import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   TextToVideoSDPipeline
    __a						=   TEXT_TO_IMAGE_PARAMS
    __a						=   TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    __a						=   frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback",
            "callback_steps",
        ]     )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: List[str]=   UNetaDConditionModel(
         block_out_channels=(32, 64, 64, 64)			,			layers_per_block=2			,			sample_size=32			,			in_channels=4			,			out_channels=4			,			down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')			,			up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')			,			cross_attention_dim=32			,			attention_head_dim=4			,			)
     SCREAMING_SNAKE_CASE__: Optional[int]=   DDIMScheduler(
         beta_start=0.00085			,			beta_end=0.012			,			beta_schedule='''scaled_linear'''			,			clip_sample=lowerCAmelCase			,			set_alpha_to_one=lowerCAmelCase			,			)
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: int=   AutoencoderKL(
         block_out_channels=[32, 64]			,			in_channels=3			,			out_channels=3			,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,			latent_channels=4			,			sample_size=128			,			)
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__: str=   CLIPTextConfig(
         bos_token_id=0			,			eos_token_id=2			,			hidden_size=32			,			intermediate_size=37			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1000			,			hidden_act='''gelu'''			,			projection_dim=512			,			)
     SCREAMING_SNAKE_CASE__: Dict=   CLIPTextModel(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: int=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

     SCREAMING_SNAKE_CASE__: Tuple=   {
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
     }
     return components
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase=0 )							->       Dict:
     if str(lowerCAmelCase ).startswith('''mps''' ):
      SCREAMING_SNAKE_CASE__: Any=   torch.manual_seed(lowerCAmelCase )
     else:
      SCREAMING_SNAKE_CASE__: Any=   torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Optional[Any]=   {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 6.0,
         '''output_type''': '''pt''',
     }
     return inputs
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: str=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__: List[Any]=   self.get_dummy_components()
     SCREAMING_SNAKE_CASE__: List[Any]=   TextToVideoSDPipeline(**lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Tuple=   sd_pipe.to(lowerCAmelCase )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Dict=   self.get_dummy_inputs(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: Any=   '''np'''
     SCREAMING_SNAKE_CASE__: Optional[int]=   sd_pipe(**lowerCAmelCase ).frames
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   frames[0][-3:, -3:, -1]

     assert frames[0].shape == (64, 64, 3)
     SCREAMING_SNAKE_CASE__: Any=   np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
    def     UpperCamelCase_					(	self )							->       int:
     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCAmelCase			,			expected_max_diff=3e-3 )
    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()			,			reason='''XFormers attention is only available with CUDA and `xformers` installed'''			,			)
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase			,			expected_max_diff=1e-2 )
    @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
    def     UpperCamelCase_					(	self )							->       List[str]:
     pass
    @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     pass
    @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
    def     UpperCamelCase_					(	self )							->       Dict:
     pass



    def     UpperCamelCase_					(	self )							->       Dict:
     return super().test_progress_bar()






@slow
@skip_mps
class 		_lowerCamelCase		( unittest.TestCase     ):
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''' )

     SCREAMING_SNAKE_CASE__: Any=   TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
     SCREAMING_SNAKE_CASE__: int=   pipe.to('''cuda''' )

     SCREAMING_SNAKE_CASE__: List[str]=   '''Spiderman is surfing'''
     SCREAMING_SNAKE_CASE__: Tuple=   torch.Generator(device='''cpu''' ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__: Optional[int]=   pipe(lowerCAmelCase			,			generator=lowerCAmelCase			,			num_inference_steps=25			,			output_type='''pt''' ).frames
     SCREAMING_SNAKE_CASE__: List[str]=   video_frames.cpu().numpy()

     assert np.abs(expected_video - video ).mean() < 5e-2



    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: List[str]=   load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''' )

     SCREAMING_SNAKE_CASE__: List[Any]=   TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
     SCREAMING_SNAKE_CASE__: Any=   pipe.to('''cuda''' )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   '''Spiderman is surfing'''
     SCREAMING_SNAKE_CASE__: Tuple=   torch.Generator(device='''cpu''' ).manual_seed(0 )

     SCREAMING_SNAKE_CASE__: Dict=   pipe(lowerCAmelCase			,			generator=lowerCAmelCase			,			num_inference_steps=2			,			output_type='''pt''' ).frames
     SCREAMING_SNAKE_CASE__: Dict=   video_frames.cpu().numpy()

     assert np.abs(expected_video - video ).mean() < 5e-2





'''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()

