


'''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 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if hor == 128:
          __A	:       int      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Union[str, Any]      =							(32, 128, 256)
          __A	:       str      =							("UpResnetBlock1D", "UpResnetBlock1D")

     elif hor == 32:
          __A	:       List[Any]      =							("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
          __A	:       Optional[Any]      =							(32, 64, 128, 256)
          __A	:       Dict      =							("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
     __A	:       Union[str, Any]      =							torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
     __A	:       List[str]      =							model.state_dict()
     __A	:       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": 14,
         "out_channels": 14,
         "extra_in_channels": 0,
         "time_embedding_type": "positional",
         "flip_sin_to_cos": False,
         "freq_shift": 1,
         "sample_size": 6_5536,
         "mid_block_type": "MidResTemporalBlock1D",
         "act_fn": "mish",
     }
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     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      =							dict(zip(model.state_dict().keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       Optional[int]      =							state_dict.pop(SCREAMING_SNAKE_CASE							)
     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							{
         "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": 6_5536,
         "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	:       Optional[int]      =							torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"							)
     __A	:       Dict      =							model
     __A	:       Union[str, Any]      =							UNetaDModel(**SCREAMING_SNAKE_CASE							)
     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	:       int      =							dict(zip(state_dict.keys()       ,					hf_value_function.state_dict().keys()							)							)
     for k, v in mapping.items():
          __A	:       str      =							state_dict.pop(SCREAMING_SNAKE_CASE							)

     hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE							)

     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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      unet(32)
      # unet(128)
      value_function()





'''simple docstring'''


import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import ViTMAEForPreTraining, ViTMAEModel
      from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor







class 							__magic_name__	:

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=13			,     lowerCamelCase=30			,     lowerCamelCase=2			,     lowerCamelCase=3			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=10			,     lowerCamelCase=0.02			,     lowerCamelCase=3			,     lowerCamelCase=0.6			,     lowerCamelCase=None			,     ):


          '''simple docstring'''





          __A	:       Tuple      =							parent
          __A	:       Union[str, Any]      =							batch_size
          __A	:       List[Any]      =							image_size
          __A	:       Union[str, Any]      =							patch_size
          __A	:       List[Any]      =							num_channels
          __A	:       Optional[Any]      =							is_training
          __A	:       str      =							use_labels
          __A	:       Tuple      =							hidden_size
          __A	:       int      =							num_hidden_layers
          __A	:       Dict      =							num_attention_heads
          __A	:       List[Any]      =							intermediate_size
          __A	:       Tuple      =							hidden_act
          __A	:       Tuple      =							hidden_dropout_prob
          __A	:       str      =							attention_probs_dropout_prob
          __A	:       Optional[Any]      =							type_sequence_label_size
          __A	:       Union[str, Any]      =							initializer_range
          __A	:       Optional[Any]      =							mask_ratio
          __A	:       List[Any]      =							scope

          # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
          # (we add 1 for the [CLS] token)
          __A	:       Optional[int]      =							(image_size // patch_size) ** 2
          __A	:       List[Any]      =							int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A	:       Tuple      =							None
          if self.use_labels:
               __A	:       Union[str, Any]      =							ids_tensor([self.batch_size]			,     self.type_sequence_label_size		)

          __A	:       Optional[int]      =							self.get_config()

          return config, pixel_values, labels


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return ViTMAEConfig(
              image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=lowerCamelCase			,     initializer_range=self.initializer_range			,     mask_ratio=self.mask_ratio			,     )


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							ViTMAEModel(config=lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       List[Any]      =							model(lowerCamelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       str      =							ViTMAEForPreTraining(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       Optional[Any]      =							model(lowerCamelCase		)
          __A	:       List[str]      =							(self.image_size // self.patch_size) ** 2
          __A	:       List[Any]      =							self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, num_patches, expected_num_channels)		)

