


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 (
     MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
     VideoMAEForPreTraining,
     VideoMAEForVideoClassification,
     VideoMAEModel,
 )
 from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from transformers import VideoMAEImageProcessor







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=13		,				_UpperCAmelCase=10		,				_UpperCAmelCase=3		,				_UpperCAmelCase=2		,				_UpperCAmelCase=2		,				_UpperCAmelCase=2		,				_UpperCAmelCase=True		,				_UpperCAmelCase=True		,				_UpperCAmelCase=32		,				_UpperCAmelCase=5		,				_UpperCAmelCase=4		,				_UpperCAmelCase=37		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=10		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=0.9		,				_UpperCAmelCase=None		,				):
       snake_case_	=				parent
       snake_case_	=				batch_size
       snake_case_	=				image_size
       snake_case_	=				num_channels
       snake_case_	=				patch_size
       snake_case_	=				tubelet_size
       snake_case_	=				num_frames
       snake_case_	=				is_training
       snake_case_	=				use_labels
       snake_case_	=				hidden_size
       snake_case_	=				num_hidden_layers
       snake_case_	=				num_attention_heads
       snake_case_	=				intermediate_size
       snake_case_	=				hidden_act
       snake_case_	=				hidden_dropout_prob
       snake_case_	=				attention_probs_dropout_prob
       snake_case_	=				type_sequence_label_size
       snake_case_	=				initializer_range
       snake_case_	=				mask_ratio
       snake_case_	=				scope

       # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
       snake_case_	=				(image_size // patch_size) ** 2
       snake_case_	=				(num_frames // tubelet_size) * self.num_patches_per_frame

       # use this variable to define bool_masked_pos
       snake_case_	=				int(mask_ratio * self.seq_length )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				floats_tensor(
           [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )

       snake_case_	=				None
       if self.use_labels:
             snake_case_	=				ids_tensor([self.batch_size]		,				self.type_sequence_label_size )

       snake_case_	=				self.get_config()

       return config, pixel_values, labels



 def 						UpperCamelCase__					(     self ):
       return VideoMAEConfig(
           image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				num_frames=self.num_frames		,				tubelet_size=self.tubelet_size		,				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=_UpperCAmelCase		,				initializer_range=self.initializer_range		,				)



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				VideoMAEModel(config=_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       snake_case_	=				model(_UpperCAmelCase )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size) )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				VideoMAEForPreTraining(_UpperCAmelCase )
       model.to(_UpperCAmelCase )
       model.eval()
       # important: each video needs to have the same number of masked patches
       # hence we define a single mask, which we then repeat for each example in the batch
       snake_case_	=				torch.ones((self.num_masks,) )
       snake_case_	=				torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
       snake_case_	=				mask.expand(self.batch_size		,				-1 ).bool()

       snake_case_	=				model(_UpperCAmelCase		,				_UpperCAmelCase )
       # model only returns predictions for masked patches
       snake_case_	=				mask.sum().item()
       snake_case_	=				3 * self.tubelet_size * self.patch_size**2
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_masked_patches, decoder_num_labels) )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.prepare_config_and_inputs()
       snake_case_	,   snake_case_	,   snake_case_	=				config_and_inputs
       snake_case_	=				{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  (
     (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
 )
 __snake_case												=  (
     {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
     if is_torch_available()
     else {}
 )

 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False
 __snake_case												=  False



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				VideoMAEModelTester(self )
       snake_case_	=				ConfigTester(self		,				config_class=_UpperCAmelCase		,				has_text_modality=_UpperCAmelCase		,				hidden_size=37 )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase=False ):
       snake_case_	=				copy.deepcopy(_UpperCAmelCase )

       if model_class == VideoMAEForPreTraining:
             # important: each video needs to have the same number of masked patches
             # hence we define a single mask, which we then repeat for each example in the batch
             snake_case_	=				torch.ones((self.model_tester.num_masks,) )
             snake_case_	=				torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
             snake_case_	=				mask.expand(self.model_tester.batch_size		,				-1 ).bool()
             snake_case_	=				bool_masked_pos.to(_UpperCAmelCase )

       if return_labels:
             if model_class in [
                 *get_values(_UpperCAmelCase ),
             ]:
                   snake_case_	=				torch.zeros(
                       self.model_tester.batch_size		,				dtype=torch.long		,				device=_UpperCAmelCase )

       return inputs_dict



 def 						UpperCamelCase__					(     self ):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='''VideoMAE does not use inputs_embeds''' )
 def 						UpperCamelCase__					(     self ):
       pass



