

"""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		     =    False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def __init__(   self			,					snake_case			,					snake_case=7			,					snake_case=3			,					snake_case=18			,					snake_case=30			,					snake_case=400			,					snake_case=None			,					snake_case=True			,					snake_case=True			,					snake_case=None			,					):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				size if size is not None else {"height": 20, "width": 20}
        lowerCAmelCase__   :				Union[str, Any] 			=				parent
        lowerCAmelCase__   :				Optional[Any] 			=				batch_size
        lowerCAmelCase__   :				Optional[int] 			=				num_channels
        lowerCAmelCase__   :				List[Any] 			=				image_size
        lowerCAmelCase__   :				Optional[int] 			=				min_resolution
        lowerCAmelCase__   :				List[str] 			=				max_resolution
        lowerCAmelCase__   :				Optional[Any] 			=				size
        lowerCAmelCase__   :				Any 			=				do_normalize
        lowerCAmelCase__   :				List[Any] 			=				do_convert_rgb
        lowerCAmelCase__   :				Any 			=				[512, 1_024, 2_048, 4_096]
        lowerCAmelCase__   :				Tuple 			=				patch_size if patch_size is not None else {"height": 16, "width": 16}






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        lowerCAmelCase__   :				List[Any] 			=				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					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						PixaStructImageProcessor if is_vision_available() else None






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				PixaStructImageProcessingTester(self   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(snake_case			,					"do_normalize"   )   )
        self.assertTrue(hasattr(snake_case			,					"do_convert_rgb"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.image_processor_tester.prepare_dummy_image()

        lowerCAmelCase__   :				Dict 			=				self.image_processing_class(**self.image_processor_dict   )
        lowerCAmelCase__   :				List[Any] 			=				2_048

        lowerCAmelCase__   :				List[str] 			=				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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        lowerCAmelCase__   :				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
        lowerCAmelCase__   :				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
         lowerCAmelCase__   :				List[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
         lowerCAmelCase__   :				Optional[int] 			=				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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        lowerCAmelCase__   :				List[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
        lowerCAmelCase__   :				Union[str, Any] 			=				(
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        lowerCAmelCase__   :				Optional[Any] 			=				True

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

         lowerCAmelCase__   :				List[Any] 			=				"Hello"

         lowerCAmelCase__   :				Any 			=				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
         lowerCAmelCase__   :				Any 			=				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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        lowerCAmelCase__   :				Union[str, Any] 			=				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   )

        lowerCAmelCase__   :				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
         lowerCAmelCase__   :				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
         lowerCAmelCase__   :				Dict 			=				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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        lowerCAmelCase__   :				List[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
        lowerCAmelCase__   :				Dict 			=				(
            (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
         lowerCAmelCase__   :				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
         lowerCAmelCase__   :				Optional[int] 			=				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					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						PixaStructImageProcessor if is_vision_available() else None






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				PixaStructImageProcessingTester(self			,					num_channels=4   )
        lowerCAmelCase__   :				Tuple 			=				3






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.image_processor_tester.prepare_image_processor_dict()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(snake_case			,					"do_normalize"   )   )
        self.assertTrue(hasattr(snake_case			,					"do_convert_rgb"   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        lowerCAmelCase__   :				Tuple 			=				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
        lowerCAmelCase__   :				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
         lowerCAmelCase__   :				Dict 			=				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
         lowerCAmelCase__   :				Tuple 			=				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       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 if hor == 1_2_8:
  lowerCAmelCase__   :				str 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				int 			=				(3_2, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D")

 elif hor == 3_2:
  lowerCAmelCase__   :				Any 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				Dict 			=				(3_2, 6_4, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
 lowerCAmelCase__   :				Tuple 			=				torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""   )
 lowerCAmelCase__   :				str 			=				model.state_dict()
 lowerCAmelCase__   :				Union[str, Any] 			=				{
     "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": 1_4,
     "out_channels": 1_4,
     "extra_in_channels": 0,
     "time_embedding_type": "positional",
     "flip_sin_to_cos": False,
     "freq_shift": 1,
     "sample_size": 6_5_5_3_6,
     "mid_block_type": "MidResTemporalBlock1D",
     "act_fn": "mish",
 }
 lowerCAmelCase__   :				str 			=				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()   )}"""   )
 lowerCAmelCase__   :				Dict 			=				dict(zip(model.state_dict().keys()					,					hf_value_function.state_dict().keys()   )   )
 for k, v in mapping.items():
  lowerCAmelCase__   :				Tuple 			=				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       SCREAMING_SNAKE_CASE							( )			->						Any:
 lowerCAmelCase__   :				Dict 			=				{
     "in_channels": 1_4,
     "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
     "up_block_types": (),
     "out_block_type": "ValueFunction",
     "mid_block_type": "ValueFunctionMidBlock1D",
     "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
     "layers_per_block": 1,
     "downsample_each_block": True,
     "sample_size": 6_5_5_3_6,
     "out_channels": 1_4,
     "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",
 }

 lowerCAmelCase__   :				Any 			=				torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"   )
 lowerCAmelCase__   :				Optional[int] 			=				model
 lowerCAmelCase__   :				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()   )}"""   )

 lowerCAmelCase__   :				str 			=				dict(zip(state_dict.keys()					,					hf_value_function.state_dict().keys()   )   )
 for k, v in mapping.items():
  lowerCAmelCase__   :				List[Any] 			=				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()






