






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							_lowerCAmelCase(			UpperCAmelCase_				,      UpperCAmelCase_				,      UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						Union[str, Any]										=StableDiffusionInstructPixaPixPipeline
       a							:						List[str]										=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
       a							:						Any										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       a							:						List[str]										=IMAGE_TO_IMAGE_IMAGE_PARAMS
       a							:						Union[str, Any]										=IMAGE_TO_IMAGE_IMAGE_PARAMS





       def      _a       (   self				):
         torch.manual_seed(0				)
         UpperCamelCase_:   Tuple        =							UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)    ,     layers_per_block=2    ,     sample_size=3_2    ,     in_channels=8    ,     out_channels=4    ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,     cross_attention_dim=3_2    ,     )
         UpperCamelCase_:   List[Any]        =							PNDMScheduler(skip_prk_steps=_lowerCamelCase				)
         torch.manual_seed(0				)
         UpperCamelCase_:   Optional[int]        =							AutoencoderKL(
             block_out_channels=[3_2, 6_4]    ,     in_channels=3    ,     out_channels=3    ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,     latent_channels=4    ,     )
         torch.manual_seed(0				)
         UpperCamelCase_:   Any        =							CLIPTextConfig(
             bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=3_2    ,     intermediate_size=3_7    ,     layer_norm_eps=1e-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=1_0_0_0    ,     )
         UpperCamelCase_:   int        =							CLIPTextModel(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase=0				):
         UpperCamelCase_:   str        =							floats_tensor((1, 3, 3_2, 3_2)    ,     rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							image.cpu().permute(0    ,     2    ,     3    ,     1				)[0]
         UpperCamelCase_:   str        =							Image.fromarray(np.uinta(_lowerCamelCase				)				).convert('RGB'				)
         if str(_lowerCamelCase				).startswith('mps'				):
           UpperCamelCase_:   List[Any]        =							torch.manual_seed(_lowerCamelCase				)
         else:
           UpperCamelCase_:   Tuple        =							torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'image_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							'cpu'  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase_:   str        =							self.get_dummy_components()
         UpperCamelCase_:   Dict        =							StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase				)
         UpperCamelCase_:   int        =							sd_pipe.to(_lowerCamelCase				)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   int        =							self.get_dummy_inputs(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							sd_pipe(**_lowerCamelCase				).images
         UpperCamelCase_:   Dict        =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 3_2, 3_2, 3)
         UpperCamelCase_:   Optional[int]        =							np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3





       def      _a       (   self				):
         UpperCamelCase_:   str        =							'cpu'  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase_:   int        =							self.get_dummy_components()
         UpperCamelCase_:   Dict        =							StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase				)
         UpperCamelCase_:   List[Any]        =							sd_pipe.to(_lowerCamelCase				)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   Any        =							self.get_dummy_inputs(_lowerCamelCase				)
         UpperCamelCase_:   Dict        =							'french fries'
         UpperCamelCase_:   Optional[int]        =							sd_pipe(**_lowerCamelCase    ,     negative_prompt=_lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							output.images
         UpperCamelCase_:   List[str]        =							image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         UpperCamelCase_:   Dict        =							np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3





       def      _a       (   self				):
         UpperCamelCase_:   int        =							'cpu'  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase_:   Tuple        =							self.get_dummy_components()
         UpperCamelCase_:   Any        =							StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase				)
         UpperCamelCase_:   List[Any]        =							sd_pipe.to(_lowerCamelCase				)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							self.get_dummy_inputs(_lowerCamelCase				)
         UpperCamelCase_:   List[Any]        =							[inputs['prompt']] * 2

         UpperCamelCase_:   Optional[Any]        =							np.array(inputs['image']				).astype(np.floataa				) / 2_5_5.0
         UpperCamelCase_:   Any        =							torch.from_numpy(_lowerCamelCase				).unsqueeze(0				).to(_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							image / 2 + 0.5
         UpperCamelCase_:   int        =							image.permute(0    ,     3    ,     1    ,     2				)
         UpperCamelCase_:   int        =							image.repeat(2    ,     1    ,     1    ,     1				)

         UpperCamelCase_:   List[Any]        =							sd_pipe(**_lowerCamelCase				).images
         UpperCamelCase_:   List[str]        =							image[-1, -3:, -3:, -1]

         assert image.shape == (2, 3_2, 3_2, 3)
         UpperCamelCase_:   Optional[int]        =							np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3





       def      _a       (   self				):
         UpperCamelCase_:   int        =							'cpu'  # ensure determinism for the device-dependent torch.Generator
         UpperCamelCase_:   Any        =							self.get_dummy_components()
         UpperCamelCase_:   str        =							EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5    ,     beta_end=0.0_1_2    ,     beta_schedule='scaled_linear'				)
         UpperCamelCase_:   Dict        =							StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase				)
         UpperCamelCase_:   str        =							sd_pipe.to(_lowerCamelCase				)
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   Union[str, Any]        =							self.get_dummy_inputs(_lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							sd_pipe(**_lowerCamelCase				).images
         UpperCamelCase_:   Optional[Any]        =							image[0, -3:, -3:, -1]

         UpperCamelCase_:   Tuple        =							[round(_lowerCamelCase    ,     4				) for x in image_slice.flatten().tolist()]
         print(','.join([str(_lowerCamelCase				) for x in slice]				)				)

         assert image.shape == (1, 3_2, 3_2, 3)
         UpperCamelCase_:   Tuple        =							np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3





       def      _a       (   self				):
         super().test_inference_batch_single_identical(expected_max_diff=3e-3				)