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             snake_case_	=				model_class(_UpperCAmelCase )
             self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module) )
             snake_case_	=				model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase		,				nn.Linear ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             snake_case_	=				model_class(_UpperCAmelCase )
             snake_case_	=				inspect.signature(model.forward )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             snake_case_	=				[*signature.parameters.keys()]

             snake_case_	=				['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase )



 @slow
 def 						UpperCamelCase__					(     self ):
       for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_	=				VideoMAEModel.from_pretrained(_UpperCAmelCase )
             self.assertIsNotNone(_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       if not self.has_attentions:
             pass

       else:
             snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()
             snake_case_	=				True

             for model_class in self.all_model_classes:
                   snake_case_	=				self.model_tester.seq_length - self.model_tester.num_masks
                   snake_case_	=				(
                       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                   )

                   snake_case_	=				True
                   snake_case_	=				False
                   snake_case_	=				True
                   snake_case_	=				model_class(_UpperCAmelCase )
                   model.to(_UpperCAmelCase )
                   model.eval()
                   with torch.no_grad():
                         snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )
                   snake_case_	=				outputs.attentions
                   self.assertEqual(len(_UpperCAmelCase )		,				self.model_tester.num_hidden_layers )

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   snake_case_	=				True
                   snake_case_	=				model_class(_UpperCAmelCase )
                   model.to(_UpperCAmelCase )
                   model.eval()
                   with torch.no_grad():
                         snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )
                   snake_case_	=				outputs.attentions
                   self.assertEqual(len(_UpperCAmelCase )		,				self.model_tester.num_hidden_layers )

                   self.assertListEqual(
                       list(attentions[0].shape[-3:] )		,				[self.model_tester.num_attention_heads, seq_len, seq_len]		,				)
                   snake_case_	=				len(_UpperCAmelCase )

                   # Check attention is always last and order is fine
                   snake_case_	=				True
                   snake_case_	=				True
                   snake_case_	=				model_class(_UpperCAmelCase )
                   model.to(_UpperCAmelCase )
                   model.eval()
                   with torch.no_grad():
                         snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )

                   self.assertEqual(out_len + 1		,				len(_UpperCAmelCase ) )

                   snake_case_	=				outputs.attentions

                   self.assertEqual(len(_UpperCAmelCase )		,				self.model_tester.num_hidden_layers )
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:] )		,				[self.model_tester.num_attention_heads, seq_len, seq_len]		,				)



 def 						UpperCamelCase__					(     self ):
       def check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
             snake_case_	=				model_class(_UpperCAmelCase )
             model.to(_UpperCAmelCase )
             model.eval()

             with torch.no_grad():
                   snake_case_	=				model(**self._prepare_for_class(_UpperCAmelCase		,				_UpperCAmelCase ) )

             snake_case_	=				outputs.hidden_states
             snake_case_	=				self.model_tester.num_hidden_layers + 1
             self.assertEqual(len(_UpperCAmelCase )		,				_UpperCAmelCase )

             snake_case_	=				self.model_tester.seq_length - self.model_tester.num_masks
             snake_case_	=				num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:] )		,				[seq_length, self.model_tester.hidden_size]		,				)

       snake_case_	,   snake_case_	=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             snake_case_	=				True
             check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             snake_case_	=				True

             check_hidden_states_output(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )





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






def 	__lowerCAmelCase   ()->  Optional[int]:




      """simple docstring"""

      snake_case_	=				hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video''' , filename='''eating_spaghetti.npy''' , repo_type='''dataset''' )
      snake_case_	=				np.load(SCREAMING_SNAKE_CASE )
      return list(SCREAMING_SNAKE_CASE )







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

 '''simple docstring'''

 @cached_property
 def 						UpperCamelCase__					(     self ):
       # logits were tested with a different mean and std, so we use the same here
       return (
           VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,				image_std=[0.5, 0.5, 0.5] )
           if is_vision_available()
           else None
       )



 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics''' ).to(
           _UpperCAmelCase )

       snake_case_	=				self.default_image_processor
       snake_case_	=				prepare_video()
       snake_case_	=				image_processor(_UpperCAmelCase		,				return_tensors='''pt''' ).to(_UpperCAmelCase )

       # forward pass
       with torch.no_grad():
             snake_case_	=				model(**_UpperCAmelCase )

       # verify the logits
       snake_case_	=				torch.Size((1, 4_00) )
       self.assertEqual(outputs.logits.shape		,				_UpperCAmelCase )

       snake_case_	=				torch.tensor([0.3_669, -0.0_688, -0.2_421] ).to(_UpperCAmelCase )

