



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_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor


class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def __init__(  self       :							Any					,       _snake_case       :							Optional[int]					,       _snake_case       :							Any=7					,       _snake_case       :							int=3					,       _snake_case       :							Tuple=18					,       _snake_case       :							Tuple=30					,       _snake_case       :							Tuple=4_00					,       _snake_case       :							List[str]=True					,       _snake_case       :							str=None					,       _snake_case       :							int=True					,       _snake_case       :							Tuple=None					,       _snake_case       :							Tuple=True					,       _snake_case       :							Optional[int]=[0.5, 0.5, 0.5]					,       _snake_case       :							Any=[0.5, 0.5, 0.5]					,       ):






								"""simple docstring"""


								A__	  =     size if size is not None else {'shortest_edge': 18}
								A__	  =     crop_size if crop_size is not None else {'height': 18, 'width': 18}
								A__	  =     parent
								A__	  =     batch_size
								A__	  =     num_channels
								A__	  =     image_size
								A__	  =     min_resolution
								A__	  =     max_resolution
								A__	  =     do_resize
								A__	  =     size
								A__	  =     do_center_crop
								A__	  =     crop_size
								A__	  =     do_normalize
								A__	  =     image_mean
								A__	  =     image_std



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}







@require_torch
@require_vision
class   __lowerCAmelCase  (		UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Dict      							=     LevitImageProcessor if is_vision_available() else None



					def      _a				(  self       :							str       ):






								"""simple docstring"""


								A__	  =     LevitImageProcessingTester(self       )



					@property
					def      _a				(  self       :							int       ):






								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_snake_case					,       'image_mean'       )       )
								self.assertTrue(hasattr(_snake_case					,       'image_std'       )       )
								self.assertTrue(hasattr(_snake_case					,       'do_normalize'       )       )
								self.assertTrue(hasattr(_snake_case					,       'do_resize'       )       )
								self.assertTrue(hasattr(_snake_case					,       'do_center_crop'       )       )
								self.assertTrue(hasattr(_snake_case					,       'size'       )       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     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}       )

								A__	  =     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      _a				(  self       :							str       ):






								"""simple docstring"""


								pass



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								A__	  =     prepare_image_inputs(self.image_processor_tester					,       equal_resolution=_snake_case       )
								for image in image_inputs:
											self.assertIsInstance(_snake_case					,       Image.Image       )

								# Test not batched input
								A__	  =     image_processing(image_inputs[0]					,       return_tensors='pt'       ).pixel_values
								self.assertEqual(
								    encoded_images.shape					,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,       )

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



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								A__	  =     prepare_image_inputs(self.image_processor_tester					,       equal_resolution=_snake_case					,       numpify=_snake_case       )
								for image in image_inputs:
											self.assertIsInstance(_snake_case					,       np.ndarray       )

								# Test not batched input
								A__	  =     image_processing(image_inputs[0]					,       return_tensors='pt'       ).pixel_values
								self.assertEqual(
								    encoded_images.shape					,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,       )

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



					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								A__	  =     prepare_image_inputs(self.image_processor_tester					,       equal_resolution=_snake_case					,       torchify=_snake_case       )
								for image in image_inputs:
											self.assertIsInstance(_snake_case					,       torch.Tensor       )

								# Test not batched input
								A__	  =     image_processing(image_inputs[0]					,       return_tensors='pt'       ).pixel_values
								self.assertEqual(
								    encoded_images.shape					,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )					,       )

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









import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    'down_block_types': down_block_types,
			    'block_out_channels': block_out_channels,
			    'up_block_types': up_block_types,
			    'layers_per_block': 1,
			    'use_timestep_embedding': True,
			    'out_block_type': 'OutConv1DBlock',
			    'norm_num_groups': 8,
			    'downsample_each_block': False,
			    'in_channels': 14,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'sample_size': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						f'''hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'''       )
			with open(f'''hub/hopper-medium-v2/unet/hor{hor}/config.json'''			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    'in_channels': 14,
			    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
			    'up_block_types': (),
			    'out_block_type': 'ValueFunction',
			    'mid_block_type': 'ValueFunctionMidBlock1D',
			    'block_out_channels': (32, 64, 128, 256),
			    'layers_per_block': 1,
			    'downsample_each_block': True,
			    'sample_size': 65_536,
			    'out_channels': 14,
			    'extra_in_channels': 0,
			    'time_embedding_type': 'positional',
			    'use_timestep_embedding': True,
			    'flip_sin_to_cos': False,
			    'freq_shift': 1,
			    'norm_num_groups': 8,
			    'act_fn': 'mish',
			}

			A__	  =     torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'       )
			A__	  =     model
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )

			A__	  =     dict(zip(state_dict.keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )

			hf_value_function.load_state_dict(__UpperCamelCase       )

			torch.save(hf_value_function.state_dict()			,						'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'       )
			with open('hub/hopper-medium-v2/value_function/config.json'			,						'w'       ) as f:
						json.dump(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




