

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__A      		=     False
__A      		=     True
__A      		=     False


if __name__ == "__main__":
 __A      		=     argparse.ArgumentParser()

 parser.add_argument(
     '''--repo_path''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 __A      		=     parser.parse_args()

 __A      		=     {
     '''image_size''': '''sample_size''',
     '''num_res_blocks''': '''layers_per_block''',
     '''block_channels''': '''block_out_channels''',
     '''down_blocks''': '''down_block_types''',
     '''up_blocks''': '''up_block_types''',
     '''downscale_freq_shift''': '''freq_shift''',
     '''resnet_num_groups''': '''norm_num_groups''',
     '''resnet_act_fn''': '''act_fn''',
     '''resnet_eps''': '''norm_eps''',
     '''num_head_channels''': '''attention_head_dim''',
 }

 __A      		=     {
     '''time_steps''': '''time_proj''',
     '''mid''': '''mid_block''',
     '''downsample_blocks''': '''down_blocks''',
     '''upsample_blocks''': '''up_blocks''',
 }

 __A      		=     '''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

 with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
  __A      		=     reader.read()
  __A      		=     json.loads(text)

 if do_only_config:
  for key in config_parameters_to_change.keys():
   config.pop(key, None)

 if has_file(args.repo_path, '''config.json'''):
  __A      		=     UNetaDModel(**config)
 else:
  __A      		=     UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  __A      		=     class_name(**config)

 if do_only_config:
  model.save_config(os.path.join(args.repo_path, subfolder))

 __A      		=     dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    __A      		=     config[key]
    del config[key]

  __A      		=     [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  __A      		=     [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

 if do_only_weights:
  __A      		=     torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

  __A      		=     {}
  for param_key, param_value in state_dict.items():
   if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
    continue
   __A      		=     False
   for key, new_key in key_parameters_to_change.items():
    if not has_changed and param_key.split('''.''')[0] == key:
     __A      		=     param_value
     __A      		=     True
   if not has_changed:
    __A      		=     param_value

  model.load_state_dict(new_state_dict)
  model.save_pretrained(os.path.join(args.repo_path, subfolder))




import unittest

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_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    int      , A__				:    List[str]=7      , A__				:    Tuple=3      , A__				:    List[str]=10      , A__				:    Optional[int]=18      , A__				:    int=30      , A__				:    Tuple=400      , A__				:    Dict=True      , A__				:    str=None      , A__				:    str=True      , A__				:    List[str]=[0.5, 0.5, 0.5]      , A__				:    int=[0.5, 0.5, 0.5]      , A__				:    List[Any]=None      , )   ->					int:
        _snake_case						=							size if size is not None else {'''shortest_edge''': 18}
        _snake_case						=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							num_channels
        _snake_case						=							num_frames
        _snake_case						=							image_size
        _snake_case						=							min_resolution
        _snake_case						=							max_resolution
        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							do_normalize
        _snake_case						=							image_mean
        _snake_case						=							image_std
        _snake_case						=							crop_size







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Union[str, Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Tuple										=    VivitImageProcessor if is_vision_available() else None



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							VivitImageProcessingTester(self		)



      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[Any]:
        _snake_case						=							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__      , '''do_center_crop'''		)		)
        self.assertTrue(hasattr(A__      , '''size'''		)		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 18, '''width''': 18}		)

        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 84, '''width''': 84}		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL videos
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , Image.Image		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , numpify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , np.ndarray		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					int:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , torchify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , torch.Tensor		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

