
'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 						a   (				unittest.TestCase    ):




				def 	__snake_case							(							self	:				Optional[int]      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				@property
				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     List[Any] =    1
										snake_case       :     Optional[Any] =    3
										snake_case       :     Optional[Any] =    (32, 32)

										snake_case       :     Tuple =    floats_tensor((batch_size, num_channels) + sizes,    rng=random.Random(0      )      ).to(SCREAMING_SNAKE_CASE_      )
										return image




				@property
				def 	__snake_case							(							self	:				int      ):
										torch.manual_seed(0      )
										snake_case       :     str =    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,    )
										return model




				@property
				def 	__snake_case							(							self	:				Dict      ):
										torch.manual_seed(0      )
										snake_case       :     List[Any] =    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,    )
										return model




				@property
				def 	__snake_case							(							self	:				Optional[int]      ):
										torch.manual_seed(0      )
										snake_case       :     Any =    RobertaSeriesConfig(
										    hidden_size=32,    project_dim=32,    intermediate_size=37,    layer_norm_eps=1e-05,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=50_06,    )
										return RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_      )




				@property
				def 	__snake_case							(							self	:				Tuple      ):
										def extract(*SCREAMING_SNAKE_CASE_	:				List[str],    **SCREAMING_SNAKE_CASE_	:				Tuple      ):

																class 						a   :




																				def __init__(							self	:				Any      ):
																										snake_case       :     str =    torch.ones([0]      )




																				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
																										self.pixel_values.to(SCREAMING_SNAKE_CASE_      )
																										return self




																return Out()

										return extract




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Optional[Any] =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case       :     str =    self.dummy_cond_unet
										snake_case       :     Tuple =    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Dict =    self.dummy_vae
										snake_case       :     Tuple =    self.dummy_text_encoder
										snake_case       :     Union[str, Any] =    XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
										snake_case       :     int =    77

										snake_case       :     Optional[int] =    self.dummy_image.to(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    init_image / 2 + 0.5

										# make sure here that pndm scheduler skips prk
										snake_case       :     int =    AltDiffusionImgaImgPipeline(
										    unet=SCREAMING_SNAKE_CASE_,    scheduler=SCREAMING_SNAKE_CASE_,    vae=SCREAMING_SNAKE_CASE_,    text_encoder=SCREAMING_SNAKE_CASE_,    tokenizer=SCREAMING_SNAKE_CASE_,    safety_checker=SCREAMING_SNAKE_CASE_,    feature_extractor=self.dummy_extractor,    )
										snake_case       :     int =    VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,    do_normalize=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    alt_pipe.to(SCREAMING_SNAKE_CASE_      )
										alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     int =    '''A painting of a squirrel eating a burger'''
										snake_case       :     List[Any] =    torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )
										snake_case       :     List[str] =    alt_pipe(
										    [prompt],    generator=SCREAMING_SNAKE_CASE_,    guidance_scale=6.0,    num_inference_steps=2,    output_type='''np''',    image=SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    output.images

										snake_case       :     Tuple =    torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(0      )
										snake_case       :     List[Any] =    alt_pipe(
										    [prompt],    generator=SCREAMING_SNAKE_CASE_,    guidance_scale=6.0,    num_inference_steps=2,    output_type='''np''',    image=SCREAMING_SNAKE_CASE_,    return_dict=SCREAMING_SNAKE_CASE_,    )[0]

										snake_case       :     Optional[int] =    image[0, -3:, -3:, -1]
										snake_case       :     Optional[Any] =    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										snake_case       :     Optional[int] =    np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 5e-3




				@unittest.skipIf(torch_device != '''cuda''',    '''This test requires a GPU'''      )
				def 	__snake_case							(							self	:				int      ):
										snake_case       :     Optional[Any] =    self.dummy_cond_unet
										snake_case       :     List[str] =    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    self.dummy_vae
										snake_case       :     Union[str, Any] =    self.dummy_text_encoder
										snake_case       :     str =    XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
										snake_case       :     int =    77

										snake_case       :     List[str] =    self.dummy_image.to(SCREAMING_SNAKE_CASE_      )

										# put models in fp16
										snake_case       :     int =    unet.half()
										snake_case       :     Dict =    vae.half()
										snake_case       :     str =    bert.half()

										# make sure here that pndm scheduler skips prk
										snake_case       :     Union[str, Any] =    AltDiffusionImgaImgPipeline(
										    unet=SCREAMING_SNAKE_CASE_,    scheduler=SCREAMING_SNAKE_CASE_,    vae=SCREAMING_SNAKE_CASE_,    text_encoder=SCREAMING_SNAKE_CASE_,    tokenizer=SCREAMING_SNAKE_CASE_,    safety_checker=SCREAMING_SNAKE_CASE_,    feature_extractor=self.dummy_extractor,    )
										snake_case       :     List[Any] =    VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,    do_normalize=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Optional[Any] =    alt_pipe.to(SCREAMING_SNAKE_CASE_      )
										alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case       :     List[str] =    '''A painting of a squirrel eating a burger'''
										snake_case       :     List[Any] =    torch.manual_seed(0      )
										snake_case       :     Union[str, Any] =    alt_pipe(
										    [prompt],    generator=SCREAMING_SNAKE_CASE_,    num_inference_steps=2,    output_type='''np''',    image=SCREAMING_SNAKE_CASE_,    ).images

										assert image.shape == (1, 32, 32, 3)




