



"""simple docstring"""






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class     A__     (						__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			AltDiffusionPipeline
 SCREAMING_SNAKE_CASE					   =			TEXT_TO_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE					   =			TEXT_TO_IMAGE_BATCH_PARAMS
 SCREAMING_SNAKE_CASE					   =			TEXT_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE					   =			TEXT_TO_IMAGE_IMAGE_PARAMS

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Tuple:

    """simple docstring"""

    torch.manual_seed(0)
    __lowerCAmelCase   :						Optional[Any]				= UNetaDConditionModel(
        block_out_channels=(32, 64) ,    layers_per_block=2 ,    sample_size=32 ,    in_channels=4 ,    out_channels=4 ,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,    cross_attention_dim=32 ,    )
    __lowerCAmelCase   :						Union[str, Any]				= DDIMScheduler(
        beta_start=0.0_0085 ,    beta_end=0.012 ,    beta_schedule="scaled_linear" ,    clip_sample=_SCREAMING_SNAKE_CASE ,    set_alpha_to_one=_SCREAMING_SNAKE_CASE ,    )
    torch.manual_seed(0)
    __lowerCAmelCase   :						Dict				= 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 ,    )

    # TODO: address the non-deterministic text encoder (fails for save-load tests)
    # torch.manual_seed(0)
    # text_encoder_config = RobertaSeriesConfig(
    #     hidden_size=32,
    #     project_dim=32,
    #     intermediate_size=37,
    #     layer_norm_eps=1e-05,
    #     num_attention_heads=4,
    #     num_hidden_layers=5,
    #     vocab_size=5002,
    # )
    # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

    torch.manual_seed(0)
    __lowerCAmelCase   :						str				= CLIPTextConfig(
        bos_token_id=0 ,    eos_token_id=2 ,    hidden_size=32 ,    projection_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1e-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=5002 ,    )
    __lowerCAmelCase   :						Tuple				= CLIPTextModel(_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						Any				= XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
    __lowerCAmelCase   :						Optional[Any]				= 77

    __lowerCAmelCase   :						Union[str, Any]				= {
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    return components

 def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	str=0)       -> List[Any]:

    """simple docstring"""

    if str(_SCREAMING_SNAKE_CASE).startswith("mps"):
       __lowerCAmelCase   :						Any				= torch.manual_seed(_SCREAMING_SNAKE_CASE)
    else:
       __lowerCAmelCase   :						Union[str, Any]				= torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Any				= {
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }
    return inputs

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

    """simple docstring"""

    super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Tuple:

    """simple docstring"""

    super().test_inference_batch_single_identical(expected_max_diff=3e-3)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						Dict				= "cpu"  # ensure determinism for the device-dependent torch.Generator

    __lowerCAmelCase   :						int				= self.get_dummy_components()
    torch.manual_seed(0)
    __lowerCAmelCase   :						List[str]				= RobertaSeriesConfig(
        hidden_size=32 ,    project_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1e-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    vocab_size=5002 ,    )
    # TODO: remove after fixing the non-deterministic text encoder
    __lowerCAmelCase   :						List[Any]				= RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						int				= text_encoder

    __lowerCAmelCase   :						List[Any]				= AltDiffusionPipeline(**_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Optional[Any]				= alt_pipe.to(_SCREAMING_SNAKE_CASE)
    alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						Tuple				= self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Any				= "A photo of an astronaut"
    __lowerCAmelCase   :						Optional[Any]				= alt_pipe(**_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Optional[Any]				= output.images
    __lowerCAmelCase   :						List[Any]				= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    __lowerCAmelCase   :						List[Any]				= np.array(
        [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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







 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						int				= "cpu"  # ensure determinism for the device-dependent torch.Generator

    __lowerCAmelCase   :						Tuple				= self.get_dummy_components()
    __lowerCAmelCase   :						List[Any]				= PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE)
    torch.manual_seed(0)
    __lowerCAmelCase   :						Union[str, Any]				= RobertaSeriesConfig(
        hidden_size=32 ,    project_dim=32 ,    intermediate_size=37 ,    layer_norm_eps=1e-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    vocab_size=5002 ,    )
    # TODO: remove after fixing the non-deterministic text encoder
    __lowerCAmelCase   :						Any				= RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Tuple				= text_encoder
    __lowerCAmelCase   :						Dict				= AltDiffusionPipeline(**_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						str				= alt_pipe.to(_SCREAMING_SNAKE_CASE)
    alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						int				= self.get_dummy_inputs(_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Optional[int]				= alt_pipe(**_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						List[Any]				= output.images
    __lowerCAmelCase   :						List[str]				= image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    __lowerCAmelCase   :						Dict				= np.array(
        [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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






@slow
@require_torch_gpu
class     A__     (						unittest.TestCase							):




 '''simple docstring'''





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

    """simple docstring"""

    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Union[str, Any]:

    """simple docstring"""

    __lowerCAmelCase   :						str				= AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" ,    safety_checker=_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						Dict				= alt_pipe.to(_SCREAMING_SNAKE_CASE)
    alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						int				= "A painting of a squirrel eating a burger"
    __lowerCAmelCase   :						List[Any]				= torch.manual_seed(0)
    __lowerCAmelCase   :						Union[str, Any]				= alt_pipe([prompt] ,    generator=_SCREAMING_SNAKE_CASE ,    guidance_scale=6.0 ,    num_inference_steps=20 ,    output_type="np")

    __lowerCAmelCase   :						str				= output.images

    __lowerCAmelCase   :						List[str]				= image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCAmelCase   :						Union[str, Any]				= np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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







 def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[Any]				= DDIMScheduler.from_pretrained("BAAI/AltDiffusion" ,    subfolder="scheduler")

    __lowerCAmelCase   :						Union[str, Any]				= AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" ,    scheduler=_SCREAMING_SNAKE_CASE ,    safety_checker=_SCREAMING_SNAKE_CASE)
    __lowerCAmelCase   :						List[str]				= alt_pipe.to(_SCREAMING_SNAKE_CASE)
    alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

    __lowerCAmelCase   :						Dict				= "A painting of a squirrel eating a burger"
    __lowerCAmelCase   :						List[str]				= torch.manual_seed(0)

    __lowerCAmelCase   :						Tuple				= alt_pipe([prompt] ,    generator=_SCREAMING_SNAKE_CASE ,    num_inference_steps=2 ,    output_type="numpy")
    __lowerCAmelCase   :						Union[str, Any]				= output.images

    __lowerCAmelCase   :						Union[str, Any]				= image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCAmelCase   :						Optional[int]				= np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)