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.get_dummy_components()
         UpperCamelCase_:   Optional[Any]        =							StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase				)
         UpperCamelCase_:   str        =							VaeImageProcessor(do_resize=_lowerCamelCase    ,     do_normalize=_lowerCamelCase				)
         UpperCamelCase_:   Optional[Any]        =							pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)

         UpperCamelCase_:   Dict        =							pipe(**self.get_dummy_inputs_by_type(_lowerCamelCase    ,     input_image_type='pt'				)				)[0]

         UpperCamelCase_:   Union[str, Any]        =							components['vae']
         UpperCamelCase_:   Union[str, Any]        =							self.get_dummy_inputs_by_type(_lowerCamelCase    ,     input_image_type='pt'				)

         for image_param in self.image_latents_params:
           if image_param in inputs.keys():
             UpperCamelCase_:   str        =							vae.encode(inputs[image_param]				).latent_dist.mode()

         UpperCamelCase_:   Dict        =							pipe(**_lowerCamelCase				)[0]

         UpperCamelCase_:   str        =							np.abs(out - out_latents_inputs				).max()
         self.assertLess(_lowerCamelCase    ,     1e-4    ,     'passing latents as image input generate different result from passing image'				)







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





       """simple docstring"""







       def      _a       (   self				):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





       def      _a       (   self    ,     _lowerCamelCase=0				):
         UpperCamelCase_:   int        =							torch.manual_seed(_lowerCamelCase				)
         UpperCamelCase_:   int        =							load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'				)
         UpperCamelCase_:   Union[str, Any]        =							{
             'prompt': 'turn him into a cyborg',
             'image': image,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'image_guidance_scale': 1.0,
             'output_type': 'numpy',
         }
         return inputs





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,     safety_checker=_lowerCamelCase				)
         pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   List[str]        =							self.get_inputs()
         UpperCamelCase_:   Optional[int]        =							pipe(**_lowerCamelCase				).images
         UpperCamelCase_:   Dict        =							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCamelCase_:   Optional[Any]        =							np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]				)

         assert np.abs(expected_slice - image_slice				).max() < 1e-3





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,     safety_checker=_lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
         pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   Union[str, Any]        =							self.get_inputs()
         UpperCamelCase_:   Optional[Any]        =							pipe(**_lowerCamelCase				).images
         UpperCamelCase_:   Optional[Any]        =							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCamelCase_:   Any        =							np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]				)

         assert np.abs(expected_slice - image_slice				).max() < 1e-3





       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,     safety_checker=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							DDIMScheduler.from_config(pipe.scheduler.config				)
         pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   int        =							self.get_inputs()
         UpperCamelCase_:   Any        =							pipe(**_lowerCamelCase				).images
         UpperCamelCase_:   str        =							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCamelCase_:   str        =							np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]				)

         assert np.abs(expected_slice - image_slice				).max() < 1e-3





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							0

         def callback_fn(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				) -> None:
           UpperCamelCase_:   List[Any]        =							True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
             UpperCamelCase_:   List[str]        =							latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 6_4, 6_4)
             UpperCamelCase_:   Optional[Any]        =							latents[0, -3:, -3:, -1]
             UpperCamelCase_:   Tuple        =							np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]				)

             assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2
           elif step == 2:
             UpperCamelCase_:   str        =							latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 6_4, 6_4)
             UpperCamelCase_:   Optional[int]        =							latents[0, -3:, -3:, -1]
             UpperCamelCase_:   Any        =							np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]				)

             assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5e-2

         UpperCamelCase_:   Tuple        =							False

         UpperCamelCase_:   Optional[int]        =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,     safety_checker=_lowerCamelCase    ,     torch_dtype=torch.floataa				)
         UpperCamelCase_:   Tuple        =							pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   Optional[Any]        =							self.get_inputs()
         pipe(**_lowerCamelCase    ,     callback=_lowerCamelCase    ,     callback_steps=1				)
         assert callback_fn.has_been_called
         assert number_of_steps == 3





       def      _a       (   self				):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCamelCase_:   List[Any]        =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,     safety_checker=_lowerCamelCase    ,     torch_dtype=torch.floataa				)
         UpperCamelCase_:   Optional[int]        =							pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing(1				)
         pipe.enable_sequential_cpu_offload()

         UpperCamelCase_:   Union[str, Any]        =							self.get_inputs()
         UpperCamelCase_:   Optional[Any]        =							pipe(**_lowerCamelCase				)

         UpperCamelCase_:   str        =							torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 1_0**9





       def      _a       (   self				):
         UpperCamelCase_:   Tuple        =							self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         UpperCamelCase_:   Dict        =							inputs['image'].resize((5_0_4, 5_0_4)				)

         UpperCamelCase_:   int        =							'timbrooks/instruct-pix2pix'
         UpperCamelCase_:   Optional[Any]        =							StableDiffusionInstructPixaPixPipeline.from_pretrained(
             _lowerCamelCase    ,     safety_checker=_lowerCamelCase    ,     )
         pipe.to(_lowerCamelCase				)
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)
         pipe.enable_attention_slicing()

         UpperCamelCase_:   Tuple        =							pipe(**_lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							output.images[0]

         UpperCamelCase_:   List[Any]        =							image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

         assert image.shape == (5_0_4, 5_0_4, 3)
         UpperCamelCase_:   Union[str, Any]        =							np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]				)

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






"""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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''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"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

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

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

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

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =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."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

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

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														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(SCREAMING_SNAKE_CASE )


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)




