





import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)

class 		UpperCamelCase__ (      nn.Module   ):



     '''simple docstring'''

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




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE		=						nn.Linear(3						,4       )
           SCREAMING_SNAKE_CASE		=						nn.BatchNormad(4       )
           SCREAMING_SNAKE_CASE		=						nn.Linear(4						,5       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    Tuple       )     ->		Optional[Any]:




           '''simple docstring'''


           return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__       )       )       )

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						ModelForTest()
           with TemporaryDirectory() as tmp_dir:
                 offload_state_dict(lowerCamelCase__						,model.state_dict()       )
                 SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""index.json"""       )
                 self.assertTrue(os.path.isfile(lowerCamelCase__       )       )
                 # TODO: add tests on what is inside the index

                 for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                       SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,F"""{key}.dat"""       )
                       self.assertTrue(os.path.isfile(lowerCamelCase__       )       )
                       # TODO: add tests on the fact weights are properly loaded






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[torch.floataa, torch.floataa, torch.bfloataa]

           for dtype in dtypes:
                 SCREAMING_SNAKE_CASE		=						torch.randn(2						,3						,dtype=lowerCamelCase__       )
                 with TemporaryDirectory() as tmp_dir:
                       SCREAMING_SNAKE_CASE		=						offload_weight(lowerCamelCase__						,"""weight"""						,lowerCamelCase__						,{}       )
                       SCREAMING_SNAKE_CASE		=						os.path.join(lowerCamelCase__						,"""weight.dat"""       )
                       self.assertTrue(os.path.isfile(lowerCamelCase__       )       )
                       self.assertDictEqual(lowerCamelCase__						,{"""weight""": {"""shape""": [2, 3], """dtype""": str(lowerCamelCase__       ).split("""."""       )[1]}}       )

                       SCREAMING_SNAKE_CASE		=						load_offloaded_weight(lowerCamelCase__						,index["""weight"""]       )
                       self.assertTrue(torch.equal(lowerCamelCase__						,lowerCamelCase__       )       )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						ModelForTest()
           SCREAMING_SNAKE_CASE		=						model.state_dict()
           SCREAMING_SNAKE_CASE		=						{k: v for k, v in state_dict.items() if """linear2""" not in k}
           SCREAMING_SNAKE_CASE		=						{k: v for k, v in state_dict.items() if """linear2""" in k}

           with TemporaryDirectory() as tmp_dir:
                 offload_state_dict(lowerCamelCase__						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						OffloadedWeightsLoader(state_dict=lowerCamelCase__						,save_folder=lowerCamelCase__       )

                 # Every key is there with the right value
                 self.assertEqual(sorted(lowerCamelCase__       )						,sorted(state_dict.keys()       )       )
                 for key, param in state_dict.items():
                       self.assertTrue(torch.allclose(lowerCamelCase__						,weight_map[key]       )       )

           SCREAMING_SNAKE_CASE		=						{k: v for k, v in state_dict.items() if """weight""" in k}
           SCREAMING_SNAKE_CASE		=						{k: v for k, v in state_dict.items() if """weight""" not in k}

           with TemporaryDirectory() as tmp_dir:
                 offload_state_dict(lowerCamelCase__						,lowerCamelCase__       )
                 SCREAMING_SNAKE_CASE		=						OffloadedWeightsLoader(state_dict=lowerCamelCase__						,save_folder=lowerCamelCase__       )

                 # Every key is there with the right value
                 self.assertEqual(sorted(lowerCamelCase__       )						,sorted(state_dict.keys()       )       )
                 for key, param in state_dict.items():
                       self.assertTrue(torch.allclose(lowerCamelCase__						,weight_map[key]       )       )

           with TemporaryDirectory() as tmp_dir:
                 offload_state_dict(lowerCamelCase__						,lowerCamelCase__       )
                 # Duplicates are removed
                 SCREAMING_SNAKE_CASE		=						OffloadedWeightsLoader(state_dict=lowerCamelCase__						,save_folder=lowerCamelCase__       )

                 # Every key is there with the right value
                 self.assertEqual(sorted(lowerCamelCase__       )						,sorted(state_dict.keys()       )       )
                 for key, param in state_dict.items():
                       self.assertTrue(torch.allclose(lowerCamelCase__						,weight_map[key]       )       )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{"""a.1""": 0, """a.10""": 1, """a.2""": 2}
           SCREAMING_SNAKE_CASE		=						extract_submodules_state_dict(lowerCamelCase__						,["""a.1""", """a.2"""]       )
           self.assertDictEqual(lowerCamelCase__						,{"""a.1""": 0, """a.2""": 2}       )

           SCREAMING_SNAKE_CASE		=						{"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
           SCREAMING_SNAKE_CASE		=						extract_submodules_state_dict(lowerCamelCase__						,["""a.1""", """a.2"""]       )
           self.assertDictEqual(lowerCamelCase__						,{"""a.1.a""": 0, """a.2.a""": 2}       )









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 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			TextToVideoSDPipeline
     __snake_case    :	int			=			TEXT_TO_IMAGE_PARAMS
     __snake_case    :	Tuple			=			TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     __snake_case    :	Dict			=			frozenset(
         [
             "num_inference_steps",
             "generator",
             "latents",
             "return_dict",
             "callback",
             "callback_steps",
         ]   )






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




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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		=						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		=						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		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,hidden_act="""gelu"""						,projection_dim=512						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           SCREAMING_SNAKE_CASE		=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
           }
           return components






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    int=0       )     ->		List[Any]:




           '''simple docstring'''


           if str(lowerCamelCase__       ).startswith("""mps"""       ):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__       )
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						{
               """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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						sd_pipe.to(lowerCamelCase__       )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						"""np"""
           SCREAMING_SNAKE_CASE		=						sd_pipe(**lowerCamelCase__       ).frames
           SCREAMING_SNAKE_CASE		=						frames[0][-3:, -3:, -1]

           assert frames[0].shape == (64, 64, 3)
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		int:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[Any]:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           return super().test_progress_bar()

@slow
@skip_mps
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=25						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

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