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





 @slow
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short''' ).to(_UpperCAmelCase )

       snake_case_	=				self.default_image_processor
       snake_case_	=				prepare_video()
       snake_case_	=				image_processor(_UpperCAmelCase		,				return_tensors='''pt''' ).to(_UpperCAmelCase )

       # add boolean mask, indicating which patches to mask
       snake_case_	=				hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''		,				filename='''bool_masked_pos.pt''' )
       snake_case_	=				torch.load(_UpperCAmelCase )

       # forward pass
       with torch.no_grad():
             snake_case_	=				model(**_UpperCAmelCase )

       # verify the logits
       snake_case_	=				torch.Size([1, 14_08, 15_36] )
       snake_case_	=				torch.tensor(
           [[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]		,				device=_UpperCAmelCase )
       self.assertEqual(outputs.logits.shape		,				_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,				_UpperCAmelCase		,				atol=1E-4 ) )

       # verify the loss (`config.norm_pix_loss` = `True`)
       snake_case_	=				torch.tensor([0.5_142]		,				device=_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.loss		,				_UpperCAmelCase		,				atol=1E-4 ) )

       # verify the loss (`config.norm_pix_loss` = `False`)
       snake_case_	=				VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''		,				norm_pix_loss=_UpperCAmelCase ).to(
           _UpperCAmelCase )

       with torch.no_grad():
             snake_case_	=				model(**_UpperCAmelCase )

       snake_case_	=				torch.tensor(torch.tensor([0.6_469] )		,				device=_UpperCAmelCase )
       self.assertTrue(torch.allclose(outputs.loss		,				_UpperCAmelCase		,				atol=1E-4 ) )



import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       super().__init__()
       snake_case_	=				value_function
       snake_case_	=				unet
       snake_case_	=				scheduler
       snake_case_	=				env
       snake_case_	=				env.get_dataset()
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].mean()
             except:  # noqa: E722
                   pass
       snake_case_	=				{}
       for key in self.data.keys():
             try:
                   snake_case_	=				self.data[key].std()
             except:  # noqa: E722
                   pass
       snake_case_	=				env.observation_space.shape[0]
       snake_case_	=				env.action_space.shape[0]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       if type(_UpperCAmelCase ) is dict:
             return {k: self.to_torch(_UpperCAmelCase ) for k, v in x_in.items()}
       elif torch.is_tensor(_UpperCAmelCase ):
             return x_in.to(self.unet.device )
       return torch.tensor(_UpperCAmelCase		,				device=self.unet.device )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				x.shape[0]
       snake_case_	=				None
       for i in tqdm.tqdm(self.scheduler.timesteps ):
             # create batch of timesteps to pass into model
             snake_case_	=				torch.full((batch_size,)		,				_UpperCAmelCase		,				device=self.unet.device		,				dtype=torch.long )
             for _ in range(_UpperCAmelCase ):
                   with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         snake_case_	=				self.value_function(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         snake_case_	=				torch.exp(0.5 * posterior_variance )
                         snake_case_	=				model_std * grad

                   snake_case_	=				0
                   snake_case_	=				x.detach()
                   snake_case_	=				x + scale * grad
                   snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

             snake_case_	=				self.unet(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample.permute(0		,				2		,				1 )

             # TODO: verify deprecation of this kwarg
             snake_case_	=				self.scheduler.step(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				predict_epsilon=_UpperCAmelCase )['''prev_sample''']

             # apply conditions to the trajectory (set the initial state)
             snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
             snake_case_	=				self.to_torch(_UpperCAmelCase )
       return x, y





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

       snake_case_	=				{0: self.to_torch(_UpperCAmelCase )}
       snake_case_	=				(batch_size, planning_horizon, self.state_dim + self.action_dim)

       # generate initial noise and apply our conditions (to make the trajectories start at current state)
       snake_case_	=				randn_tensor(_UpperCAmelCase		,				device=self.unet.device )
       snake_case_	=				self.reset_xa(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )
       snake_case_	=				self.to_torch(_UpperCAmelCase )

       # run the diffusion process
       snake_case_	,   snake_case_	=				self.run_diffusion(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )

       # sort output trajectories by value
       snake_case_	=				y.argsort(0		,				descending=_UpperCAmelCase ).squeeze()
       snake_case_	=				x[sorted_idx]
       snake_case_	=				sorted_values[:, :, : self.action_dim]
       snake_case_	=				actions.detach().cpu().numpy()
       snake_case_	=				self.de_normalize(_UpperCAmelCase		,				key='''actions''' )

       # select the action with the highest value
       if y is not None:
             snake_case_	=				0
       else:
             # if we didn't run value guiding, select a random action
             snake_case_	=				np.random.randint(0		,				_UpperCAmelCase )

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions