parser.parse_args()

   convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


