


'''simple docstring'''



import unittest

import numpy as np
import requests

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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      __UpperCAmelCase    :Optional[Any]				       =     False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor



class 			a       (	unittest.TestCase ):


     """simple docstring"""






     def __init__(       self  :	Union[str, Any]							,				snake_case  :	Optional[int]							,				snake_case  :	Union[str, Any]=7							,				snake_case  :	str=3							,				snake_case  :	List[Any]=18							,				snake_case  :	Optional[Any]=30							,				snake_case  :	List[Any]=400							,				snake_case  :	int=None							,				snake_case  :	List[Any]=True							,				snake_case  :	int=True							,				snake_case  :	Dict=None							,				)		->							str:
          __UpperCAmelCase			:				Tuple													=    size if size is not None else {'''height''': 20, '''width''': 20}
          __UpperCAmelCase			:				Dict													=    parent
          __UpperCAmelCase			:				Union[str, Any]													=    batch_size
          __UpperCAmelCase			:				int													=    num_channels
          __UpperCAmelCase			:				Optional[Any]													=    image_size
          __UpperCAmelCase			:				Any													=    min_resolution
          __UpperCAmelCase			:				Any													=    max_resolution
          __UpperCAmelCase			:				List[Any]													=    size
          __UpperCAmelCase			:				Optional[Any]													=    do_normalize
          __UpperCAmelCase			:				int													=    do_convert_rgb
          __UpperCAmelCase			:				Tuple													=    [512, 1024, 2048, 4096]
          __UpperCAmelCase			:				List[Any]													=    patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							int:
          __UpperCAmelCase			:				int													=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
          __UpperCAmelCase			:				List[str]													=    Image.open(requests.get(snake_case							,				stream=snake_case    ).raw    ).convert('''RGB'''    )
          return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,						reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,						)
@require_torch
@require_vision
class 			a       (	_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				str							     =     PixaStructImageProcessor if is_vision_available() else None

     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[int]:
          __UpperCAmelCase			:				Any													=    PixaStructImageProcessingTester(self    )

     @property
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							str:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Dict:
          __UpperCAmelCase			:				Optional[int]													=    self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(snake_case							,				'''do_normalize'''    )    )
          self.assertTrue(hasattr(snake_case							,				'''do_convert_rgb'''    )    )

     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.image_processor_tester.prepare_dummy_image()

          __UpperCAmelCase			:				Union[str, Any]													=    self.image_processing_class(**self.image_processor_dict    )
          __UpperCAmelCase			:				Union[str, Any]													=    2048

          __UpperCAmelCase			:				Optional[int]													=    image_processor(snake_case							,				return_tensors='''pt'''							,				max_patches=snake_case    )
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,				torch.tensor(0.0_606    )							,				atol=1E-3							,				rtol=1E-3    )    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Tuple:
          # Initialize image_processor
          __UpperCAmelCase			:				Optional[Any]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          __UpperCAmelCase			:				Union[str, Any]													=    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
          __UpperCAmelCase			:				str													=    (
              (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __UpperCAmelCase			:				Any													=    image_processor(
                   image_inputs[0]							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(1, max_patch, expected_hidden_dim)							,				)

               # Test batched
               __UpperCAmelCase			:				Any													=    image_processor(
                   snake_case							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,				)

     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          # Initialize image_processor
          __UpperCAmelCase			:				Dict													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          __UpperCAmelCase			:				Union[str, Any]													=    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
          __UpperCAmelCase			:				str													=    (
              (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
              * self.image_processor_tester.num_channels
          ) + 2

          __UpperCAmelCase			:				str													=    True

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               with self.assertRaises(snake_case    ):
                    __UpperCAmelCase			:				str													=    image_processor(
                        image_inputs[0]							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches

               __UpperCAmelCase			:				int													=    '''Hello'''

               __UpperCAmelCase			:				List[str]													=    image_processor(
                   image_inputs[0]							,				return_tensors='''pt'''							,				max_patches=snake_case							,				header_text=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(1, max_patch, expected_hidden_dim)							,				)

               # Test batched
               __UpperCAmelCase			:				Tuple													=    image_processor(
                   snake_case							,				return_tensors='''pt'''							,				max_patches=snake_case							,				header_text=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,				)

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Union[str, Any]:
          # Initialize image_processor
          __UpperCAmelCase			:				List[str]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          __UpperCAmelCase			:				List[str]													=    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    )

          __UpperCAmelCase			:				List[str]													=    (
              (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __UpperCAmelCase			:				Union[str, Any]													=    image_processor(
                   image_inputs[0]							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(1, max_patch, expected_hidden_dim)							,				)

               # Test batched
               __UpperCAmelCase			:				Optional[Any]													=    image_processor(
                   snake_case							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,				)






     def 				lowerCamelCase__				(       self  :	int    )		->							Dict:
          # Initialize image_processor
          __UpperCAmelCase			:				Tuple													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          __UpperCAmelCase			:				str													=    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
          __UpperCAmelCase			:				Union[str, Any]													=    (
              (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __UpperCAmelCase			:				str													=    image_processor(
                   image_inputs[0]							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(1, max_patch, expected_hidden_dim)							,				)

               # Test batched
               __UpperCAmelCase			:				Optional[Any]													=    image_processor(
                   snake_case							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,				)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,						reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,						)
@require_torch
@require_vision
class 			a       (	_a			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[str]							     =     PixaStructImageProcessor if is_vision_available() else None

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
          __UpperCAmelCase			:				List[Any]													=    PixaStructImageProcessingTester(self							,				num_channels=4    )
          __UpperCAmelCase			:				str													=    3

     @property
     def 				lowerCamelCase__				(       self  :	Any    )		->							str:
          return self.image_processor_tester.prepare_image_processor_dict()

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				int													=    self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(snake_case							,				'''do_normalize'''    )    )
          self.assertTrue(hasattr(snake_case							,				'''do_convert_rgb'''    )    )






     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							str:
          # Initialize image_processor
          __UpperCAmelCase			:				List[str]													=    self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          __UpperCAmelCase			:				str													=    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
          __UpperCAmelCase			:				str													=    (
              (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
               # Test not batched input
               __UpperCAmelCase			:				Any													=    image_processor(
                   image_inputs[0]							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(1, max_patch, expected_hidden_dim)							,				)

               # Test batched
               __UpperCAmelCase			:				List[Any]													=    image_processor(
                   snake_case							,				return_tensors='''pt'''							,				max_patches=snake_case    ).flattened_patches
               self.assertEqual(
                   encoded_images.shape							,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,				)



'''simple docstring'''



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			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

     elif hor == 32:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Any													=    (32, 64, 128, 256)
          __UpperCAmelCase			:				Optional[int]													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
     __UpperCAmelCase			:				Any													=    torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
     __UpperCAmelCase			:				str													=    model.state_dict()
     __UpperCAmelCase			:				int													=    {
         '''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''': 65536,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     __UpperCAmelCase			:				Optional[Any]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)
     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(model.state_dict().keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				List[Any]													=    state_dict.pop(_lowercase						)
     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''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''': 65536,
         '''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''',
     }

     __UpperCAmelCase			:				str													=    torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
     __UpperCAmelCase			:				Optional[Any]													=    model
     __UpperCAmelCase			:				Optional[int]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)

     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(state_dict.keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				Dict													=    state_dict.pop(_lowercase						)

     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)


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