import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224'''  ,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
    __UpperCamelCase					:Optional[Any]        =		MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Any        =		'''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        __UpperCamelCase					:List[Any]        =		847
        __UpperCamelCase					:str        =		'''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        __UpperCamelCase					:List[str]        =		150
        __UpperCamelCase					:int        =		'''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        __UpperCamelCase					:Any        =		171
        __UpperCamelCase					:int        =		'''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        __UpperCamelCase					:List[str]        =		133
        __UpperCamelCase					:List[str]        =		'''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        __UpperCamelCase					:Optional[Any]        =		19
        __UpperCamelCase					:Optional[int]        =		'''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        __UpperCamelCase					:Dict        =		65
        __UpperCamelCase					:Optional[Any]        =		'''mapillary-vistas-id2label.json'''

    __UpperCamelCase					:List[Any]        =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''     )  ,      '''r'''     )     )
    __UpperCamelCase					:List[Any]        =		{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}

    return config



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		[]
    # stem
    # fmt: off
    rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')     )
    rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')     )
    rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')     )
    rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')     )
    # stages
    for i in range(len(config.backbone_config.depths     )     ):
        for j in range(config.backbone_config.depths[i]     ):
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

        if i < 3:
            rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
        rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
        rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

    # FPN
    rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')     )
    rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')     )
    rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')     )
    for source_index, target_index in zip(range(3  ,      0  ,      -1     )  ,      range(0  ,      3     )     ):
        rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
        rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
        rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
        rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
        rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
        rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
    rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')     )
    rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')     )

    # Transformer decoder
    for idx in range(config.decoder_config.decoder_layers     ):
        # self-attention out projection
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
        # cross-attention out projection
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
        # MLP 1
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
        # MLP 2
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
        # layernorm 1 (self-attention layernorm)
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
        # layernorm 2 (cross-attention layernorm)
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
        # layernorm 3 (final layernorm)
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

    rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')     )
    rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')     )

    # heads on top
    rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')     )

    rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')     )
    rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')     )

    rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')     )
    rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')     )

    for i in range(3     ):
        rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")     )
    # fmt: on

    return rename_keys



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		dct.pop(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:str        =		val



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
    for i in range(len(backbone_config.depths     )     ):
        __UpperCamelCase					:Any        =		num_features[i]
        for j in range(backbone_config.depths[i]     ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            __UpperCamelCase					:str        =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
            __UpperCamelCase					:Any        =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
            # next, add query, keys and values (in that order) to the state dict
            __UpperCamelCase					:Union[str, Any]        =		in_proj_weight[:dim, :]
            __UpperCamelCase					:Optional[Any]        =		in_proj_bias[: dim]
            __UpperCamelCase					:Union[str, Any]        =		in_proj_weight[
                dim : dim * 2, :
            ]
            __UpperCamelCase					:Any        =		in_proj_bias[
                dim : dim * 2
            ]
            __UpperCamelCase					:Union[str, Any]        =		in_proj_weight[
                -dim :, :
            ]
            __UpperCamelCase					:Union[str, Any]        =		in_proj_bias[-dim :]
            # fmt: on



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		config.decoder_config.hidden_size
    for idx in range(config.decoder_config.decoder_layers     ):
        # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
        __UpperCamelCase					:List[str]        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
        __UpperCamelCase					:Optional[Any]        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase					:int        =		in_proj_weight[: hidden_size, :]
        __UpperCamelCase					:List[str]        =		in_proj_bias[:config.hidden_size]
        __UpperCamelCase					:Optional[Any]        =		in_proj_weight[hidden_size : hidden_size * 2, :]
        __UpperCamelCase					:Optional[Any]        =		in_proj_bias[hidden_size : hidden_size * 2]
        __UpperCamelCase					:Tuple        =		in_proj_weight[-hidden_size :, :]
        __UpperCamelCase					:List[Any]        =		in_proj_bias[-hidden_size :]
        # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
        __UpperCamelCase					:int        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
        __UpperCamelCase					:Any        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase					:List[str]        =		in_proj_weight[: hidden_size, :]
        __UpperCamelCase					:List[Any]        =		in_proj_bias[:config.hidden_size]
        __UpperCamelCase					:Optional[Any]        =		in_proj_weight[hidden_size : hidden_size * 2, :]
        __UpperCamelCase					:Tuple        =		in_proj_bias[hidden_size : hidden_size * 2]
        __UpperCamelCase					:str        =		in_proj_weight[-hidden_size :, :]
        __UpperCamelCase					:List[str]        =		in_proj_bias[-hidden_size :]
    # fmt: on



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase					:Any        =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,      stream=SCREAMING_SNAKE_CASE     ).raw     )
    return im



@torch.no_grad()
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = False     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		get_maskformer_config(SCREAMING_SNAKE_CASE     )

    # load original state_dict
    with open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ) as f:
        __UpperCamelCase					:Optional[int]        =		pickle.load(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[int]        =		data['''model''']

    # for name, param in state_dict.items():
    #     print(name, param.shape)

    # rename keys
    __UpperCamelCase					:Optional[int]        =		create_rename_keys(SCREAMING_SNAKE_CASE     )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    read_in_swin_q_k_v(SCREAMING_SNAKE_CASE  ,      config.backbone_config     )
    read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    # update to torch tensors
    for key, value in state_dict.items():
        __UpperCamelCase					:int        =		torch.from_numpy(SCREAMING_SNAKE_CASE     )

    # load 🤗 model
    __UpperCamelCase					:Union[str, Any]        =		MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE     )
    model.eval()

    for name, param in model.named_parameters():
        print(SCREAMING_SNAKE_CASE  ,      param.shape     )

    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(SCREAMING_SNAKE_CASE     ) == 0, f"""Unexpected keys: {unexpected_keys}"""

    # verify results
    __UpperCamelCase					:Tuple        =		prepare_img()
    if "vistas" in model_name:
        __UpperCamelCase					:Dict        =		65
    elif "cityscapes" in model_name:
        __UpperCamelCase					:Dict        =		65_535
    else:
        __UpperCamelCase					:Tuple        =		255
    __UpperCamelCase					:str        =		True if '''ade''' in model_name else False
    __UpperCamelCase					:Dict        =		MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE  ,      reduce_labels=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Any        =		image_processor(SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''     )

    __UpperCamelCase					:List[Any]        =		model(**SCREAMING_SNAKE_CASE     )

    print('''Logits:'''  ,      outputs.class_queries_logits[0, :3, :3]     )

    if model_name == "maskformer-swin-tiny-ade":
        __UpperCamelCase					:List[str]        =		torch.tensor(
            [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]     )
    assert torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,      SCREAMING_SNAKE_CASE  ,      atol=1e-4     )
    print('''Looks ok!'''     )

    if pytorch_dump_folder_path is not None:
        print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
        Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
        model.save_pretrained(SCREAMING_SNAKE_CASE     )
        image_processor.save_pretrained(SCREAMING_SNAKE_CASE     )

    if push_to_hub:
        print('''Pushing model and image processor to the hub...'''     )
        model.push_to_hub(f"""nielsr/{model_name}"""     )
        image_processor.push_to_hub(f"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''maskformer-swin-tiny-ade''',
        type=str,
        help=('''Name of the MaskFormer model you\'d like to convert''',),
    )
    parser.add_argument(
        '''--checkpoint_path''',
        default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
        type=str,
        help='''Path to the original state dict (.pth file).''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )
    parser.add_argument(
        '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
    )

    __lowercase										= parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )






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


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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[Any]        =		{
        '''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"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

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

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

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

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

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

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		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__":
    __lowercase										= 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.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





