
import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
							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     lowerCamelCase      :







	def __init__(     self      :Optional[Any]   ,     lowercase      :str   ,     lowercase      :Optional[Any]=1_3   ,     lowercase      :Any=3_0   ,     lowercase      :Optional[int]=2   ,     lowercase      :Tuple=3   ,     lowercase      :List[Any]=True   ,     lowercase      :List[str]=True   ,     lowercase      :str=3_2   ,     lowercase      :List[str]=5   ,     lowercase      :int=4   ,     lowercase      :Union[str, Any]=3_7   ,     lowercase      :str="gelu"   ,     lowercase      :List[Any]=0.1   ,     lowercase      :Tuple=0.1   ,     lowercase      :Tuple=1_0   ,     lowercase      :Dict=0.02   ,     lowercase      :Tuple=None   ,     lowercase      :Any=2   ,     )		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= parent
				SCREAMING_SNAKE_CASE     						= batch_size
				SCREAMING_SNAKE_CASE     						= image_size
				SCREAMING_SNAKE_CASE     						= patch_size
				SCREAMING_SNAKE_CASE     						= num_channels
				SCREAMING_SNAKE_CASE     						= is_training
				SCREAMING_SNAKE_CASE     						= use_labels
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= type_sequence_label_size
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= scope
				SCREAMING_SNAKE_CASE     						= encoder_stride

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE     						= (image_size // patch_size) ** 2
				SCREAMING_SNAKE_CASE     						= num_patches + 1







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

				SCREAMING_SNAKE_CASE     						= self.get_config()

				return config, pixel_values, labels







	def  snake_case__     (     self      :List[str]			)		->      int:







				"""simple docstring"""






				return ViTConfig(
				    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=lowercase   ,     initializer_range=self.initializer_range   ,     encoder_stride=self.encoder_stride   ,     )







	def  snake_case__     (     self      :Tuple   ,     lowercase      :Tuple   ,     lowercase      :List[Any]   ,     lowercase      :Optional[Any]			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ViTModel(config=lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(lowercase			)
				self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)			)







	def  snake_case__     (     self      :Dict   ,     lowercase      :Any   ,     lowercase      :Tuple   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ViTForMaskedImageModeling(config=lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(lowercase			)
				self.parent.assertEqual(
				    result.reconstruction.shape   ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

				# test greyscale images
				SCREAMING_SNAKE_CASE     						= 1
				SCREAMING_SNAKE_CASE     						= ViTForMaskedImageModeling(lowercase			)
				model.to(lowercase			)
				model.eval()

				SCREAMING_SNAKE_CASE     						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
				SCREAMING_SNAKE_CASE     						= model(lowercase			)
				self.parent.assertEqual(result.reconstruction.shape   ,     (self.batch_size, 1, self.image_size, self.image_size)			)







	def  snake_case__     (     self      :str   ,     lowercase      :Dict   ,     lowercase      :List[str]   ,     lowercase      :Optional[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.type_sequence_label_size
				SCREAMING_SNAKE_CASE     						= ViTForImageClassification(lowercase			)
				model.to(lowercase			)
				model.eval()
				SCREAMING_SNAKE_CASE     						= model(lowercase   ,     labels=lowercase			)
				self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size)			)

				# test greyscale images
				SCREAMING_SNAKE_CASE     						= 1
				SCREAMING_SNAKE_CASE     						= ViTForImageClassification(lowercase			)
				model.to(lowercase			)
				model.eval()

				SCREAMING_SNAKE_CASE     						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
				SCREAMING_SNAKE_CASE     						= model(lowercase			)
				self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size)			)







	def  snake_case__     (     self      :int			)		->      Optional[int]:







				"""simple docstring"""






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





@require_torch
class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					Dict    		=	(
	    (
	        ViTModel,
	        ViTForImageClassification,
	        ViTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase_  :					Any    		=	(
	    {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
	    if is_torch_available()
	    else {}
	)
	UpperCamelCase_  :					Optional[Any]    		=	True

	UpperCamelCase_  :					Optional[Any]    		=	False
	UpperCamelCase_  :					Union[str, Any]    		=	False
	UpperCamelCase_  :					Tuple    		=	False







	def  snake_case__     (     self      :str			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ViTModelTester(self			)
				SCREAMING_SNAKE_CASE     						= ConfigTester(self   ,     config_class=lowercase   ,     has_text_modality=lowercase   ,     hidden_size=3_7			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






				self.config_tester.run_common_tests()