          # test greyscale images
          __A	:       List[str]      =							1
          __A	:       str      =							ViTMAEForPreTraining(lowerCamelCase		)
          model.to(lowerCamelCase		)
          model.eval()
          __A	:       str      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A	:       int      =							model(lowerCamelCase		)
          __A	:       Union[str, Any]      =							self.patch_size**2
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, num_patches, expected_num_channels)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							self.prepare_config_and_inputs()
          __A  ,__A  ,__A	:       List[Any]      =							config_and_inputs
          __A	:       Tuple      =							{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 							__magic_name__	(       lowerCAmelCase    ,		lowerCAmelCase    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     lowerCamelCase__       						=				{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				False


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							ViTMAEModelTester(self		)
          __A	:       Tuple      =							ConfigTester(self			,     config_class=lowerCamelCase			,     has_text_modality=lowerCamelCase			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     @unittest.skip(reason="ViTMAE does not use inputs_embeds"		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       List[Any]      =							model_class(lowerCamelCase		)
               self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)		)
               __A	:       str      =							model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase			,     nn.Linear		)		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       List[str]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       int      =							model_class(lowerCamelCase		)
               __A	:       Union[str, Any]      =							inspect.signature(model.forward		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A	:       Optional[Any]      =							[*signature.parameters.keys()]

               __A	:       Any      =							["pixel_values"]
               self.assertListEqual(arg_names[:1]			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          np.random.seed(2		)

          __A	:       Tuple      =							int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
          __A	:       Optional[Any]      =							np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
          __A	:       Optional[Any]      =							torch.from_numpy(lowerCamelCase		)

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          __A	:       int      =							pt_noise

          super().check_pt_tf_models(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       List[str]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       int      =							model_class(lowerCamelCase		)
               model.to(lowerCamelCase		)
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2		)
               with torch.no_grad():
                    __A	:       List[str]      =							model(**self._prepare_for_class(lowerCamelCase			,     lowerCamelCase		)		)

               __A	:       Tuple      =							outputs[0].cpu().numpy()
               __A	:       List[Any]      =							0

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(lowerCamelCase		)
                    __A	:       List[Any]      =							model_class.from_pretrained(lowerCamelCase		)
                    model.to(lowerCamelCase		)
                    # make random mask reproducible
                    torch.manual_seed(2		)
                    with torch.no_grad():
                         __A	:       Optional[Any]      =							model(**self._prepare_for_class(lowerCamelCase			,     lowerCamelCase		)		)

                    # Make sure we don't have nans
                    __A	:       List[Any]      =							after_outputs[0].cpu().numpy()
                    __A	:       List[str]      =							0
                    __A	:       int      =							np.amax(np.abs(out_a - out_a		)		)
                    self.assertLessEqual(lowerCamelCase			,     1E-5		)


     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          pass


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A	:       List[Any]      =							ViTMAEModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Union[str, Any]      =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image







@require_torch
@require_vision
class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     @cached_property
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"		) if is_vision_available() else None


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          np.random.seed(2		)

          __A	:       Dict      =							ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"		).to(lowerCamelCase		)

          __A	:       str      =							self.default_image_processor
          __A	:       List[Any]      =							prepare_img()
          __A	:       Union[str, Any]      =							image_processor(images=lowerCamelCase			,     return_tensors="pt"		).to(lowerCamelCase		)

          # prepare a noise vector that will be also used for testing the TF model
          # (this way we can ensure that the PT and TF models operate on the same inputs)
          __A	:       Optional[Any]      =							ViTMAEConfig()
          __A	:       List[str]      =							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
          __A	:       Any      =							np.random.uniform(size=(1, num_patches)		)

          # forward pass
          with torch.no_grad():
               __A	:       str      =							model(**lowerCamelCase			,     noise=torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)		)

          # verify the logits
          __A	:       Union[str, Any]      =							torch.Size((1, 196, 768)		)
          self.assertEqual(outputs.logits.shape			,     lowerCamelCase		)

          __A	:       int      =							torch.tensor(
              [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]		)

          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,     expected_slice.to(lowerCamelCase		)			,     atol=1E-4		)		)