				@unittest.skipIf(torch_device != '''cuda''',    '''This test requires a GPU'''      )
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     int =    load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''      )
										# resize to resolution that is divisible by 8 but not 16 or 32
										snake_case       :     Dict =    init_image.resize((7_60, 5_04)      )

										snake_case       :     List[Any] =    '''BAAI/AltDiffusion'''
										snake_case       :     Optional[Any] =    AltDiffusionImgaImgPipeline.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    safety_checker=SCREAMING_SNAKE_CASE_,    )
										pipe.to(SCREAMING_SNAKE_CASE_      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
										pipe.enable_attention_slicing()

										snake_case       :     Tuple =    '''A fantasy landscape, trending on artstation'''

										snake_case       :     str =    torch.manual_seed(0      )
										snake_case       :     Dict =    pipe(
										    prompt=SCREAMING_SNAKE_CASE_,    image=SCREAMING_SNAKE_CASE_,    strength=0.75,    guidance_scale=7.5,    generator=SCREAMING_SNAKE_CASE_,    output_type='''np''',    )
										snake_case       :     List[Any] =    output.images[0]

										snake_case       :     Any =    image[2_55:2_58, 3_83:3_86, -1]

										assert image.shape == (5_04, 7_60, 3)
										snake_case       :     Dict =    np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]      )

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




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




				def 	__snake_case							(							self	:				int      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     str =    load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/img2img/sketch-mountains-input.jpg'''      )
										snake_case       :     Union[str, Any] =    init_image.resize((7_68, 5_12)      )
										snake_case       :     Dict =    load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''      )

										snake_case       :     int =    '''BAAI/AltDiffusion'''
										snake_case       :     Dict =    AltDiffusionImgaImgPipeline.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    safety_checker=SCREAMING_SNAKE_CASE_,    )
										pipe.to(SCREAMING_SNAKE_CASE_      )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
										pipe.enable_attention_slicing()

										snake_case       :     List[Any] =    '''A fantasy landscape, trending on artstation'''

										snake_case       :     Union[str, Any] =    torch.manual_seed(0      )
										snake_case       :     List[str] =    pipe(
										    prompt=SCREAMING_SNAKE_CASE_,    image=SCREAMING_SNAKE_CASE_,    strength=0.75,    guidance_scale=7.5,    generator=SCREAMING_SNAKE_CASE_,    output_type='''np''',    )
										snake_case       :     Tuple =    output.images[0]

										assert image.shape == (5_12, 7_68, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image      ).max() < 1e-2



'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase    					=   logging.get_logger(__name__)
logging.set_verbosity_info()

def      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''key_proj''', '''value_proj''', '''query_proj''']

						snake_case       :     str =    {
						    '''self_attn''': '''ngram_self_attn''',
						    '''cross_attn''': '''encoder_attn''',
						    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
						    '''feed_forward_layer_norm''': '''final_layer_norm''',
						    '''feed_forward''': '''''',
						    '''intermediate''': '''fc1''',
						    '''output''': '''fc2''',
						    '''key_proj''': '''k_proj''',
						    '''query_proj''': '''q_proj''',
						    '''value_proj''': '''v_proj''',
						    '''word_embeddings''': '''embed_tokens''',
						    '''embeddings_layer_norm''': '''emb_layer_norm''',
						    '''relative_pos_embeddings''': '''relative_linear''',
						    '''ngram_embeddings''': '''ngram_input_embed''',
						    '''position_embeddings''': '''embed_positions''',
						}

						for key in loading_info["missing_keys"]:
												snake_case       :     Optional[Any] =    key.split('''.'''       )

												if attributes[0] == "lm_head":
																		snake_case       :     Optional[int] =    prophet
																		snake_case       :     Union[str, Any] =    prophet_old
												else:
																		snake_case       :     Optional[int] =    prophet.prophetnet
																		snake_case       :     Any =    prophet_old.model

												snake_case       :     Optional[Any] =    False
												for attribute in attributes:
																		if attribute in mapping:
																								snake_case       :     List[str] =    mapping[attribute]
																								if not hasattr(A_    ,							A_       ) and len(A_       ) > 0:
																														snake_case       :     str =    attribute
																		elif hasattr(A_    ,							A_       ):
																								snake_case       :     Any =    attribute

																		if attribute == "weight":
																								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																								snake_case       :     Optional[Any] =    old_model.weight
																								logger.info(F"""{attribute} is initialized."""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								snake_case       :     List[str] =    old_model.bias
																								logger.info(F"""{attribute} is initialized"""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute in special_keys and hasattr(A_    ,							'''in_proj_weight'''       ):
																								snake_case       :     Union[str, Any] =    old_model.in_proj_weight.shape[0] // 3
																								snake_case       :     Any =    getattr(A_    ,							A_       )
																								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																								if attribute == "query_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																														snake_case       :     List[Any] =    nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																								elif attribute == "key_proj":
																														snake_case       :     str =    nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																														snake_case       :     Any =    nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																								elif attribute == "value_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																														snake_case       :     List[str] =    nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																								snake_case       :     Optional[Any] =    True
																								break
																		elif attribute == "position_embeddings":
																								assert (
																								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																								), "Hidden size has to match"
																								assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																								snake_case       :     List[Any] =    nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																								snake_case       :     Any =    True
																								break

																		if attribute.isdigit():
																								snake_case       :     Optional[Any] =    model[int(A_       )]
																								snake_case       :     List[str] =    old_model[int(A_       )]
																		else:
																								snake_case       :     Optional[Any] =    getattr(A_    ,							A_       )

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    getattr(A_    ,							A_       )

												if not is_key_init:
																		raise ValueError(F"""{key} was not correctly initialized!"""       )

						print(F"""Saving model to {pytorch_dump_folder_path}"""       )
						prophet.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