	@unittest.skip(reason='''ViT does not use inputs_embeds'''			)
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				pass







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs_for_common()

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







	def  snake_case__     (     self      :Tuple			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs_for_common()

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

							SCREAMING_SNAKE_CASE     						= ['''pixel_values''']
							self.assertListEqual(arg_names[:1]   ,     lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase			)







	@slow
	def  snake_case__     (     self      :List[str]			)		->      Any:







				"""simple docstring"""






				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE     						= ViTModel.from_pretrained(lowercase			)
							self.assertIsNotNone(lowercase			)



def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
			return image





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







	@cached_property
	def  snake_case__     (     self      :Dict			)		->      Optional[int]:







				"""simple docstring"""






				return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''			) if is_vision_available() else None







	@slow
	def  snake_case__     (     self      :Optional[int]			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''			).to(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.default_image_processor
				SCREAMING_SNAKE_CASE     						= prepare_img()
				SCREAMING_SNAKE_CASE     						= image_processor(images=lowercase   ,     return_tensors='''pt'''			).to(lowercase			)

				# forward pass
				with torch.no_grad():
							SCREAMING_SNAKE_CASE     						= model(**lowercase			)

				# verify the logits
				SCREAMING_SNAKE_CASE     						= torch.Size((1, 1_0_0_0)			)
				self.assertEqual(outputs.logits.shape   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= torch.tensor([-0.27_44, 0.82_15, -0.08_36]			).to(lowercase			)

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







	@slow
	def  snake_case__     (     self      :Tuple			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ViTModel.from_pretrained('''facebook/dino-vits8'''			).to(lowercase			)

				SCREAMING_SNAKE_CASE     						= ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''   ,     size=4_8_0			)
				SCREAMING_SNAKE_CASE     						= prepare_img()
				SCREAMING_SNAKE_CASE     						= image_processor(images=lowercase   ,     return_tensors='''pt'''			)
				SCREAMING_SNAKE_CASE     						= inputs.pixel_values.to(lowercase			)

				# forward pass
				with torch.no_grad():
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     interpolate_pos_encoding=lowercase			)

				# verify the logits
				SCREAMING_SNAKE_CASE     						= torch.Size((1, 3_6_0_1, 3_8_4)			)
				self.assertEqual(outputs.last_hidden_state.shape   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= torch.tensor(
				    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]			).to(lowercase			)

				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,     lowercase   ,     atol=1e-4			)			)







	@slow
	@require_accelerate
	@require_torch_gpu
	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ViTModel.from_pretrained('''facebook/dino-vits8'''   ,     torch_dtype=torch.floataa   ,     device_map='''auto'''			)
				SCREAMING_SNAKE_CASE     						= self.default_image_processor

				SCREAMING_SNAKE_CASE     						= prepare_img()
				SCREAMING_SNAKE_CASE     						= image_processor(images=lowercase   ,     return_tensors='''pt'''			)
				SCREAMING_SNAKE_CASE     						= inputs.pixel_values.to(lowercase			)

				# forward pass to make sure inference works in fp16
				with torch.no_grad():
							SCREAMING_SNAKE_CASE     						= model(lowercase			)

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      ( a	)     ->List[Any]:




			'''simple docstring'''


			if hor == 128:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

			elif hor == 32:
						SCREAMING_SNAKE_CASE     						= ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
						SCREAMING_SNAKE_CASE     						= (32, 64, 128, 256)
						SCREAMING_SNAKE_CASE     						= ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
			SCREAMING_SNAKE_CASE     						= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""	)
			SCREAMING_SNAKE_CASE     						= model.state_dict()
			SCREAMING_SNAKE_CASE     						= {
			    '''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''',
			}
			SCREAMING_SNAKE_CASE     						= UNetaDModel(**a	)
			print(F"""length of state dict: {len(state_dict.keys()	)}"""	)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()	)}"""	)
			SCREAMING_SNAKE_CASE     						= dict(zip(model.state_dict().keys()		,						hf_value_function.state_dict().keys()	)	)
			for k, v in mapping.items():
						SCREAMING_SNAKE_CASE     						= state_dict.pop(a	)
			hf_value_function.load_state_dict(a	)

			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(a		,						a	)


def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= {
			    '''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''',
			}

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

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

			hf_value_function.load_state_dict(a	)

			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(a		,						a	)


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