



from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    metadata={'help': 'The output directory where the model will be written.'} ,				)
				A_   :     str								=     field(
				    metadata={
				        'help': (
				            'The encoder model checkpoint for weights initialization.'
				            'Don\'t set if you want to train an encoder model from scratch.'
				        )
				    } ,				)
				A_   :     str								=     field(
				    metadata={
				        'help': (
				            'The decoder model checkpoint for weights initialization.'
				            'Don\'t set if you want to train a decoder model from scratch.'
				        )
				    } ,				)
				A_   :     Optional[str]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}       )
				A_   :     Optional[str]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}       )






def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    HfArgumentParser((ModelArguments,)				)
							((A)						,   )      						=    parser.parse_args_into_dataclasses()

							# Load pretrained model and tokenizer

							# Use explicit specified encoder config
							if model_args.encoder_config_name:
														A      						=    AutoConfig.from_pretrained(model_args.encoder_config_name				)
							# Use pretrained encoder model's config
							else:
														A      						=    AutoConfig.from_pretrained(model_args.encoder_model_name_or_path				)

							# Use explicit specified decoder config
							if model_args.decoder_config_name:
														A      						=    AutoConfig.from_pretrained(model_args.decoder_config_name				)
							# Use pretrained decoder model's config
							else:
														A      						=    AutoConfig.from_pretrained(model_args.decoder_model_name_or_path				)

							# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
							A      						=    True
							A      						=    True

							A      						=    FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
							    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path			,	decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path			,	encoder_config=lowerCAmelCase__			,	decoder_config=lowerCAmelCase__			,	)

							# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
							A      						=    decoder_config.decoder_start_token_id
							A      						=    decoder_config.pad_token_id
							if decoder_start_token_id is None:
														A      						=    decoder_config.bos_token_id
							if pad_token_id is None:
														A      						=    decoder_config.eos_token_id

							# This is necessary to make Flax's generate() work
							A      						=    decoder_config.eos_token_id
							A      						=    decoder_start_token_id
							A      						=    pad_token_id

							A      						=    AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path				)

							A      						=    AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path				)
							A      						=    tokenizer.convert_ids_to_tokens(model.config.pad_token_id				)

							model.save_pretrained(model_args.output_dir				)
							image_processor.save_pretrained(model_args.output_dir				)
							tokenizer.save_pretrained(model_args.output_dir				)


if __name__ == "__main__":
				main()




import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     VideoToVideoSDPipeline
				A_   :     Optional[Any]								=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'video'}       ) - {'image', 'width', 'height'}
				A_   :     List[str]								=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'video'}       ) - {'image'}
				A_   :     Tuple								=     PipelineTesterMixin.required_optional_params - {'latents'}
				A_   :     Dict								=     False

				# No `output_type`.
				A_   :     str								=     frozenset(
				    [
				        'num_inference_steps',
				        'generator',
				        'latents',
				        'return_dict',
				        'callback',
				        'callback_steps',
				    ]       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[Any]:
											torch.manual_seed(0       )
											A      						=    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						, )
											A      						=    DDIMScheduler(
											    beta_start=0.0_0_0_8_5						, beta_end=0.0_1_2						, beta_schedule='scaled_linear'						, clip_sample=__UpperCamelCase						, set_alpha_to_one=__UpperCamelCase						, )
											torch.manual_seed(0       )
											A      						=    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       )
											A      						=    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=1_000						, hidden_act='gelu'						, projection_dim=512						, )
											A      						=    CLIPTextModel(__UpperCamelCase       )
											A      						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

											A      						=    {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  str=0       )		->     Optional[int]:
											# 3 frames
											A      						=    floats_tensor((1, 3, 3, 32, 32)						, rng=random.Random(__UpperCamelCase       )       ).to(__UpperCamelCase       )

											if str(__UpperCamelCase       ).startswith('mps'       ):
																		A      						=    torch.manual_seed(__UpperCamelCase       )
											else:
																		A      						=    torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
											A      						=    {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'video': video,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'pt',
											}
											return inputs


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    'cpu'  # ensure determinism for the device-dependent torch.Generator
											A      						=    self.get_dummy_components()
											A      						=    VideoToVideoSDPipeline(**__UpperCamelCase       )
											A      						=    sd_pipe.to(__UpperCamelCase       )
											sd_pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    self.get_dummy_inputs(__UpperCamelCase       )
											A      						=    'np'
											A      						=    sd_pipe(**__UpperCamelCase       ).frames
											A      						=    frames[0][-3:, -3:, -1]

											assert frames[0].shape == (32, 32, 3)
											A      						=    np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()						, reason='XFormers attention is only available with CUDA and `xformers` installed'						, )
				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__UpperCamelCase						, expected_max_diff=5e-3       )


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


				@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'       )
				def __UpperCamelCase						(							self      :  str       )		->     Any:
											pass


				@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'       )
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											pass


				def __UpperCamelCase						(							self      :  Any       )		->     Any:
											return super().test_progress_bar()





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


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											A      						=    VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'						, torch_dtype=torch.floataa       )
											pipe.enable_model_cpu_offload()

											# 10 frames
											A      						=    torch.Generator(device='cpu'       ).manual_seed(0       )
											A      						=    torch.randn((1, 10, 3, 1_024, 576)						, generator=__UpperCamelCase       )
											A      						=    video.to('cuda'       )

											A      						=    'Spiderman is surfing'

											A      						=    pipe(__UpperCamelCase						, video=__UpperCamelCase						, generator=__UpperCamelCase						, num_inference_steps=3						, output_type='pt'       ).frames

											A      						=    np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]       )
											assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array       ).sum() < 1e-2